AI and I • 1h 7m

Overview

Walleye CEO Will discusses why he is pushing the hedge fund to become AI-first, starting with mandatory training and daily use of tools such as ChatGPT. His argument is practical: firms that treat AI as optional will lose speed, analytical capacity, and eventually competitiveness.

The conversation also covers AI-assisted research, internal data systems, leadership, decision-making, and how workers can use automation to move toward higher-level work rather than simply produce more output.

Key Takeaways

• Will sees AI adoption as a leadership responsibility, not an IT project. He argues that a CEO has to set the expectation publicly, use the tools personally, and make managers accountable for adoption across their teams.

• He rejects the idea that using ChatGPT is "cheating." That standard belongs to academic work, he says, where the point is to test an individual's unaided performance. In business, the goal is better results. AI should reduce time spent on drafting, formatting, and synthesizing information so people can focus on judgment and more difficult problems.

• Walleye began its internal AI effort in 2023 after an investment analyst demonstrated how early GPT tools could automate meaningful portions of analyst work. The firm now uses AI in quantitative trading, fundamental research, coding, writing, internal communication, and knowledge-sharing.

• The company measures progress partly through adoption. Will says roughly three quarters of the firm use ChatGPT regularly, while about a third use AI coding tools. More telling than a single productivity metric is whether employees independently find use cases and suggest tools worth rolling out.

• AI tools are useful before they are perfect. Will wants employees to tolerate flawed demos, imperfect outputs, and early-stage products rather than use those flaws as a reason to wait. The relevant question is whether the direction of improvement is clear.

• Walleye's internal research product, Current, collects analyst notes, earnings transcripts, broker material, and other company-specific information. The aim is not merely summarization; it is faster synthesis and analysis during periods when information arrives too quickly for a person to process alone.

• A broader priority is the firm's data strategy. Will describes a future in which recorded calls, meetings, documents, messages, and numerical data can be searched and analyzed together. He calls this idea "the Borg": a collective internal memory that helps teams revisit decisions, identify context, and spot patterns.

• Human judgment still matters. AI can generate language and surface connections, but employees must own the underlying ideas, check the output, and explain the reasoning. Will compares the technology to a jet engine: powerful, but still dependent on a well-designed plane and a human operator.

Practical Steps

• Make AI use explicit. Send a company-wide message stating which tools are approved, what work they should support, and that managers are responsible for building fluency on their teams.

• Start with routine work: draft emails from bullet points, turn meeting notes into follow-ups, summarize long documents, analyze spreadsheets, and use coding assistants for internal scripts or prototypes.

• Create a regular forum for sharing prompts and use cases. Walleye runs informal weekly AI meetups and rewards employees whose suggestions become firm-wide tools.

• Track adoption, but do not confuse tool usage with quality. Review whether staff can explain the conclusions and recommendations in AI-assisted work.

• Build a searchable archive of valuable internal information, subject to security and compliance rules. Record meetings where appropriate, retain transcripts, and connect them to relevant documents and data.

• Keep a short daily journal. Capture bullet points on work, family, health, or decisions, then use an AI tool to organize the entry in your voice. Over time, this creates a searchable record of what you were thinking when decisions were made.

Notable Quotes

• Will: "Not using these tools is like refusing to use the internet in 1995 because it wasn't perfect."

• Will: "You should be trying to be as efficient as possible, not so that you can just leave work at 2pm. So you can actually spend time thinking about next level tasks."

• Will: "If we ultimately get disrupted by AI, that's on me. And so let's get after that."

Worklife with Molly Graham • 35m

Overview

Molly Graham speaks with investor and author Bill Gurley about why peer relationships can shape careers as much as, and sometimes more than, formal mentorship. Gurley argues that a small group of generous, ambitious peers can speed up learning, offer honest support during hard moments, and create opportunities that no one member could find alone.

Their discussion centers on "co-climbing": sharing knowledge, connections, questions, and encouragement so the whole group moves forward together.

Key Takeaways

• Mentors matter, but peer groups receive far less attention. A mentor may provide advice from a distance, while peers can offer regular exchange, mutual vulnerability, and a clearer view of whether a work problem is personal, company-specific, or common to a field.

• The best groups are built around reciprocity. Members should bring useful material to one another: a book, a question, a job lead, an introduction, an experiment, or insight from their own work. Gurley says the pattern becomes clear over time: generous people tend to share back, while people who only take are poor fits.

• Sharing can create a learning advantage rather than weaken one. Gurley points to MrBeast and three early YouTube peers who reportedly traded ideas and experiments as the platform emerged. Each person studied independently but contributed discoveries to the group, multiplying the collective learning rate.

• Peer groups increase "optionality," or the range of possible opportunities. When several people know what you care about and whom you should meet, chance encounters are more likely to turn into useful introductions, career moves, or new ideas.

• Healthy groups need diversity of context. Gurley recommends building groups outside your own company, where members are less likely to reinforce the same assumptions or internal politics. Later in a career, relationships with people in more distant fields can produce ideas that do not emerge within an industry.

• Trust depends on discretion and openness. A group fails when members repeat confidential conversations, compete for every advantage, or maintain a polished "sales mode" persona. People need to be able to admit confusion, mistakes, and uncertainty without worrying that the information will be used against them.

• Gurley connects peer support to organizational design. At Benchmark, equal economics among partners reduced internal competition and made it easier to ask for help. The structure encouraged partners to want each other to succeed because each person's success benefited everyone.

Practical Steps

• Identify three or four people at a similar career stage who are trying to solve related problems. Look beyond your employer to widen the range of experience and ideas.

• Start with a specific reason to connect. Ask someone for their view on one concrete problem, share an article or idea relevant to their work, or offer an introduction. Avoid vague outreach asking someone to "be a mentor."

• Set a recurring meeting, even if it is only monthly. Use the time to discuss current problems, decisions, experiments, and opportunities rather than giving generic updates.

• Make contribution the entry price. Bring something useful to each conversation, and notice whether others do the same. If someone consistently takes ideas, contacts, or information without contributing, reconsider their place in the group.

• Practice saying, "I don't know." Ask where to learn more rather than pretending to understand. Gurley argues that this builds knowledge faster and makes a peer group more useful.

• Actively celebrate peers' wins. Send a note when someone gets promoted, lands a client, publishes work, or makes a strong move. Treat their progress as evidence of what the group can achieve, not as a threat.

Notable Quotes

• Bill Gurley: "Instead of 10,000 hours, we got 40,000 hours because everyone shared to the middle."

• Bill Gurley: "The best way to get smart is to admit when you don't know stuff."

• Bill Gurley: "Once you get this group going, root for them like you would the team that you follow the most."

Supra Insider • 1h 18m

Overview

This episode examines Anthropic's unusual "culture interview," a required behavioral interview that appears to apply to candidates across functions and seniority levels. The hosts argue that the interview is less about job-specific skill and more about whether candidates can think clearly about AI's benefits, risks, tradeoffs, and Anthropic's safety-first mission.

They also discuss what this hiring model may do inside the company: reinforce shared values, train employees to recognize culture fit, and give people across the organization a role in protecting the company's standards.

Key Takeaways

• Anthropic's culture interview appears to be a company-wide gate. Candidates may meet interviewers from marketing, IT, engineering, security, or other functions, rather than only people from their target discipline. The premise is that culture fit should be recognizable across the company, while role-specific interviews assess functional ability separately.

• The format seems different from a standard behavioral interview. Rather than spending 15 to 20 minutes probing one polished STAR story, candidates may receive 10 to 15 questions in a 45-minute slot. That favors concise, direct answers and the ability to explain difficult situations without excessive setup or jargon.

• The hosts believe interviewers are assessing clear communication, intellectual honesty, second-order thinking, resilience, and authenticity. A strong answer does not need to match Anthropic leadership's views exactly. It should show that the candidate has wrestled seriously with competing considerations, which Anthropic describes as "light and shade."

• "Why Anthropic?" carries more weight here than at many companies. Candidates should be ready to explain why Anthropic specifically, including how its safety orientation differs from other frontier AI labs. Generic enthusiasm for AI, growth, prestige, or product quality will likely sound shallow.

• The conversation frames Anthropic's hiring approach as an extension of its founding story. The company was started by former OpenAI safety leaders, according to the hosts, and its values place AI safety alongside the potential upside of advanced models. This gives the culture interview a sharper purpose than a generic values screen.

• Training employees to run culture interviews may reinforce the culture internally. Interviewers learn a common rubric for evaluating judgment and values, then return to their teams with a clearer sense of what the organization expects.

Practical Steps

• Read Anthropic's published values and Dario Amodei's essays, including "The Adolescence of Technology" and "Machines of Loving Grace." Write down where you agree, disagree, and remain uncertain. Do not memorize company language.

• Prepare a concise answer to "Why Anthropic rather than OpenAI, Google, or another AI lab?" Tie it to a personal conviction, experience, or concern. Be specific about Anthropic's approach to safety, responsible deployment, and the societal effects of AI.

• Build examples for questions about judgment under pressure. Prepare one story where you escalated a serious risk despite pressure to move ahead, and another where something went wrong and you can explain your role honestly.

• Practice answering behavioral questions in two to three minutes. Lead with the decision or point of view, give only the necessary context, explain your reasoning, and state what you learned.

• Avoid over-rehearsing STAR scripts. Have a story bank, but answer the question asked. If an interviewer moves quickly to the next question, do not treat that as negative feedback.

• Explain technical work in language that someone outside your function can follow. Your interviewer may not be a PM, engineer, or subject-matter expert.

Notable Quotes

• "You don't know who you're talking to. You go into the culture interview." - Ben

• "They're not looking to hire a bunch of people who think exactly who agree exactly with what Dario thinks." - Ben

• "You just have to convince them that you belong." - Ben

Decoder with Nilay Patel • 1h 17m

The Story

Nilay Patel talks with GoFundMe CEO Tim Cadogan about a company that has become part of the public machinery for dealing with private crises. Cadogan started in March 2020, just as COVID-19 shut down businesses, displaced workers, and pushed millions of people toward emergency fundraising. GoFundMe had to adapt quickly, including handling a flood of campaigns for restaurants and small businesses whose owners could no longer access the documents needed to verify accounts.

Patel calls GoFundMe "load-bearing" infrastructure in American life, especially after the pandemic. Cadogan accepts the description, while stressing that the same pattern appears across the 20 countries where the company operates. In his view, the platform works because people want to help each other, even if asking publicly for help remains uncomfortable almost everywhere.

The conversation keeps returning to the tension at the center of GoFundMe: it presents itself as a vehicle for care and community, but it is also a for-profit company. Cadogan says donors pay payment-processing fees, while tips to GoFundMe itself are voluntary. At scale, those optional tips become predictable enough to support the company's roughly 850 employees. He argues that operating as a company gives GoFundMe the money and technical capacity to improve the service, while its business model stays aligned with getting more donations to recipients.

Main Themes

Healthcare is the sharpest example of the contradiction GoFundMe inhabits. Medical fundraising brings in the most money on the platform, not only in the United States but in every market where it operates. Cadogan says medical crises generate fundraising because no healthcare system covers every surrounding cost: travel, lost income, childcare, caregiving, and administrative bills. More than that, he sees donations as an expression of love that institutions cannot replicate.

Patel pushes back on the emotional framing. He points to research connecting medical crowdfunding with holes in insurance coverage, including evidence that Medicaid expansion reduces health-related GoFundMe campaigns. His question is blunt: if a better social safety net reduces demand for GoFundMe, would the company oppose it? Cadogan says no. He says GoFundMe does not lobby against broader access to care and has tried to direct people toward government programs when they are available.

Cadogan describes the company's decision-making rule as simple: will a choice generate more help? But Patel tests the limits of that principle. GoFundMe could add darker prompts to extract more tips, turn fundraising into a discovery-driven attention market, or resist policy changes that reduce its largest category. Cadogan says the company deliberately avoids those paths and treats profit as a result of fulfilling its purpose, rather than the purpose itself.

AI brings a newer version of the same problem. GoFundMe's Smart Fundraising Coach helps people write campaign pages, set goals, and share requests directly with friends. Cadogan says it is meant to help people communicate their own stories, especially during stressful moments. Patel worries that AI could also make fundraising easier to manipulate at scale. Cadogan argues that campaigns still depend on genuine relationships and early support from people who know the organizer.

The episode ends with an unresolved question: can a platform built around emotionally compelling appeals remain trustworthy as AI makes synthetic emotion cheaper to produce? Cadogan believes human connection remains hard to fake. Patel is less certain.

Lenny's Podcast: Product | Career | Growth • 1h 24m

Overview

Jen Abel walks through enterprise sales as a 15-step process rather than the usual CRM shorthand of intro, demo, proposal, and close. Using a hypothetical $100,000 sale to SpaceX's legal team, she explains how to win by gathering better information than competitors, involving the right internal people early, and managing the buyer's process rather than forcing them through a generic sales funnel.

Her central idea is that enterprise buyers want a solution that helps them achieve a larger organizational goal, not another tool that promises to save a few hours.

Key Takeaways

• Start at the top or one level below it. For a major account, target the executive budget owner and their likely deputy, rather than reaching broadly into the organization. Abel calls this a "pincer" approach: a founder reaches the executive while an account executive reaches the N-1.

• The initial call is for discovery, not pitching. Keep it informal, skip slides and demos, and let the buyer speak first. Abel argues that buyers tend to be most candid before the interaction feels like a formal sales process. She also recommends not recording this call.

• Sell "alpha," not generic efficiency. A pitch such as "we reduce costs" is easy to compare against alternatives. The stronger pitch explains what the executive can newly accomplish: restructure a team, reduce risk, bring work in-house, or show measurable progress to the CEO or board.

• Never run an unprepared group demo. Before the demo, work with an internal champion to identify attendees, their priorities, objections, and the specific product areas that matter. The demo should cover the narrow slice of the product tied to their goals, rather than a full product tour.

• Build the deal with the customer. Abel repeatedly recommends co-authoring the demo, pilot, success metrics, ROI case, timeline, and procurement plan with the internal champion. This turns the buyer into an active participant and gives the seller better visibility into potential blockers.

• Keep pilots short when possible. If users can experience value without a complex integration, Abel prefers a tightly managed two- to three-day pilot with three or four users and defined tasks. If the product requires a month or more of implementation, charge for the pilot and credit the fee toward a contract.

• A healthy enterprise win rate is lower than many teams expect. Abel says qualified enterprise opportunities often close at roughly 25% to 35%. If the rate is much higher, she suggests the company may be priced too low or qualifying too narrowly.

Practical Steps

• Write a two- or three-sentence outreach message focused on the executive outcome your product enables. Test versions over time; do not rely on a generic value proposition.

• On the first call, ask what must change next year, why it matters now, and how success would be measured. Avoid scripted qualification questions about budget, authority, need, and timing.

• Before scheduling a demo, hold a short planning call with your champion. Ask who should attend, what each person cares about, what to avoid, and what a useful demo format looks like.

• After the demo, immediately debrief with the champion. Ask who seemed unconvinced, where the team needs more detail, and whether another stakeholder needs a focused conversation.

• Before starting a pilot, work backward from the desired signature date. Identify procurement, security, legal, the signing authority, and the contract process before momentum fades.

• Send contracts in editable Word format, not PDF. If redlines are extensive, ask legal or procurement to resolve them live rather than trading documents for weeks.

Notable Quotes

• Jen Abel: "The whole game is to slow down to go fast."

• Jen Abel: "Enterprise deals are won on, this feels so close to who we are. It feels like it was built specifically for me."

• Jen Abel: "Enterprise sales is mirroring their buying process. It's trying to control their buying process. It's not plopping these people into your sales process."

Platformer • 1h 3m

Overview

This episode examines how AI is changing entry-level work and how one company is adapting its own structure around the technology. Casey Newton speaks with Dan Shipper, co-founder and CEO of Every, a subscription business that combines AI journalism, software products, and hands-on experimentation with new models.

Shipper argues that AI has made it possible for a small company to run a daily publication while building several software products. But he also sees automation creating demand for more expert human judgment, especially when AI output is close to useful but still needs someone to make it accurate, specific, and worth using.

Key Takeaways

• Stanford researchers tracking millions of payroll records found that young workers in AI-exposed jobs were 19% less employed than expected relative to workers in less-exposed jobs, according to Ella Marquianes. The gap was 15% a year earlier. The researchers' evidence suggests the trend is not easily explained by interest rates or a post-COVID hiring correction.

• The jobs holding up better appear to rely on tacit knowledge: teamwork, judgment, workplace context, and other skills learned through experience rather than formal instruction. That creates a hard problem for recent graduates, who need work experience to gain the very skills employers increasingly demand.

• Shipper treats AI agents as employees: the challenge is not whether an agent can take on work, but whether a person can delegate effectively. He compares it to the choice new managers face between micromanaging for a precise result and delegating to gain capacity.

• Every's business model is built around a feedback loop. The company tests unusual AI workflows internally, writes about what it learns, sends promising ideas to its audience, and turns successful experiments into products. Its journalism also gives it a role the labs cannot easily fill: independently judging which models work best for real tasks.

• AI has changed Every's staffing model without reducing its need for people. Shipper says a single engineer can now bring a real product to market, allowing the company to run more products with a relatively small team. Yet this expansion has required more engineers and experts, because AI-generated work often needs someone skilled to turn a plausible first draft into a dependable result.

• Every spent years trying to automate copy editing and only recently got useful results. The team built an internal agent using roughly 30,000 past edits from editor-in-chief Kate Lee, then improved it by comparing its suggestions with her later corrections. For Shipper, the point is not replacing an editor but spreading an expert's standards across more work.

Practical Steps

• If you are early in your career, build evidence that you can use AI in actual work. Recruiters may value AI skills, but broad familiarity with chatbots is less persuasive than a portfolio showing a workflow you improved, a tool you built, or a process you documented.

• Seek settings where you can observe experienced people at work. Newsrooms, internships, apprenticeships, volunteer projects, and cross-functional teams can teach the tacit skills that are hard to acquire alone.

• Treat AI as a delegated collaborator. Give it a defined assignment, review the result, identify failure patterns, and revise the instructions. Avoid handing over work you cannot evaluate.

• Capture examples of expert feedback in your own work. If a manager or editor repeatedly improves your drafts, save those edits and look for patterns. Over time, those examples can become checklists, prompts, or internal tools.

• For writing, use AI to research, test phrasing, surface counterarguments, or organize material, while keeping ownership of the argument and final judgment. Shipper's standard is whether the work still reflects what the author thinks.

Notable Quotes

• "Do I micromanage and get the result I want, or do I delegate and get leverage?" - Dan Shipper

• "The way that AI works is it is trained on the residue of human expertise." - Dan Shipper

• "Writing... is about thinking. And I often don't know what I think until I write something." - Dan Shipper

One Knight in Product • 48m

Overview

Jamie Mercer, CPO at TrustedHousesitters, discusses building and scaling a two-sided marketplace for pet owners and sitters. The conversation covers marketplace trust, AI-assisted product work, commercially minded product management, and the leadership shift required when moving into senior product roles.

TrustedHousesitters connects owners who need in-home pet care while travelling with sitters seeking accommodation and time with animals. Jamie describes it as a community built on mutual exchange rather than a standard transaction, with both sides paying for membership.

Key Takeaways

• Trust is the product's foundation. New sitters need a credible path into the marketplace even before they have reviews, while owners need confidence leaving homes and pets in someone else's care. Verification, background checks, referrals, ratings, profiles, and better matching all contribute to that trust.

• Marketplace growth should respond to the side that needs attention. Jamie says the company tracks liquidity, supply, demand, and fill rates, then focuses acquisition efforts where they will improve marketplace health. At present, owner acquisition is a priority because attractive sits in desirable locations also draw sitters.

• Matching needs to account for experience and pet needs, not simply availability. A first-time owner may be better paired with an experienced sitter. Pets with medical or specialist care requirements should be matched with people who have relevant experience. Better personalization can raise the odds of a successful sit for both sides.

• AI should follow product and company strategy, rather than become a strategy of its own. Jamie's team uses AI to speed up research synthesis, competitor analysis, data work, experimentation, support, and internal communication. The aim is faster learning and more productive teams, not AI features for their own sake.

• Strong product managers need commercial judgment, but product should not become captive to short-term revenue metrics. Jamie looks for PMs who can connect customer problems to business outcomes, while maintaining a balanced portfolio that includes medium-term and higher-risk bets.

• Senior product leadership is largely an alignment job. As a CPO, Jamie spends less time in delivery and more time working with the executive team, board, marketing, finance, and other stakeholders. The difficult work is building shared understanding, resolving disagreements, and giving teams room to operate.

• Empowered teams still need clear boundaries. Jamie supports giving squads ownership of problem spaces, but warns that too much autonomy without enough direction can set teams up to fail. Product leaders must define the right scope, keep teams connected to the wider business, and help them work across functions.

Practical Steps

• Build trust signals into every stage of a marketplace. Introduce checks, references, clear profiles, reviews, and onboarding guidance. Give new participants a way to earn credibility through lower-risk early experiences.

• Treat matching as a product capability. Capture relevant information about needs, experience, preferences, and constraints. Use it to avoid poor-fit matches, such as pairing two inexperienced participants in a high-stakes situation.

• Before adopting AI tools, name the business or customer problem they should improve. Start with repeatable work such as research summaries, support triage, analytics, documentation, and copy review. Measure whether cycle time or quality improves.

• Run regular squad health checks. Ask teams whether strategy is clear, what is blocking them, how morale is holding up, and whether delivery pressure is sustainable. Select a small number of issues to address each quarter.

• For product leaders, invest in regular one-to-ones with peers outside product, especially finance and marketing. Use those conversations to surface tensions early rather than waiting for disagreements to harden.

• Give teams outcomes and boundaries, not detailed feature instructions. Make sure they understand the commercial context, customer problem, and decision-making limits before asking them to act autonomously.

Notable Quotes

• Jamie Mercer: "The best feeling in the world is alignment."

• Jamie Mercer: "The product strategy and the company strategy are so intertwined, and we really understand our growth in a way that by solving customer problems, we will drive commercial outcomes."

• Jamie Mercer: "I'm never going to tell anybody what to build or how to build it, because they're closer to their domain. They're closer to the customer."

One Knight in Product • 48m

Overview

Katja Danaki, Chief Product Officer at Holland & Barrett, discusses how a 150-year-old health and wellness retailer is changing its operating model, technology, and customer experience. The conversation focuses on digital transformation in retail: building the less visible systems that keep the business running, while improving the app, online experience, and connection between stores and digital channels.

She also explains why product leadership in a transformation setting requires a different profile from product management at an already mature tech company.

Key Takeaways

• Holland & Barrett is balancing global scale with local adaptation. Core capabilities such as search, data, pricing, promotions, and digital platforms can be built once across markets. Customer communications, health personas, regulations, and market preferences still need local adjustment.

• The company credits its growth to long-term transformation work rather than a single customer-facing feature. Katja describes replacing paper-led warehouse operations and manual supplier, pricing, and product-data processes with scalable digital systems. These foundations affect stock availability, commercial offers, supplier terms, and the ability to respond quickly.

• Internal product work has direct customer value. Katja pushes back on the idea that supply chain, forecasting, master data, and promotion systems are unglamorous product areas. Better internal tools can have a larger effect on customer outcomes than many front-end changes.

• Holland & Barrett brought its mobile app in-house because it viewed the app as a strategic capability. Katja says the previous vendor-owned app represented under 5% of digital-channel revenue and had an app-store rating in the low threes. The current app contributes more than 30% of digital revenue, according to her, with an ambition to reach half.

• Product teams are organised around two areas: "firm foundations" and omnichannel customer experience. Keeping digital and retail product leadership connected is intentional, because customers experience one brand rather than separate store, website, and app businesses.

• Product management should not become overly attached to process. Katja warns that some courses teach methodology in a way that leaves people "precious about methodology" instead of focused on solving the real problem.

• Digital transformation product managers need skills that may not be required in mature product companies. They must work through legacy systems, shifting organisational habits, unclear ownership, and stakeholders who may not yet see technology as a partner.

• Katja hires beyond conventional product backgrounds. People from stores, contact centres, and distribution centres can bring strong user empathy and operational knowledge. Training matters, but she puts equal weight on high agency, curiosity, and willingness to drive change.

Practical Steps

• Identify the "brilliant basics" your customers depend on: reliable stock, accurate product information, relevant promotions, easy transactions, and useful support. Measure them and fix weak points before funding more experimental features.

• Map which capabilities are strategically differentiating for your business. If a capability is central to customer loyalty or future growth, assess whether relying on a vendor limits your speed, data access, or ability to improve it.

• Build product teams around customer outcomes and business foundations, not only channels. Create shared ownership across store, app, web, operations, and data where the customer experience crosses those boundaries.

• When hiring or developing product managers, look for evidence that they act without waiting for ideal conditions. Pair domain experts from operations or customer service with structured product education and close coaching.

• Treat product methods as tools, not rules. Ask whether a workshop, framework, or discovery process is helping the team make a better decision or merely delaying action.

Notable Quotes

• "I've seen people coming from the course being so precious about methodology that it actually prevents them from doing the thing." - Katja Danaki

• "You cannot just take a product person from the product-established company, and you cannot expect them to be successful in the digital transformation context." - Katja Danaki

• "The reality is that people think about the brand. They don't think about physical or digital." - Katja Danaki

One Knight in Product • 1h 6m

Overview

Barry O'Reilly joins the show to discuss his book, "Artificial Organizations," and the ways AI is changing leadership, decision-making, and day-to-day work. His central argument is that AI should support human judgment rather than replace it: the real risk is not that machines take leaders' jobs, but that weak decision-making becomes easier to spot.

Drawing on his work with executives and his AI startup studio, Nobody Studios, Barry argues that the best use of AI is to reduce administrative drag, improve preparation, and create more time for hard problems and real human presence.

Key Takeaways

• AI adoption is less mature than social media makes it appear. Barry says frontier AI companies and influencers benefit from creating fear that everyone else is ahead, while many senior leaders are still early in their use of these tools. Companies are buying licenses faster than employees are finding useful ways to use them.

• Start with how you work, not with a tool list. Barry's framework is traits, tasks, then tools. He used transcription and editorial support to write around dyslexia, turning spoken conversations into draft material rather than forcing himself into a writing process that did not suit him.

• The goal is to shift time from administration to judgment. Barry estimates he has moved from an 80/20 split of admin versus creative problem-solving to roughly 40/60. The gain is not simply higher output. It is more room to think, test ideas, and make better calls.

• AI can improve meeting quality when it captures context and removes the need to hold every past action in your head. Meeting transcripts, summaries, agendas, follow-ups, and decision logs can help people arrive prepared and stay present in the conversation.

• Leaders should treat AI as a thinking partner. Rather than asking for answers, use it to identify assumptions, challenge a proposal, generate scenarios, and expose blind spots before a high-stakes meeting.

• AI slop is becoming an organizational tax. Barry warns that people can now generate long documents with little underlying thought, pushing the work of interpretation and fact-checking onto already overloaded managers. He recommends making this a cultural issue, not just a quality issue.

• Shared organizational context matters. Useful AI systems need access to current information about strategy, customers, decisions, work in progress, and internal relationships. The aim is not necessarily one perfect source of truth, but better connections between the information that already exists.

Practical Steps

• Pick one upcoming decision. Write down how you would normally make it: what evidence you need, whose views matter, which risks concern you, and what would change your mind.

• Put that decision process into an AI assistant before asking it to solve the problem. Ask: "What assumptions am I making?" "What evidence is missing?" and "What scenarios should I consider?"

• Use a meeting copilot for recurring meetings. Before each meeting, send a short agenda with the decisions required. Afterwards, share a summary of agreements, owners, and next steps.

• Set a standard for AI-generated work. Ask people to show their reasoning, sources, options considered, and recommendation. Do not accept long documents that merely shift processing work to someone else.

• Build context gradually. Start by organizing the documents, meeting notes, customer research, and strategy material most relevant to a team’s current decisions. Keep it current rather than trying to document everything at once.

Notable Quotes

• "AI isn't replacing leaders. It's exposing them." - Barry O'Reilly

• "You're not behind, but you need to start where you are. You cannot freeze. That's the worst thing to do." - Barry O'Reilly

• "The last thing that is uniquely human is our ability as product leaders, as any leader, to look at the information and make a decision." - Barry O'Reilly

Worklife with Molly Graham • 48m

Overview

Molly Graham argues that careers are less like a predictable staircase and more like a series of voluntary "cliff jumps": moves into unfamiliar work where confidence drops before capability grows. In her TED Talk and follow-up conversation with Elise Hu, she explains how to take worthwhile risks, manage the emotional strain of change, and question inherited definitions of success.

The episode also looks at reinvention in midlife, the role of mentors, and how people can keep growing even when the job market feels unstable.

Key Takeaways

• The "stairs" model of career success is misleading. A title, promotion, or linear path can provide security, but it can also trap people in work that no longer fits. Graham says bigger growth often comes from moves that appear lateral or backward at first.

• Fear needs sorting, not eliminating. Fear of failing, looking inexperienced, or asking basic questions may signal a meaningful growth opportunity. Fear tied to essentials, such as losing housing or being unable to support a family, deserves more weight. A career move is hard to sustain when financial anxiety consumes all available attention.

• The early phase of a new challenge is supposed to feel disorienting. Graham recalls receiving her lowest performance rating after moving from HR at Facebook into an unfamiliar project. She advises people to expect emotional swings rather than treat each bad day as evidence that they chose wrong.

• Being willing to look uninformed is a professional advantage. Basic questions often reveal unclear assumptions, wasted meetings, and weak decisions. The people who ask them can become the strongest learners in the room.

• Many career choices are driven by old scripts: family expectations, sibling comparisons, cultural rewards, or an "achiever" identity. Graham suggests treating these as voices rather than facts. Some may have helped in the past but may now be steering decisions toward burnout or status rather than meaningful work.

• In an uncertain economy, risk does not have to mean quitting. Taking on a hard project, giving direct feedback, leading a meeting, or learning a new skill inside a current role can build tolerance for discomfort and make a future transition less frightening.

Practical Steps

• Before a major move, name the fear precisely. Ask whether it is a practical risk, such as needing steady income, or a reputational fear, such as being judged or failing. Address the first with a plan; do not automatically obey the second.

• When a new role feels terrible, use Graham's "give it two weeks" rule. Track whether the concern persists across time rather than reacting to a single stressful meeting or day.

• Ask one question you are tempted to suppress. Try: "Sorry if this is a stupid question, but can you define that term?" or "Why are we holding this meeting?" Notice what the answer exposes.

• If you are considering time between jobs, calculate your own safety threshold. Work out the monthly amount that would let you take time off without constant financial stress. If full time off is not feasible, consider part-time consulting or other work that covers core expenses while leaving room to recover and explore.

• Identify the voice driving a career decision. Write down what your achiever, people-pleaser, creative self, or cautious self wants. Then ask which voice should have the steering wheel for this decision.

• Choose a next step instead of demanding a complete reinvention plan. Look for an experiment that teaches you something about the work, people, or pace you want next.

Notable Quotes

• "Excellent careers are not built by excellent stair climbers." - Molly Graham

• "You have to learn to expect the roller coaster and ignore it at the same time." - Molly Graham

• "It was, like, almost so excited about having had the job that I never stopped to ask, 'Are you excited to do the job?'" - Molly Graham

Supra Insider • 1h 14m

Overview

This episode looks at LinkedIn and job search strategy through a recruiter's eyes. Chris Lee, founder of Product Scout, explains why candidates should start with "candidate-market fit": deciding which roles they want before trying to write a profile that appeals to everyone.

The conversation also includes a live review of the hosts' LinkedIn profiles against a Head of Product opening at an AI-driven biotech company. The exercise shows how quickly recruiters form an opinion based on location, recency, domain experience, seniority, and the story a profile tells.

Key Takeaways

• A LinkedIn profile cannot be evaluated in the abstract. The right profile for a consumer growth PM role may be a poor fit for a first product hire at an AI biotech startup. Start by defining the target: company stage, role level, IC versus management, domain, location, and work arrangement.

• Broad positioning often weakens a candidate's case. Chris advises treating a job search like a product experiment: pick a focused target, position for it, test the response, then adjust if needed. Trying to appeal to startups, big tech, consumer, B2B, and several role types at once can make the narrative feel unfocused.

• Location is a hard filter for in-person roles. For a Bay Area or New York role that requires office time, Chris says recruiters usually search locally first. Listing a city before moving there is reasonable only if the move is already underway and the candidate can explain concrete plans.

• Recruiters make fast judgments. Chris says a candidate's introduction and LinkedIn headline often shape the rest of the conversation. A clear story about past experience, current strengths, and why the role fits reduces work for the recruiter and makes them more likely to advocate for the candidate.

• LinkedIn matters more than a resume for initial sourcing. Chris rarely starts with resumes. He uses LinkedIn profiles and keyword-based tools to identify candidates, then reviews profiles for domain fit, stage fit, seniority, product type, and evidence of impact.

• Keywords can get someone into the funnel, but they do not carry the process. For AI product roles, mention work with relevant tools or methods, such as evals, model testing, golden datasets, or AI product launches. A bare "AI PM" label may trigger interest, but recruiters will quickly look for proof.

• Recent, hands-on work matters for early-stage roles. A former product leader who has spent years coaching or advising may be less attractive for a first-PM role than someone currently shipping products. Short tenures, limited domain experience, and long gaps from full-time product work can compound.

Practical Steps

• Write down a target role definition before editing LinkedIn. Include stage, domain, location, level, product type, and whether you want IC, player-coach, or management work.

• Build a list of roughly five to ten companies that match that target. Avoid running too many unrelated interview processes at once; it becomes harder to explain what you want and why.

• Put the strongest evidence in each role's experience section. Add two or three readable bullets covering the product, your role, the work you led, and the business result.

• Use the About section to connect the dots. Explain the themes in your career, your strongest areas, and the type of work you want next. Do not repeat every job description.

• Include meaningful terms recruiters may search for, but place them in real sentences. For example, describe the AI systems, customer problems, technical partners, or outcomes involved.

• Review your profile as if you were a recruiter with 30 seconds. Ask: Is my current location clear? Is my last hands-on product role obvious? Can someone see why I fit the roles I want?

Notable Quotes

• Chris Lee: "Treat your job search like you're building a product where you are the product as well."

• Chris Lee: "If you're trying to craft your story or profile or narrative to have the widest appeal, it's really not going to resonate with anyone very, very strongly."

• Chris Lee: "The best candidates... have spent time up front to craft that narrative about themselves and what they're looking for."

The Pragmatic Engineer • 1h 25m

Overview

Charity Majors discusses why software engineering has split into two AI camps: enthusiasts who see rapid gains in speed and capability, and skeptics, often closer to production operations, who see rising incident counts, degraded quality, and more unreviewed "slop." Her argument is that both groups are reacting to real evidence, but they are rarely sharing the same feedback loop.

The conversation centers on a shift from trusting code because humans wrote and reviewed it to trusting systems because they have been tested, observed, and proven to behave correctly in production-like conditions.

Key Takeaways

• The question of whether engineers will ship AI-generated code they have not read is, in Majors' view, mostly a question of timing. Operations and QA teams have always had to validate software written by "unreliable agents," meaning developers. AI makes that relationship more obvious.

• Code review bundles together several jobs that should be separated: product and API design decisions, mentoring, communication, syntax checks, and bug detection. Human discussion remains valuable for deciding whether a change belongs in the product and whether its mental model makes sense. Reading every line is a poor primary mechanism for proving correctness at higher code volumes.

• AI enthusiasm and AI skepticism are both grounded in reality. Enthusiasts see competitors moving faster and believe companies must adapt. On-call engineers see failures, weak mental models, and support burdens caused by code or decisions that reached production without enough validation.

• Majors argues that reliability is getting worse at many companies because organizations are prioritizing output without building equivalent safeguards. She points to the danger of removing reliability, trust-and-safety, or operational capacity while increasing the volume of changes.

• Production is part of development, not the stage after development. Engineers need fast feedback from live systems, and they should understand how their code behaves for real users. The source repository contains intent, but it is not the complete source of truth.

• Observability becomes more valuable with nondeterministic systems and AI agents. Rich telemetry, tracing, and automated instrumentation help teams inspect actual behavior rather than relying on what code appears likely to do.

• For managers and directors, AI does not eliminate the need for management. Majors describes good middle management as sensemaking and context-giving: helping people understand what the business is trying to achieve, why it matters, and how teams should coordinate around it.

Practical Steps

• Set a communication rule: do not send colleagues AI-generated material you have not read. If it takes someone else longer to review than it took you to generate, revise it first.

• Move more validation into automated systems. Invest in CI, property-based tests, fault injection, end-to-end tests, runtime guardrails, and deterministic reproduction of failures. Treat these as the replacement for trust lost when humans stop reading every diff.

• Give engineers access to production telemetry and make it part of normal development. Add OpenTelemetry or comparable instrumentation early, then inspect traces and outcomes after deployment.

• Separate code-review expectations. Use human review for design, product intent, interface choices, and coaching. Let linters, tests, and automated checks handle routine correctness work where possible.

• If you are a manager or director, get hands-on with AI-assisted development. Majors advises leaders to understand the current workflow by submitting changes and seeing how code moves through review, deployment, and production.

• Build AI experience now, especially if your current role offers little exposure. Managers facing a narrower job market may benefit from spending time as an IC to regain technical fluency and demonstrate current skills.

Notable Quotes

• "The question is not if we will stop reading code written by AI but when." - Charity Majors

• "Production is not what happens after development. It is a stage of development." - Charity Majors

• "Anxiety and excitement are psychologically almost the same, but the difference between them is agency." - Charity Majors

Decoder with Nilay Patel • 1h 11m

The Story

Nilay Patel talks with Semafor co-founder and editor-in-chief Ben Smith about building a news company after the collapse of the traffic-driven digital media model Smith once helped define at BuzzFeed. Semafor began with global ambitions, a free website, newsletters, and a belief that elite readers still wanted serious reporting. Four years in, Smith says the company has found a different business model: roughly half its revenue comes from advertising and half from events, or "convening."

Smith describes Semafor as an "anti-scale" company, though not a small one. Rather than chase viral distribution, it tries to reach decision-makers across email, web, video, and live events. Its global reporting in Africa, the Gulf, and soon Europe and Asia is meant to build local audiences rather than feed a New York-centered operation. He says the company has learned from BuzzFeed's international expansion, where audiences grew faster than revenue.

The conversation keeps returning to a hard conflict: news companies need access to powerful people, while their value depends on questioning those same people. Semafor's new Silicon Valley and the World event includes tech leaders such as Jensen Huang and Satya Nadella in advisory roles, while Semafor plans to interview and cover them independently. Smith argues that tough questions improve conferences rather than threaten them. Patel is less certain, pointing out the pressure created when attendees buy tickets to see executives whom reporters may also need to investigate.

Main Themes

Smith's central argument is that journalism can survive if it serves an audience that values independent reporting enough to support a focused advertising and events business. He rejects rigid thinking about revenue, whether that means treating subscriptions as morally purer than advertising or assuming a free model is automatically more democratic. Semafor may add subscriptions later, but Smith sees them as one option among several rather than the company's identity.

They also examine how platforms have changed media incentives. Smith agrees with his former BuzzFeed colleague Jonah Peretti that Facebook made a strategic mistake by abandoning professional content, even if news itself remains a difficult category. He sees YouTube as more durable because it lets individual creators build businesses, but says its structure leaves little room for media companies to gain bargaining power. Patel presses on the result: creators increasingly make integrated advertising because platforms pay too little, blurring the line between editorial work and marketing.

Both men worry about a public climate in which audiences assume every institution is bought. Smith says transparency and independent reporting are the answer, though he warns against treating every financial connection as proof of corruption. He expects public resistance to undisclosed influencer marketing, especially political influence campaigns, to produce a regulatory response. Patel sees a younger audience that has grown up inside feeds packed with sponsored posts, clips, and synthetic material, making the old signals of editorial independence less obvious.

AI, Power, and What Comes Next

AI enters the conversation as both a reporting subject and a newsroom tool. Smith says Semafor is using AI for internal work and for synthesizing large amounts of material, such as transcripts from its events. But he draws a line around the parts of journalism that remain hardest to automate: finding new information and communicating it to people.

They end on AI politics, data centers, and the federal government's growing role in technology. Smith expects AI regulation and government ownership stakes in companies such as Intel to reshape the relationship between Washington and Silicon Valley. For Semafor, that creates more stories, more powerful people to question, and more pressure to prove that its access does not soften its reporting.

Decoder with Nilay Patel • 39m

The Story

Nilay Patel and Verge AI reporter Hayden Field look at Google DeepMind's latest leadership shakeup as a test of whether Google still intends to lead the race for frontier AI. The moves are striking: Jeff Dean, Google's longtime chief scientist and a founder of Google Brain, is leaving to start a new lab on Google Cloud. DeepMind co-founder Demis Hassabis is stepping back from day-to-day leadership to become chairman and focus on longer-range research. Koray Kavukcuoglu takes over operational leadership under Sundar Pichai.

The timing makes the changes hard to dismiss as routine succession planning. Google has immense advantages: search, Gmail, Android, cloud infrastructure, custom chips, vast consumer reach, and a profitable core business that can fund AI work. Yet the company is no longer widely seen as leading model development. Field says Google remains a frontier lab for now, but the gap could become harder to close if its strongest researchers leave and its releases keep trailing OpenAI and Anthropic.

A SemiAnalysis report went further, arguing that DeepMind has already ceased to be a frontier lab because Google is too slow, bureaucratic, and strategically cautious. Patel sees that criticism as painfully familiar to anyone who has followed Google. Field thinks declaring Google's odds of ever returning to the top as zero is premature. AI rankings shift quickly, and Google could still release a leading model. But she worries the company could become a consistent follower: capable of catching up, unable to set the pace.

The conversation returns repeatedly to the tension between research and products. Hassabis is associated with ambitious science, including protein folding, drug discovery, world models, and the company's talk of approaching the "foothills of the singularity." Google's immediate commercial needs are less grand: useful Gemini features in search and Gmail, low-cost consumer tools, cloud services, coding, and enterprise automation. The reorganization may be Google deciding that product speed matters more than the kind of long-term research DeepMind was built to pursue.

Main Themes

The first theme is that winning AI may mean different things to different companies. Google may not need to produce the best model in the world to remain enormously successful. It can put adequate models in products used by billions of people while selling cloud capacity and TPUs to companies such as Anthropic and OpenAI. Patel asks whether Google might eventually accept this role: close enough to the frontier for its products, while profiting from those spending more aggressively to reach AGI.

But that strategy risks changing what makes DeepMind valuable. Field points to the people problem. Dean and Hassabis were technical leaders, but they also represented a set of values around military use, surveillance, and the social consequences of AI. Google staff have raised concerns that the company is becoming more willing to work with the US government and military. If those employees no longer believe Google shares their limits, departures could accelerate.

The episode treats talent loss as the clearest near-term signal. A weak Gemini 4 release would matter, especially if it merely matches features competitors shipped months earlier. A larger exodus from DeepMind would matter more. Field also sees Hassabis's eventual departure as a major marker, since his continued presence still gives London-based DeepMind some continuity.

Google is unlikely to disappear from AI. Its resources and distribution make that implausible. The open question is whether it remains a place where the most ambitious researchers want to work, and whether its leaders can turn that talent into products before faster-moving rivals make the decision for them.

AI and I • 28m

Overview

Microsoft CTO Kevin Scott discusses why the conversation around AI has shifted from model scaling to the systems agents need in order to act usefully. He argues that models' reasoning abilities are moving ahead of many current products, creating a "capability overhang" that the industry now needs to close through better memory, tool access, identity, permissions, and open standards.

The episode also covers the future of software engineering, the tradeoffs between open and closed agent platforms, and why developers should stay curious as their tools change.

Key Takeaways

• Scott sees the agentic web as the next major technical problem. Useful agents need more than strong reasoning: they must retain context across tasks, access information, call tools, and make changes in external systems. That requires common protocols, much as the web depended on standards such as HTTP and HTML.

• He frames MCP as a simple starting point for connecting agents to tools and services. Microsoft wants internal systems to speak common agent protocols as well, partly to avoid "shipping your org chart" into every agent integration. Without shared interfaces, teams repeatedly build one-off connections that reflect company structure rather than user needs.

• Security remains unfinished, but Scott thinks open standards can support it. He points to agent identity, entitlement systems, permission requests, and administrator controls as core pieces. An agent should be able to explain which systems it needs to access for a task, request the necessary permissions, and operate within a user's established rights.

• Scott rejects the idea that openness and security are opposites. He argues that personal security agents could improve protection by monitoring signals across communication channels and checking suspicious activity through multiple sources before escalating to the user.

• On coding agents, he treats the debate as part of a long history of changing tools. The same argument appears in woodworking: hand tools versus power tools, then power tools versus CNC machines. People value different parts of the work, whether that is the process, the result, or both.

• He expects many software agents rather than a single dominant one. The biggest differences will come from companies that understand specific user problems and apply available infrastructure to solve them well, rather than from infrastructure alone.

• Scott's warning is directed at people waiting for AI systems to become cheaper or more capable before trying them. In his view, those improvements will continue, and delaying experimentation risks leaving teams behind as agents move from real-time prompting toward longer-running, asynchronous work.

Practical Steps

• Identify repetitive work that currently requires moving between several systems. Test whether an agent could gather information, draft an action plan, or complete low-risk portions of that work.

• When building agent integrations, define clear identities and permissions. Record who the agent represents, what resources it can access, which actions require approval, and what administrators can audit.

• Prefer common protocols over custom point-to-point integrations where possible. This reduces repeated engineering work and gives teams more flexibility to change agent providers later.

• Treat agent memory as a product requirement. Decide what context should persist across tasks, what must expire, and what users should be able to inspect or remove.

• For developers concerned about losing control of their craft, test coding agents on bounded tasks rather than making an all-or-nothing decision. Keep the tools and workflows that matter to you, while using automation where the outcome matters more than the method.

Notable Quotes

• Kevin Scott: "The important thing here is not the way that I'm doing this particular part of it; it's the outcome that I'm trying to get to."

• Kevin Scott: "Be curious. Try stuff. And if it works for you, use it, and if it doesn't, don't."

• Kevin Scott: "You're going to start to get to the point where you're able to go from this synchronous mode of interaction with agents to asynchronous."

Worklife with Molly Graham • 46m

Overview

Molly Graham talks with Hilary Gridley about whether AI can make managers better or simply help them make bigger mistakes faster. Gridley argues that AI does not change the core job of management: defining what good work looks like, giving people the context to make sound decisions, and helping a team accomplish more together than its members could alone.

Their conversation focuses on using AI as a coaching and teaching tool, while resisting the temptation to outsource the parts of work that build judgment, taste, and strategic thinking.

Key Takeaways

• AI raises the stakes for managerial clarity. As employees can produce more work faster, weak assumptions and unclear priorities spread faster too. Managers need to resolve conflicts over what matters most, which data is meaningful, and what tradeoffs the company will accept.

• A manager's job is to make implicit standards explicit. Gridley describes a strong manager as someone who can articulate "what good looks like" for a role, a piece of work, or a decision, then help people reach that bar. Building AI tools forces managers to put those standards into words.

• Good AI use begins with a real employee problem, not a manager's desire for more control. Gridley found success by building a tool that helped employees respond to sudden executive requests, rather than imposing a generic "strategy coach" on them. Internal AI tools need the same product-market fit as customer products.

• Do not automate work that develops a core capability. Gridley warns against outsourcing research synthesis, customer interviews, or other work that teaches people how to distinguish signal from noise. The final report matters, but the thinking required to produce it often matters more.

• AI can be useful as a guided coach for work people are still learning. Gridley built a custom GPT that walks product teams through testing a feature idea: identifying assumptions, isolating the riskiest one, and designing a small experiment. It helps employees arrive with evidence rather than unsupported opinions.

• "Slop" comes from shallow inputs, not from AI alone. A vague prompt such as "write a Q3 strategy" produces generic output because it contains no meaningful thinking to amplify. Employees still own the work they submit, even if AI helped produce an early draft.

Practical Steps

• Pick one recurring frustration your team experiences, especially a task that interrupts work or creates avoidable delay. Ask a willing teammate to test a small AI solution before rolling it out more broadly.

• Gather examples of strong and weak work you have previously edited, such as executive emails, project proposals, or decision memos. Put them into two columns and ask an AI tool to identify the differences. Refine its output into a short set of criteria for passing work.

• Turn those criteria into a reusable prompt or custom GPT. Give the tool instructions to assess work against your standards, explain gaps, and suggest a revision. Treat it as a first-round coach, not a substitute for review on high-stakes decisions.

• Demonstrate your own AI process in team meetings or one-on-ones. Show a real problem, the prompts you used, where the output was weak, and how you improved it. Pair people who are comfortable with AI with teammates who need hands-on practice.

• Set a clear quality bar for AI-assisted work. If a document is generic, unfocused, or reads like unedited machine output, send it back with specific direction: the draft is a starting point, and the employee must add judgment, context, and accountability.

• Build a daily habit of trying one slightly difficult AI task rather than waiting to take a formal course or reserve a large block of time.

Notable Quotes

• Hilary Gridley: "A manager is just somebody who makes their team more than the sum of their parts."

• Hilary Gridley: "The AI sort of, it shows you what's going on inside a person's brain, right? And it is an amplifier."

• Hilary Gridley: "If it seems impossible for you to explain to someone else what good looks like in your domain... you probably haven't thought about it enough as a manager."

Worklife with Molly Graham • 25m

Overview

Molly Graham examines "layering": when a company brings in a more experienced leader above an existing employee or divides that person's role as the business grows. The episode explains why this often happens in healthy, fast-growing companies, why it can feel like a personal failure, and how both employees and managers can handle it without turning a hard transition into a damaging one.

Former Facebook operations leader Don Fall shares his experience of being layered by Sheryl Sandberg. Though he initially saw it as a setback, he later found that the narrower scope helped him improve, learn from his new manager, and build a stronger career.

Key Takeaways

• Layering is often a capacity problem, not a performance verdict. A role may grow beyond what one person can reasonably manage, even when that person is doing strong work. Don says he believed his team was performing at an "eight out of 10," but later saw that it had been closer to a three or four because the scope had outpaced his ability to focus.

• The first reaction is usually emotional, and that reaction should not dictate a career decision. Losing scope, autonomy, status, or a direct reporting line can feel like a demotion. Graham advises people to make room for disappointment, then assess whether the revised role still offers meaningful work, learning, and room to grow.

• A layered employee should judge the situation by future experience rather than title math. Future employers are more likely to care about the scale of the work, problems solved, and leadership experience than the exact organizational chart at one point in time.

• Managers set the tone through the way they communicate. Sheryl Sandberg spent serious time explaining why Facebook needed to split Don's role and how the change could help him. That care gave him a reason to stay long enough to see whether the arrangement could work.

• Leaders should not use layering as a vague announcement. Before speaking with the employee, they need a clear account of the business gap, a meaningful proposed role, areas where the employee can shape the transition, and reasons to stay such as compensation, equity, or development opportunities.

• Layering carries a real retention risk. A company may make the right organizational call and still lose the person affected. Leaders should plan for that outcome rather than resent an employee for leaving.

Practical Steps

For employees who are layered:

• Do not resign in the first conversation unless the new role is plainly unacceptable. Give yourself time to process the news before making a decision.
• Ask direct questions: What problem is the company solving? What will I own? What will I learn from the incoming leader? What does growth look like in the revised role?
• Consider a three- to six-month trial period. Use it to assess the new manager, the actual scope of the role, and whether resentment fades or grows.
• If you decide to leave, do it deliberately. Protect the relationships and reputation you have built rather than reacting in anger.

For leaders considering layering:

• Diagnose the real issue first. Coach the current leader on performance or capacity gaps so the decision does not arrive as a total surprise.
• Present a concrete plan rather than asking the affected person to invent their own future job while upset.
• Check in repeatedly after the announcement. The employee may question their value, security, and future at the company.
• Speak to the current leader before external recruiting becomes visible. Being surprised through rumors or candidate outreach destroys trust across the wider team.

Notable Quotes

• "You can choose to feel sorry for yourself. That will not help you." - Don Fall

• "The company is growing faster than your ability to scale, and that's no slight against you." - Sheryl Sandberg, quoted by Don Fall

• "Your time is a compass." - Molly Graham

Decoder with Nilay Patel • 1h 15m

The Story

Tony Schneider arrived at Bluesky after a career spent around open systems, first as CEO of Automattic and later as an investor at True Ventures. He initially joined Bluesky as interim CEO after founder Jay Graber chose to move into a chief innovation role, focused on new ideas and the collision between AI and the AT Protocol. Within a few months, Schneider decided to stay. He says the job, team, and mission felt like a long-term fit, and he now frames Bluesky as a project with a decade or more of work ahead.

That timeline matters because Bluesky is trying to be two things at once. There is the familiar Bluesky app, a text-first social network with about 45 million users, and there is AT Protocol, the open technical layer beneath it. Schneider sees the app as an on-ramp to a broader network of interoperable services, now called the "ATmosphere." His hope is that users enter through Bluesky but gradually encounter newsletters, blogs, communities, games, video, and other services built by independent developers.

He points to Standard Site as an early example. Several publishing products agreed on a shared format for longer posts, and Bluesky can now display those posts inside its app. WordPress and other publishing tools are beginning to support it. The idea is that publishers should be able to put work into one format and have it travel across many services without negotiating separate platform deals.

The next major bet is private data and private communities. AT Protocol has largely been public so far, which limits it to broad publishing and public conversation. Schneider says Bluesky is building support for groups, membership-based spaces, and other private interactions. He compares the aim to getting the benefits of a Reddit-like community or a self-run forum without forcing every group to become an isolated island.

Main Themes

The central tension is whether Bluesky can grow a mainstream app without abandoning its open-network principles. Schneider is comfortable leaving some formats to other developers. He does not want Bluesky itself to become TikTok, even as other major social platforms chase full-screen video. In his view, Bluesky can remain a place where people discover content and then leave to watch, read, subscribe, donate, or participate elsewhere.

That same philosophy shapes moderation. Bluesky still employs moderators and contractors, but its labeling system is designed to be shared and extended by others. Blacksky, for example, runs its own moderation approach for a Black-focused community while remaining connected to the wider network. Schneider argues that different communities need different rules, rather than one company imposing a single standard everywhere.

He also wants Bluesky to give up control over the protocol over time. The company is transferring its identity directory to a separate Swiss nonprofit and moving AT Protocol's specification toward the IETF. Schneider says the test is whether the network can continue functioning if Bluesky disappears. A major DDoS attack offered a partial proof: Bluesky went down, while Blacksky continued operating.

Money remains unresolved, but Schneider ruled out ads as the main model. He described something closer to affiliate revenue: if Bluesky or another AT Protocol service sends a publisher a paying subscriber or customer, it could share in that transaction. The goal is a business model where the company earns alongside publishers and developers, rather than trapping attention inside a feed.

Finally, Schneider acknowledged Bluesky's political reputation as a progressive alternative to X. He wants to broaden that identity, not erase it, by building stronger communities around sports, live events, and other interests. The company’s wager is that a more open social network can become larger and more varied without turning into the same kind of closed platform it set out to replace.

The Ezra Klein Show • 58m

The Story

Ezra Klein brings Maggie Haberman on to talk about "Regime Change," the book she wrote with Jonathan Swan about the opening stretch of Donald Trump's second term. The conversation starts with a basic question: after that first burst of action, what does year two actually look like? Haberman pushes back on the idea that everything has simply fizzled. Some things did stall or collapse, like Elon Musk's promised government overhaul through DOGE. But she argues that plenty of the administration's early moves still mattered because they showed how Trump governs: by impulse, by executive action, and with little patience for process, Congress, or limits.

From there, the episode turns into a portrait of a presidency that keeps creating its own emergencies. Trump is no longer driving events the way he did at the start; now he is often reacting to problems he helped set off, whether that's the trade war, the Iran conflict, or the political mess around Epstein. Haberman's reporting on Iran is one of the clearest threads in the talk. She says Trump's hostility toward Iran is old and personal, sharpened by years of feeling under threat and by the alleged Iranian murder plot against him in 2024. Netanyahu may have helped sell him on military action, but Haberman is clear that Trump did not need much convincing. He already believed Iran was weak, that regime pressure might work, and that things usually work out for him.

What gives the conversation its edge is Haberman's sense that this White House runs on mood, spectacle, and a very small circle of people. Trump absorbs information visually, through television clips, printouts, and whatever is put directly in front of him. Natalie Harp appears in the discussion as a symbol of that world: the "human printer" handing him flattering stories and preferred data. Meetings sound less like a disciplined process and more like a rolling open-door session where people drift in, pitch him on firings or military moves or culture-war grievances, and he issues orders on the spot.

By the end, the picture is less of an administration building a durable project than of one centered entirely on Trump's appetites. Haberman says there really is no fixed Trump doctrine beyond Trump himself. There are people around him with plans, especially Stephen Miller on immigration, but they do not form a coherent governing program. Congress barely features except as a submissive body. Corruption sits in plain view, yet Haberman argues that scandal no longer works the way it once did because partisan media, weak oversight, and Republican acquiescence blunt the effect. What seems to hold Trump's attention most, oddly and revealingly, is not policy but self-monument: arches, ballrooms, renovations, visible marks that cannot be taken away.

Main Themes

The main idea running through the episode is that Trump 2.0 is less ideological than personal. Klein keeps testing whether there is such a thing as Trumpism, and Haberman mostly rejects that frame. Trump has a few fixed obsessions, immigration above all, but the larger pattern is instinct, grievance, revenge, and self-protection. His movement exists, she says, yet it remains tied to one man who can redefine its meaning whenever he wants.

Another big theme is the collapse of normal governing structure. Haberman describes a White House where interagency review barely matters, where bad news is filtered, and where people tailor everything to Trump's tastes. That leads straight into the episode's focus on spectacle. Trump judges by visuals, communicates by visuals, and understands politics as an attention game. The same style that made him powerful also traps him in a feedback loop where appearance can replace reality.

The last theme is impunity. Haberman's argument is that Trump's corruption and overreach do not land the way older scandals did because the institutions that once turned revelation into consequence are weaker, slower, or politically captured. Republicans, in her telling, are the core reason. That leaves a presidency driven less by policy ambition than by unilateral action, endless conflict, and the desire to leave behind monuments to himself.

Platformer • 1h 14m

Overview

This episode looks at two sides of workplace AI. First, Casey Newton and Ella Marcianos discuss whether AI is actually cutting jobs or whether companies still need plenty of people to manage, guide, and clean up after AI systems. Then Casey talks with Chris Pedregal, CEO of Granola, the AI meeting note taker, about why note-taking has taken off, what happens when every conversation can be recorded, and whether these tools save time or just make work denser.

The center of the episode is Granola's bigger ambition. Pedregal says meeting notes are only the entry point; the real goal is to become a "context layer" that feeds useful information into AI agents and other workplace tools.

Key Takeaways

Pedregal's main point is that transcripts are valuable less because people constantly reread them and more because they remove the fear of losing information. Users may not search old notes often, but knowing the record exists matters. He argues that forgetting is normal, and the product works when it can recover the one detail you suddenly need.

A second idea is that meeting transcripts are "context exhaust." People naturally generate useful work context by talking, and tools like Granola can capture it with almost no extra effort. Pedregal sees that as the real asset: agents are smart, but they know very little about a user or a company unless they can tap into that context.

He also draws a hard line on privacy. Granola's notes are private to the user by default, even when an employer is paying. Pedregal says he does not want managers or executives using a hidden backdoor to inspect everyone's meetings and run surveillance queries. The harder problem ahead, in his view, is not whether meetings should be transcribed, but which parts of that context should be shareable and which should stay personal.

On product design, Granola's "invisible" recording model came from a practical goal: make it work across Zoom, Slack huddles, and in-person meetings without a bot joining the call. That gives users flexibility, but it also shifts disclosure and consent onto them. Pedregal says platforms like Zoom and Google Meet have not made it easy for third-party apps to show clear, built-in consent prompts.

The broader argument is that meeting notes are only the first useful AI work tool, not the end state. Pedregal thinks the bigger fight is over which apps become the daily interface for work in an AI-heavy future. He does not think everyone will live inside a single chatbot, but he does think a small number of products will dominate how people get work done.

Practical Steps

• Use AI notes for low-friction capture, not for perfect recall. Turn it on in meetings where you usually split your attention between listening and typing.
• Tell people when you're recording or transcribing, even if the software doesn't force you to. Set the norm yourself.
• Separate personal context from shared work summaries. If you use these tools on sales calls or 1:1s, decide what should stay private before you circulate notes.
• Test whether AI context actually helps your workflow. Ask your note tool or connected model a few real questions from past meetings rather than assuming the archive is useful.
• Keep your notification load under control. The episode makes the case that distraction, more than note-taking itself, is a big reason work feels fragmented.

Notable Quotes

• Chris Pedregal: "Transcripts are this goldmine of context."
• Chris Pedregal: "I actually think forgetting and not going back and looking at stuff is a feature as much as a bug."
• Chris Pedregal: "Does AI feel like it is my tool... or is it the company's tool or the government's tool or the system's tool?"

In Depth • 1h 8m

Overview

This episode is a detailed look at why some executives move from big companies to startups and struggle, while others adapt well and keep growing. The guest explains why her move from Google Cloud to Checkr worked, then gets into a bigger set of operating questions: how to build an enterprise motion, how to run a company without drowning in process, and what changes when a CRO becomes COO.

A lot of the conversation comes back to fit, focus, and operating discipline. The guest argues that enterprise growth is rarely a simple extension of a mid-market playbook, and that scaling well depends on tight feedback loops, clear decision ownership, and staying close to customers.

Key Takeaways

The guest says her transition from a large company to Checkr worked for three reasons. First, she likes entering new environments without assuming her old playbook will apply. Second, her Google Cloud experience was closer to startup building than outsiders might think: she describes it as scrappy, hands-on, and short on support functions for years. Third, she says Checkr wanted exactly what she brought at that moment: a data-driven operator who could help expand from a strong position in gig hiring into a more segmented, repeatable sales motion.

Her view on enterprise expansion is blunt: many companies underestimate how different it is. Selling to a 25,000-person company means long buying cycles, many stakeholders, lower brand awareness in the right buyer set, and product requirements that do not show up in smaller accounts. A company can hurt itself by overcommitting to every prospect request, but it can also fail by dismissing enterprise needs too quickly. The hard part is telling apart a real deal blocker from a nice-to-have.

Focus matters more than ambition. Rather than "going enterprise" in the abstract, she recommends picking a narrow set of verticals where the product already performs well and where the company has a clear advantage over incumbents. She describes this as finding "rich niches" and doubling down once the data shows traction.

She also makes the case that enterprise success cannot sit inside sales alone. Product, engineering, marketing, legal, and leadership all have to commit. At Checkr, she says they had to align explicitly across functions before making another push into enterprise. The tight learning loop mattered just as much: product leaders and PMs were in customer calls constantly so they could adjust quickly.

As COO, one of her biggest lessons has been around decision-making. If decisions always flow upward, senior leaders become bottlenecks and the company slows down. Her fix is simple: define who the DRI is and who actually makes the decision, then keep pushing decisions lower unless they are high-stakes strategy, major hiring, M&A, or other one-way-door choices.

Practical Steps

• Before hiring a senior operator from a large company, test for adaptation, not pedigree. Ask how they learn a new environment and whether they can work without heavy support infrastructure.
• If you want to move upmarket, start with a deep vertical analysis of your current customer base. Look at retention, expansion, speed of sale, and where customers get the most value.
• Build a small, dedicated enterprise group early:
  • enterprise-experienced sellers
  • a product manager embedded with the team
  • marketing support built for account-based work and events
• Do not promise every feature request. Force a distinction between:
  • deal blockers
  • strategic capabilities
  • customer-specific noise
• Create a fast feedback loop between sales and product. Put PMs and product leaders in customer calls regularly.
• Define decision rights clearly. For every major initiative, name a DRI and a decision-maker.
• Stay close to customers even in senior roles. The guest says dashboards miss emerging problems and opportunities that show up first in live conversations.
• For sales comp, align pay to realized customer value. In Checkr's case, the guest says moving reps from bookings-based incentives to revenue-based incentives improved behavior and outcomes.

Notable Quotes

• "There's no scarier job than being the first CRO at a startup."
• "You can't go from being a challenger in the market to capturing everything overnight."
• "You could make a really good decision and still have a bad outcome. Or you can make a really poor decision and have a good outcome."

Worklife with Molly Graham • 45m

Overview

This episode centers on what pressure teaches leaders that ordinary work often does not. Molly Graham talks with Don Faul, a former Marine officer, early Google operator, former Facebook executive, and past CrossFit CEO, about how leadership changes when the stakes are high and people are watching your every move.

Faul’s main point is that strong leadership has less to do with projecting certainty and more to do with humility, emotional control, service to the team, and the willingness to act on conviction when the easy path would be safer.

Key Takeaways

Faul says he began his career with a common but weak idea of leadership: the leader should look confident, have the answers, and direct everyone else. Over time, especially at Facebook, he came to see that better leadership means recognizing uncertainty without spreading panic. The leader’s job is to help a group move toward an answer even when no one has one yet.

A second theme is servant leadership. Faul argues that the Marine Corps is often misunderstood as purely top-down. In his telling, the culture teaches leaders that they exist to support their people, not the other way around. He points to General Mattis walking the lines and sitting with junior Marines as an example of what that looks like in practice. The signal mattered: seniority does not excuse distance.

He also stresses ownership. At the Naval Academy, he learned to stop making excuses and start treating obstacles as his responsibility. That mindset later helped him move into tech despite, as he tells it, having no background in the field. He did not assume qualification had to come before contribution.

The conversation gets sharper when it turns to moral courage. At Facebook, Faul dealt with hard policy and operations calls where every option carried costs. He says those moments were rarely clean or obvious from the inside, even if they looked simple from the outside. His regret is not that some decisions were hard. It is that there were times in his career when he lacked the courage to push for what he thought was right.

Another useful point is about pressure and self-awareness. High-stakes situations create more chances to become petty, reactive, or ego-driven. Faul describes learning that he could not always control events or other people, but he could control how he showed up. That, in his view, is a leader’s real test.

Practical Steps

• Watch the emotional signal you send. In tense moments, people read your tone, posture, and pace as much as your words. Before speaking, ask what state you are spreading through the team.
• Treat your role as service. Check whether your team has what they need to do the job well. Make support visible through regular one-on-ones, follow-up on blockers, and direct contact with frontline staff.
• Replace excuses with ownership. When something slips, skip the long explanation first. State what happened, what you know, and what you will do next.
• Build teams that can decide without you. Be clear about the mission, the constraints, and what success looks like, then let people act.
• When emotions spike, buy time. Faul’s advice is simple: if you feel yourself going from zero to ten, pause. Delay the comment or decision when you can. A little space can keep you from making a bad call for ego-driven reasons.
• Review your regrets honestly. Ask where you stayed quiet because speaking up had a cost. That is often where your next leadership lesson is.

Notable Quotes

• "Your job as a leader is to support and serve your people." - Don Faul
• "Great leadership is often about understanding the uncertainty." - Don Faul
• "I can still control how I show up. And that's what I should be accountable for." - Don Faul

One Knight in Product • 1h 6m

Overview

This episode is about "product delight" and what it takes to build products people remember, enjoy, and stick with. Dr. Nesreen Shangel argues that delight is not decoration or gimmicks. It comes from combining functional value with emotional connection in ways that reduce friction, anticipate needs, and exceed expectations.

A big part of the conversation deals with whether this applies outside consumer apps. Nesreen says yes: even in B2B, buyers and users respond to products that make them feel understood, effective, and proud to use them.

Key Takeaways

Nesreen defines delight as more than satisfaction. A product can work fine and still leave no impression. Delight happens when a product serves both the practical job and the emotional reason someone keeps coming back. She points to three drivers: removing friction, anticipating needs, and exceeding expectations.

Her examples make that concrete. Uber's quick refund flow after a canceled ride is delight through friction removal. Revolut adding eSIM support for frequent travelers is delight through anticipating needs. Microsoft's Edge finding and applying coupons at checkout is delight through exceeding expectations. In her framing, delight is often less about "wow" moments and more about taking stress out of the experience.

She also draws a useful line between "surface delight" and "deep delight." Surface delight is the light-touch stuff: balloons on your birthday, confetti, playful micro-interactions. Deep delight is when the feature improves the core task and creates a positive feeling at the same time. Her point is that teams often overfocus on surface touches because they are visible, while the stronger gains usually come from deep delight.

The B2B section is one of the more interesting parts of the discussion. Nesreen says emotionally aware products matter even when procurement signs the contract and employees have no choice about the tool. Her argument is simple: users are still human, and companies want retention, revenue, and referrals. She cites studies from firms including HBR, McKinsey, Deloitte, and Capgemini that, in her telling, point to the same pattern: emotionally connected users are more likely to stay, recommend, and spend.

She is also careful about where delight can go wrong. A Deliveroo Mother's Day campaign that mimicked a missed call from "Mom" landed badly because it ignored grief and personal context. Gesture-based effects during video calls created awkward moments in serious settings. Her warning is clear: delight has to be inclusive, situational, and tied to what users actually value. Otherwise it turns into distraction or harm.

Practical Steps

Start by mapping user motivators in two columns:

• Functional: what they need to get done
• Emotional: how they want to feel, or avoid feeling

Then review your roadmap against those motivators. If most items only serve functional needs, look for ways to improve them with emotional value built in. That might mean personalization, clearer feedback, less stress, or giving users a sense of competence or pride.

Use Nesreen's "delight grid" to sort planned features into:

• Low delight: functional only
• Deep delight: functional plus emotional value
• Surface delight: emotional touch only

She recommends aiming roughly for a 50-40-10 mix across those three categories. Treat that as a guide, not a rule.

If stakeholders are skeptical, don't pitch "delight" as a feel-good idea. Connect it to what they already care about: retention, referrals, revenue, adoption. Her advice is to shift from your own perception to their perspective and make the case in their language.

Finally, validate for risk before shipping. Check whether a feature could confuse, embarrass, exclude, or distract users. A delightful idea in one setting can become a bad experience in another.

Notable Quotes

• "Delight is the ability to create products that do not only serve functional needs."
• "Delight is a combination of surprise and joy." - Nesreen Shangel
• "What do they care about? What's their value? They value retention. They want people to stay longer. Do they value revenues and more monies? Then try to show that delight or emotional connections is going to drive them toward that goal." - Nesreen Shangel

One Knight in Product • 53m

Overview

Georgie Smallwood talks about leading product, technology, and data at Moonpig without getting trapped by product dogma. The episode covers how Moonpig balances a very human product experience with modern tech, and how Georgie thinks about team structure, empowerment, and leadership at executive level.

A thread running through the conversation is that good product leadership is less about following a pure model and more about making sound trade-offs in a commercial business.

Key Takeaways

One of Georgie’s clearest points is that "empowered teams" should not be treated as a permanent operating law. She says it is unrealistic to expect a large commercial company to run that way every day, and risky to build a culture where top-down decisions are seen as unacceptable. Her view is practical: sometimes market shifts, business pressure, or timing mean direction has to come from above, and teams need to be able to absorb that without stalling.

She also makes a strong case for product organizations staying flexible. Georgie has tried many org designs and does not think there is one right structure that always wins. What matters is whether the setup fits the next phase of the business and can be changed without too much friction. She is wary of locking in plans, structures, or roadmaps so tightly that the company cannot respond when conditions change.

On product development itself, she comes back to the product trio - product manager, engineering manager, and designer. In her experience, teams tend to do their best work when that group is working closely and well together. The structure around them may vary, but that core partnership tends to show up in the strongest teams.

There is also an interesting tension in how Moonpig uses technology. Georgie is not arguing against AI or digital progress. She is arguing that tech should support things people still value in the physical world, rather than flattening them into disposable digital interactions. Her example is Moonpig’s AI stickers: customers like them because they fit an existing mental model. The AI is powerful, but the interface feels familiar. That is a useful lesson for any mass-market product. New technology lands better when customers can understand it through something they already know.

Her comments on leadership are also blunt and useful. Moving from product into a broader role across engineering and data forced her to stop relying on functional expertise as the source of credibility. Instead, she says she learned to ask better questions, listen carefully, and test her understanding out loud. That shift matters for senior leaders who can no longer be the person with all the answers.

Practical Steps

• Treat empowerment as a spectrum, not a purity test. Decide in advance where teams have freedom and where leadership may need to step in quickly.
• Review org design against the next 6-12 months, not some ideal future state. Ask whether the current setup helps the business move now.
• Protect the product trio. If a PM, engineering lead, and designer are not working as a unit, fix that before redrawing the whole org chart.
• Introduce new technology through familiar use cases. If customers already understand the behavior, adoption gets easier.
• As a senior leader, build credibility by asking clear questions and checking your understanding, especially in areas outside your original discipline.

Notable Quotes

• "I've never worked in a business where it has been empowered teams every day of every week of every year." - Georgie Smallwood
• "It's really dangerous to build a culture where it's not okay for something to come in from the top." - Georgie Smallwood
• "The best things are always the scariest." - Georgie Smallwood

Decoder with Nilay Patel • 1h 11m

The Story

Evan Smith comes back to Decoder sounding less like a founder with a thesis and more like someone whose thesis has been stress-tested by reality. A year and a half ago, he and Nilay were talking about globalization getting harder to manage. Now Smith says that shift is no longer theoretical. Trade is more fractured, rules are changing constantly, and Altana's pitch - software that maps and manages global supply chains - has moved from a smart bet to a practical tool for governments, logistics companies, and major importers trying to keep up.

That leads into the first tension running through the conversation. Nilay pushes on the human cost behind phrases like "an index bet on dislocation." Smith doesn't dodge it. He argues that trade is tied to ordinary life at the most basic level: food, fuel, medicine, prices. His case for Altana is that globalization is not going away, but the old version is. What replaces it will need more verification, more enforcement, and a lot more coordination.

From there the discussion splits in two directions at once. One is about AI and software work itself. Smith says Altana is just under 300 people, only modestly larger than last time, partly because AI is changing how the company builds products. Product managers, designers, and engineers are starting to blur together. Domain experts in customs, procurement, and logistics are getting pulled much closer to product decisions. Smith sounds excited by the speed gains, but also pretty candid about the tradeoff: it is easy to prototype with AI, much harder to ship reliable software for customers who expect near-perfect uptime and are running high-stakes operations.

The other direction is the world Altana is trying to model. Smith says the tariffs meant to reduce dependence on China have mostly rerouted Chinese goods through countries like Vietnam, Mexico, Malaysia, and Canada rather than moving production cleanly into the United States. He says the data shows imports shifting, but not true separation. Manufacturing output in the US may be recovering after an initial hit, yet manufacturing jobs, by his account, are still declining, which points to automation more than some factory-job revival.

That broader point carries into defense and geopolitics. Smith argues that chokepoints - rare earths, shipping lanes, energy routes like the Strait of Hormuz - are now central instruments of power. Economic pressure can turn into military pressure fast. Altana's role, as he sees it, is to help governments and companies identify these weak points before they blow up: trace goods, model dependencies, simulate shocks, and build alternatives.

By the end, Smith brings it back to the architecture of his own company. Altana works by federating data instead of demanding everyone dump sensitive information into one common pool. Governments and companies keep control of their own data, while Altana builds a shared supply-chain graph and improves its models from the patterns it learns. His closing argument is basically that better visibility creates more economic security, and that more economic security lowers the odds that states reach for open conflict.

Main Themes

The episode keeps circling one big idea: globalization did not end, but trust in the old way it worked did. What used to be a mostly invisible system of moving goods is now crowded with tariffs, customs checks, export controls, national-security rules, and outright geopolitical rivalry. Smith's whole argument is that software has become part of the machinery that makes trade possible under those conditions.

AI sits right in the middle of that shift. On one level, it is helping automate the bureaucracy: customs declarations, standard operating procedures, document handling, classification. Nilay is skeptical of the absurdity of using AI to fill out forms created by political decisions that made trade more cumbersome in the first place. Smith's answer is that the forms are not going away, and agent-based systems are getting good enough to handle the mess. On another level, AI is changing the internal shape of software companies, shrinking some boundaries between roles while raising fresh questions about testing, reliability, and cost.

The policy thread is sharper than the usual trade talk. Smith's read is that tariffs alone have not done what their advocates claimed. They changed routing, raised costs, and pushed work into more complicated channels. When industrial policy has had visible effects, he points more to directed demand, bans, subsidies, and procurement - especially in areas like drones and defense supply chains.

Under all of it is a darker point about the future. Supply chains are no longer just commercial systems. They are instruments of state power. Whoever controls the bottlenecks can apply pressure without firing a shot, at least at first. Smith is betting that if governments and companies can see those networks clearly enough, they can build enough redundancy to make the whole system less fragile and a little less likely to tip into war.

One Knight in Product • 50m

Overview

Sean O'Neill talks about why product teams stall even when the company has a clear vision. His main point is that strategy fails in practice when most capacity gets swallowed by one-off customer work, internal dependencies, and too many parallel initiatives.

He also reflects on what he took from Amazon, how those ideas translate into older companies, and why leaders need to be honest about whether they are building a repeatable product business or a bespoke software business.

Key Takeaways

O'Neill's top principle is to "solve real user needs." He argues that the worst waste of capacity is building something nobody actually wants. Close behind that is releasing value in small slices so teams get feedback early, instead of disappearing for months and emerging with a big launch based on wishful thinking.

A third theme is simplification. He says teams regularly overestimate how manageable dependencies will be, then get blocked by one missing piece from another team. That makes simplification less about elegance and more about survival: cut scope, reduce dependencies, and ship what matters.

On strategy, his sharpest point is about capacity allocation. A company may say it believes in long-term bets, but if only a small share of engineering time goes to them and the rest goes to customer-specific commitments, the strategy will lose by default. He describes those one-customer features as a sinkhole that drains the time needed for real progress.

He also makes a useful distinction between two valid business types. Some firms win through repeatable product propositions. Others win through relationships and custom delivery. Either can work, but leaders need to know which one they are running. If you are effectively a consulting and bespoke software shop, you should not expect product-style scale or economics from that work.

Another strong idea is that product-market fit is only the start. O'Neill says companies also need "business model fit" and some form of defensibility. A product that customers like can still be exposed if rivals can copy it easily or outspend you.

Practical Steps

• Audit where capacity goes today. Count how many teams are spending time on strategic bets, migrations, internal support, and customer-specific requests.
• Put the current portfolio on one page. If you cannot explain the work simply, you probably have too much in flight.
• Tag customer requests by expected reuse. Ask whether a feature is likely to serve one customer or many, and decide accordingly.
• Ship in smaller increments. Create earlier feedback points for desirability, feasibility, and commercial value.
• Remove dependencies before launch planning. For each initiative, ask what can be cut or changed so another team does not hold the whole release hostage.
• Run a long-range trend exercise. O'Neill describes working backward from changes that may hit in 10 to 20 years, then asking what the company should invest in now to be ready.
• Review org design as if it were a product. He argues structures drift over time, so leaders should regularly ask whether the current setup still matches the business problem.

Notable Quotes

• "There is no greater crime of wasting capacity than to build something that the universe has no need for." - Sean O'Neill
• "Product-market fit is not the finish line." - Sean O'Neill
• "That is the sinkhole that steals your precious capacity from strategic progress." - Sean O'Neill

Decoder with Nilay Patel • 44m

The Story

Nilay Patel brings Hayden Field on to talk through Apple's trade secret suit against OpenAI, but the conversation quickly turns into something bigger: whether OpenAI can survive one more major problem at a moment when it already seems overextended. Apple's claims are blunt. The company says former Apple employees at OpenAI pushed job candidates for confidential information, asked for "show and tell" with hardware, and pulled files tied to manufacturing. OpenAI denies the allegations, and Hayden is careful to frame them as claims, not settled facts. Still, after talking to trade secret lawyers, her takeaway is that the case looks serious, especially because Apple is the one bringing it.

Nilay places the fight in a long Apple tradition. Apple has used copyright, patent, and now trade secret law against rivals, from Microsoft to Samsung. The difference, he argues, is that those companies were big enough to absorb years of litigation and huge bills. OpenAI may not be. Hayden agrees, and she keeps coming back to Apple's appetite for a long fight. In her reporting, lawyers described Apple as relentless. If Apple wants to drag this out, it probably can. The question is whether OpenAI can keep paying for everything else while that happens.

From there, the lawsuit becomes a window into OpenAI's consumer hardware ambitions. Tang Tan, a longtime Apple executive who later joined Jony Ive's hardware company before OpenAI bought it, sits near the center of the story. That matters because this is not really a lawsuit about language models. It is about hardware, which is the part of the business Apple knows best and protects most aggressively. Nilay's read is that OpenAI was trying to do two huge things at once: threaten Google in consumer AI and then threaten the iPhone with new devices. Hayden sounds doubtful that either path is working.

They spend a lot of time on that doubt. Hayden says OpenAI has already shifted focus toward enterprise and coding, the parts of the business that actually look like revenue drivers, while its hardware plans now feel like an expensive commitment it can't easily walk away from. Neither of them seems convinced that consumers want standalone AI devices in large numbers. Nilay compares this moment to the smartphone wars, when the world clearly wanted smartphones and the open question was who would win. Here, the more basic question is whether people want the category at all.

By the end, the lawsuit feels less like an isolated legal fight and more like pressure applied at exactly the wrong time. OpenAI is still burning cash, still reorganizing, still losing executives, and still trying to tell a coherent story ahead of an IPO that may already be slipping. Hayden's view is that the company knows there is a gap between AI hype and consumer trust, but probably does not grasp how wide it is. If that gap stays open, the hardware dream starts to look less like a bold next move and more like another distraction.

Main Themes

The main thread running through the episode is fragility. Apple can treat a lawsuit like another line item. OpenAI cannot. That imbalance shapes everything: the legal risk, the business risk, even the timing of a public offering. Hayden and Nilay keep circling back to the idea that a company can survive bad headlines, executive turnover, expensive bets, or a giant lawsuit, but handling all of them at once is another matter.

Another theme is the mismatch between AI industry ambition and public demand. OpenAI still talks like a consumer giant in the making, yet the conversation suggests the real business may be elsewhere, in enterprise tools and coding products. Hardware sits awkwardly in that picture. The company has spent heavily enough that it cannot simply drop the plan, but nobody on the episode sounds convinced the public is waiting for five new AI gadgets.

There is also a strong thread about hypocrisy and memory in tech. Nilay points out that AI companies rage about model distillation while showing little sympathy for artists, writers, and musicians who feel their own work was taken first. Apple, for its part, may be protecting trade secrets and also using the suit to slow a rival. Both things can be true. That messiness is what makes the episode compelling. Nobody sounds shocked that powerful companies are acting out of self-interest. The real question is which of them can afford the consequences.

AI and I • 46m

Overview

This episode is a postmortem on Every's new "All Access" tier and the Builder Pack inside it. The team explains why they launched it, how they put it together, and why they say it drove the biggest subscription revenue jump in the company's history last week.

Most of the conversation is less about pricing or packaging and more about how they work with AI day to day. Their main point is that the pack matters because it gives people access to the same stack they use internally, which lets them spend less time on setup and more time on decisions, testing, and shipping.

Key Takeaways

The strongest idea in the episode is that AI changes the shape of work when you stop treating it like a chatbot and start treating it like an operating layer. Yash gives a clear example: instead of manually setting up A/B tests, choosing audience sizes, and moving experiments from 10 percent to 50 percent by hand, he wants Claude and PostHog to run that loop so he can focus on what to test and why.

Several people describe this as being "at the top and bottom of the AI sandwich." A person defines the problem, points the tools at it, and reviews the output. The middle - the repetitive setup, maintenance, and production work - gets pushed to agents. That shows up in growth, coding, video editing, design, and internal ops.

Another useful point is that the tools get more valuable when they connect. Austin talks about Codex reorganizing Notion, while Claude, Fable, and Descript handle video prep in parallel. He says the output is not final, but it gets him most of the way there. That pattern comes up again and again: AI is not replacing judgment, but it is taking a large chunk of the first draft and setup work.

The team also makes a business case for the Builder Pack beyond convenience. They argue that serious AI use is expensive for individuals, especially people between jobs or early in their careers. Their pitch is that bundling credits, subscriptions, and product access lowers the cost of getting good at these tools.

Practical Steps

If you want to work the way this team does, their advice is pretty concrete:

• Pick one real project and build it now. Do not wait to find the "perfect" idea.
• Start by copying a product you already like. Build a stripped-down version, then change what annoys you. That is often how an original product starts.
• Choose something you would be excited to text to a friend. That emotional stake helps you push through the boring parts.
• Connect your model tools to the rest of your stack early. Let the agent touch tools like Render, PostHog, Notion, or design software instead of keeping everything in chat.
• Write down your process. Douglas says the upfront work is figuring out how you think. Once you've spelled that out, the tools can repeat it.
• If you work in marketing, design, writing, or product, rebuild your portfolio site. It is a bounded project with real upside, and it forces you to use multiple tools together.
• Look for "fake work" in your job - repetitive tasks, dashboard clicking, housekeeping in docs, test setup - and automate that first.

Notable Quotes

• "I should be working on the interesting, more interesting problems. And I should not be clicking buttons." - Yash

• "You decide what the problem is. You frame the idea and then you review the work." - Austin, describing the "AI sandwich"

• "Just start doing it." - Brandon, on the best way to begin building with the pack

Worklife with Molly Graham • 41m

Overview

This episode of WorkLife is about letting go as a leadership skill. Molly Graham talks with Chris Anderson about how TED grew from a small, exclusive conference into a global platform in part because he kept giving away pieces of control: opening talks to the internet, letting others run TEDx events, stepping back from the CEO role, and eventually handing TED to a new steward.

The through line is simple: the thing that made you successful can become the thing that limits your next stage. Anderson argues that generosity, when done on purpose and with standards, can expand an organization far beyond what tight control ever could.

Key Takeaways

Anderson’s central idea is that scale often comes from release, not protection. TED became much bigger after it stopped treating its content and brand as scarce assets to guard. Putting talks online for free and allowing TEDx organizers around the world to run local events were risky decisions, but they turned a closed institution into a worldwide one.

He connects that move to a wider pattern. He points to open-source software and Wikipedia as examples that helped make the case internally: if you let more people participate, you may create more value than you could have produced alone. That did not mean abandoning quality. It meant accepting some mess in exchange for far greater reach.

A second insight is that leaders often outgrow parts of their own job. Anderson says that once organizations get beyond roughly 150 people, he becomes less effective at running the whole system. He liked curation, ideas, fundraising, and shaping the event. He did not like managing a large organization. Instead of forcing himself to stay in the wrong seat, he narrowed his role and gave the CEO job to someone better suited for it.

The episode also shows that succession is easier when values are clearer than ego. When Anderson decided to pass TED to a new leader, he says more than 100 groups expressed interest, including some offering very large sums. He chose the nonprofit path and backed Sal Khan because he believed the fit on mission mattered more than the biggest check.

There is also a useful tension in his view of technology. He says he did not expect the disappointment that came with social media, even as he remains alert to the possibilities of AI. That matters because it shows that optimism is not blind faith. It is a willingness to keep betting on the future while admitting past bets had costs.

Practical Steps

• Make a list of responsibilities you are holding because you are good at them, not because you are still the best person to do them now.
• Identify one “Lego” to give away this quarter: a meeting, process, team decision, client relationship, or project area.
• Before handing something off, define the non-negotiables. Be clear about standards, values, and what must stay consistent.
• Test openness in a bounded way. Share one piece of knowledge, content, or process more widely than feels comfortable and watch what happens.
• If you are choosing a successor or partner, rank value alignment ahead of money, prestige, or familiarity.
• Ask this question regularly: “Am I protecting what built this, or am I making room for what could build the next version?”

Notable Quotes

• “A talk isn’t just a talk. A talk is a moment when something passes from one mind into hundreds of other minds.” - Chris Anderson
• “Humans are really weird. We are wired to be generous, but a lot of us don’t even know that.” - Chris Anderson
• “The person with the best idea can have it.” - Chris Anderson, describing his decision to seek the next steward for TED

Supra Insider • 1h 24m

Overview

This episode is a candid look at a senior product leader's job search after a layoff. Eric, a former product leader at Honey and founding product hire at Pi, talks through how he landed a new role in six weeks by leaning hard on referrals, staying organized, and being selective about fit rather than chasing logos.

The conversation also gets into what surprised him most: despite all the noise around AI, very few interviewers asked about how he uses it, even for senior product roles.

Key Takeaways

Eric's search started the day he was laid off. He says that being unemployed changed the emotional stakes, especially with an infant at home, and pushed him to move fast instead of taking a break. His first move was not cold applying. It was reaching out to people he already knew, with personal messages that gave context, explained what had happened, and made it easy for others to help.

The biggest edge came from referrals, especially when the referrer knew the hiring manager well enough to send a direct note. Eric says standard ATS referrals still helped, but the strongest outcomes came when someone inside the company could personally vouch for him and nudge the process forward. In a few cases, that even let him skip the recruiter screen and go straight to the hiring manager.

Another strong point was preparation. Eric built a simple job-search tracker and a resume workflow using Claude, which let him tailor his resume for each role without making things up. He was adjusting emphasis, not rewriting history. That helped him present the parts of his background most relevant to each job, whether the role leaned more toward leadership or individual contribution.

A more surprising takeaway was how unchanged the interview process felt. Eric says most loops still looked like recruiter screen, hiring manager, product sense, and sometimes a take-home. He expected much more focus on AI fluency and got almost none. The group's read was that many companies still do not know how to assess AI skill directly, or they assume strong candidates are already using these tools well enough.

Practical Steps

• Start networking before you need help. Eric's search worked because he had built real relationships over years, not because he sent perfect messages after the layoff.
• Reach out with context. Tell people what happened, what you're looking for, and why you thought of them. Keep the ask light unless a direct ask makes sense.
• Prioritize referrals over cold applications. Eric says he mostly ignored heavily trafficked postings and focused on paths that gave him a warm introduction.
• Ask for the strongest referral possible. Best case: someone who works directly with the hiring manager and will message them personally.
• Tailor your resume per role. Shift emphasis based on the job description, but keep the substance honest.
• Track your pipeline. Eric built a simple funnel right away so he would not lose momentum or miss follow-ups.
• Use late-stage conversations to evaluate the company. After getting an offer, Eric asked for more time with the hiring manager and CEO to clarify scope, expectations, resources, and whether he would be set up to succeed.
• Do not let recruiters be the only channel once an offer is on the table. Get direct answers from decision-makers.

Notable Quotes

• "Cold applying, it's dead." - Eric
• "The most effective referral will be somebody who directly works with the hiring manager." - Eric
• "I'd rather invest in myself and stay in the senior level." - Eric

In Depth • 1h 9m

Overview

This conversation is about operating a company through repeated shocks: the pandemic, a failed acquisition, a market crash in fintech, and the rise of AI. The COO's main point is that the job does not change because the headlines change. What matters is whether the company already has a culture, operating rhythm, and org design that keep people focused on customers and execution when everything around them gets noisy.

He also gets specific about what that looks like in practice: limit the number of people pulled into major events, make clear who owns decisions, stay close to the front lines, and build enough spare capacity into the system to react when the market shifts.

Key Takeaways

The standout idea here is that resilience is not a communications tactic. The guest pushes back on the idea that leaders can simply message their way through chaos. He says stability comes from a culture that was set long before the crisis, one that treats acquisitions, IPOs, and other headline events as milestones rather than the point of the company.

He argues that culture only becomes real when it changes hiring, promotions, pay, and exits. If values are not tied to who gets rewarded, they are just slogans. The hardest test comes when short-term results conflict with those values. In his view, once leaders make one “small” compromise for speed or metrics, more compromises follow and the culture loses meaning.

On scale, he says growth naturally adds friction, but companies can offset that by being explicit about ownership. One person should be accountable for each decision, even when many people contribute. He also prefers fewer management layers and expects senior leaders to stay capable of doing individual contributor work.

Another strong point is his framework for trade-offs: speed, risk, and cost. Faster is not always better, especially in a business where small errors can have real consequences for customers. In areas where accuracy matters, he says the team should be clear that precision beats pace.

His org design comments are practical. Plaid, as he describes it, moved from a generalist model to segment-based teams, then to vertical specialization, and later added horizontal product experts. The goal was to improve the customer experience across sales, account management, and support, while still bringing in deep expertise when needed.

Practical Steps

• Treat major company events as milestones, not end states. Keep repeating what the company exists to do for customers.
• Keep the working team on high-stakes events small when possible, so most of the company stays focused on normal execution.
• Define culture in writing. Then tie it to hiring, promotions, compensation, and who gets to lead.
• Address high-performing people who damage the culture early and directly. Start by separating bad intent from poor awareness.
• End meetings with a clear owner for each action item. One owner, not three.
• Use a simple decision frame for major projects: what trade-off are we making on speed, risk, and cost?
• Build some slack into teams and calendars. The guest’s view is that running at full capacity leaves no room to respond when the market changes or a new opening appears.
• Stay close to the point of attack. He says that means talking to customers and frontline employees constantly, not once a quarter.

Notable Quotes

• “This is just one potential milestone among many. It is not the end goal. It is not the finish line.”
• “You cannot get yourself separated from the point of attack.”
• “There’s almost no amount of money that I wouldn’t reasonably spend to make sure that one of our customers gets to a good outcome.”

Decoder with Nilay Patel • 1h 24m

The Story

Nilay Patel brings Bart Butler on for a very Decoder kind of conversation: what does it take to build software that asks people to trust it, and what happens when that trust runs into governments, growth pressure, and the ugliest parts of the internet. Butler is clear from the start that Proton does not mainly sell email, cloud storage, or calendars. It sells trust. The products matter, but the pitch is that Proton has arranged its technology and its business so that betraying users is hard by design. Data is encrypted so Proton often cannot read it, and the company makes money from users instead of ads, which Butler argues keeps the incentives pointed in the right direction.

That leads into the harder question Nilay keeps pressing: if trust is the product, where does it actually live? In the cryptography? In the nonprofit-style foundation that controls Proton? In the people? Butler says all of it has to work together. Proton is a Swiss corporation, but a controlling stake sits with the Proton Foundation, which is meant to guard the mission if management ever drifts. Even so, he does not pretend the structure frees Proton from market pressure. His point is almost the opposite. If Proton wants privacy to be the default for ordinary people, it has to get big enough to compete with Google and Microsoft on convenience and features, not just ideals.

The conversation gets more concrete once Nilay brings up the Stop Cop City case, where Proton turned over payment data to Swiss authorities, who then shared it with the FBI. Butler’s answer is blunt: no company is above the law, and Proton complies with valid Swiss orders. The whole bet, he says, is that Switzerland is a better buffer than many other jurisdictions and that Proton’s systems limit what can be handed over in the first place. But he also admits the pressure is rising. If European surveillance laws get worse, Proton is actively thinking about what it would mean to move parts of its operations elsewhere.

From there the episode turns into a long argument about child safety, age verification, and encryption. Butler does not deny the scale of harm, especially around CSAM. He says Proton spends heavily on abuse prevention and claims it can disrupt bad actors without scanning everyone’s content, though he refuses to explain exactly how. That is the sharpest tension in the episode: he asks listeners to trust a company whose whole philosophy is supposed to reduce the need for trust.

They end on AI, where the same conflict shows up again. Butler sees enterprises feeling pushed to dump sensitive data into model companies, and he wants Proton’s AI products to offer a different option. His view is that privacy should mean control over when and how you share data, not total isolation from modern tools.

Main Themes

The main thread is that privacy is not just a feature. It is a set of technical limits, legal choices, and business incentives that have to reinforce each other. Butler keeps returning to the idea that good intentions are weak on their own. If a company can read everything, mine everything, and change course when money gets tight, then promises about values do not mean much.

A second theme is that scale cuts both ways. Proton needs growth to matter, but growth can warp the mission it claims to defend. Butler insists those two goals are linked, while Nilay keeps pointing out how often companies say that right before the compromises start.

The episode also keeps circling one uncomfortable fact: every system has a failure point. For Proton, that might be the legal authority of the Swiss state, the practical limits of abuse detection in encrypted systems, or the pull of AI and enterprise demand. Butler’s case is that you cannot remove pressure from the system, only choose where it lands and reduce the damage when it hits.

Worklife with Molly Graham • 39m

Overview

This episode is a conversation between Molly Graham and Stacey Brown-Philpott about faith as a decision-making tool at work. Stacey explains how her Christian faith shapes how she leads, sets boundaries, takes risks, and makes sense of uncertainty, especially in high-stakes roles as CEO and investor.

The discussion stays grounded in real workplace situations: fundraising, company culture, hard conversations, and the pressure to hide parts of yourself in professional settings. Even for listeners who are not religious, the episode raises a broader question about where people get meaning, conviction, and steadiness when work gets shaky.

Key Takeaways

Stacey describes faith less as private belief and more as an operating system. Her definition comes from scripture: "the substance of things hoped for and the evidence of things not seen." In practice, she sees faith as what fills the gap between effort and outcome. You can do the analysis, make the plan, take the meetings, and still not know how things will come together.

That view shows up clearly in venture investing. Stacey says backing an early-stage company already involves a leap. Data matters, but it does not remove uncertainty. Her point is that many professional decisions are already acts of belief, even if people prefer not to call them that.

Another strong thread is identity at work. Stacey says she spent much of her earlier career, including at Google, keeping faith mostly in the background. Once she became CEO of TaskRabbit, she had more room to be open about it and to set clearer boundaries around church, family time, and rest. Status made that easier, which is an honest reminder that authenticity at work is not equally available to everyone.

One of the best examples in the episode is the interfaith group at TaskRabbit. It started from a one-on-one conversation and turned into a useful space for employees from different backgrounds to connect. Stacey says that foundation mattered later, during COVID and after George Floyd's murder, when people needed a place to share grief, fear, and lived experience.

The episode also gets at a larger problem: many people tie purpose too tightly to work. Stacey agrees that founders and ambitious professionals can pour so much identity into the company that work starts to function like religion. Her warning is simple: if work is your only source of meaning, work will eventually hurt you.

Practical Steps

• Identify the gap between what you can control and what you cannot. Do the work, make the plan, and name the part that still depends on trust, judgment, or belief.
• Set one boundary that reflects what matters most to you. Stacey's example was protecting Sunday mornings, then family dinner. The point is to make your values visible in your calendar.
• Start small if faith or values feel hard to discuss at work. A one-on-one conversation may be more realistic than a big public statement.
• Build a space for people to talk about meaning, not just performance. At TaskRabbit, that became an interfaith group. In other settings, it could be a discussion group, employee community, or informal check-in.
• Prepare language for parts of your identity that others may question. Stacey had to figure out how to explain "we leverage our faith as much as our intellect" in a way that fit her work as an investor.
• Put some source of purpose outside your job. Stacey mentions community service, mentoring, and other forms of contribution that keep your identity from rising and falling with work.

Notable Quotes

• Stacey Brown-Philpott: "Faith is the substance of things hoped for and the evidence of things not seen."
• Stacey Brown-Philpott: "We have done all the data. We have done all the analysis. But we are still making a bet. So what's the difference between making a bet and having faith?"
• Stacey Brown-Philpott: "It's not healthy to attach your sense of meaning to what's on this earth, like what's here right now."

Eat Sleep Work Repeat - better workplace culture • 55m

Overview

Bruce Daisley talks with Professor Rob Briner about a simple question that gets ignored in workplace advice: what problem are you actually trying to solve? Briner’s case is that many popular ideas in management and HR - engagement, psychological safety, culture change, growth mindset - are either poorly defined, weakly measured, or adopted long before anyone checks whether they help.

The conversation is a pushback against management fashion. Briner is not arguing that workplaces cannot improve. He is arguing that vague concepts and trendy tools often distract from the harder, better work of identifying specific behaviors, testing options, and using evidence.

Key Takeaways

Briner’s starting point is that skepticism is not cynicism. He says most people pushing workplace ideas mean well, but good intentions do not stop companies from wasting time and money on ideas that do little or nothing. His frustration is with waste, and with the missed chance to make work better by being more precise.

On engagement, his argument is blunt: many organizations measure it without a clear definition of what it is, and often without checking whether their scores predict anything that matters. He says engagement measures overlap heavily with job satisfaction, and job satisfaction has long shown only a weak link to performance. So teams end up staring at tiny survey score changes as if they mean something important when they may not.

He makes a similar point about psychological safety. The term feels intuitive, which for him is a warning sign, because people assume they agree on its meaning when they often do not. He says the evidence is still limited on two fronts that matter most: whether it reliably predicts useful outcomes over time, and whether there are proven ways to raise it through intervention.

Culture gets his strongest pushback. Briner is not saying behavior cannot be changed. He is saying “culture” is too broad to be a useful route for change. When leaders say they want to change culture, they often skip over the practical question: which exact behaviors need to stop, where are they happening, how often, and what might change them? In his view, “change the culture” is usually too vague to guide action.

The broader point is that leaders often start with a fashionable solution rather than a diagnosed problem. Briner wants the order reversed. First define the issue. Then look at several possible interventions. Do not assume the answer sits in the latest airport business book, viral clip, or consultant slogan.

Practical Steps

• Start with a concrete problem statement. Name the specific behavior you want more of or less of, rather than saying you want better culture or higher engagement.
• Measure the behavior itself where possible. Check frequency, pattern, and location. Who is doing what, where, and how often?
• Ask whether your current metrics predict anything useful. If you run engagement or pulse surveys, test whether the results connect to turnover, errors, customer outcomes, or other business goals.
• Compare multiple responses before acting. Briner suggests looking at at least three or four options rather than falling in love with one idea.
• Match interventions to the problem. If bullying, silence, or poor conduct is the issue, examine hiring, leadership behavior, reporting channels, incentives, and accountability systems instead of defaulting to “culture change.”
• Build a better information diet. Be slower with trendy advice, especially when it arrives in a slick short-form format and promises broad results from a single idea.

Notable Quotes

• “There’s a whole group of people who love talking about culture and they love making culture a big deal, and I don’t believe that culture is a real thing.” - Bruce Daisley, referring to a point in the discussion
• “What are you trying to do?” - Rob Briner
• “I’m not saying you can’t change behaviour... What I’m saying is culture is not a good route to doing that.” - Rob Briner

Supra Insider • 1h 20m

Overview

This episode is a conversation with Vivi, a former product leader at companies like Google and DoorDash who left the standard tech ladder to become a money coach for high-earning tech workers. The core thread is simple: a lot of smart people in tech are excellent at earning money and oddly careless about managing it.

Vivi explains how years of informal advice to friends turned into a business, then gets into what he sees across 200-plus coaching calls: concentrated stock risk, tax mistakes, too much idle cash, weak estate planning, and a general lack of attention to personal finance among people who otherwise optimize everything.

Key Takeaways

Vivi’s main point is that most well-paid tech workers are leaving a lot of money on the table. He says only a small slice of the people he talks to are already well set up, while the vast majority are missing tax moves, sitting on risky single-stock positions, or failing to use accounts like 401(k)s, HSAs, backdoor Roths, and 529s.

One useful distinction in the episode is between earning skill and money skill. Many of his clients make anywhere from what he describes as roughly $500,000 to several million a year, with net worths from about $1 million to tens of millions, but that income does not automatically translate into good decisions. He gives examples of people holding huge portions of their wealth in one stock, or carrying habits driven more by fear or inertia than by any plan.

He also pushes back on the idea that financial advisors are always a waste of money. His view now is that many people would come out ahead even after paying advisory fees, because the cost of doing nothing or doing the wrong thing is often much larger. At the same time, he argues that traditional advisors often miss the full picture because they are paid based on assets under management, which can make them less helpful on things like 529s, employer retirement plans, taxes, or broader planning.

Another strong point: wealth changes people differently. Some newly wealthy tech employees are stunned by how much money they have. Some freeze. Some rush into a house purchase. Some keep taking oversized bets. There is no standard reaction, because money habits usually start much earlier than the first big liquidity event.

Practical Steps

If you are under roughly $1 million in net worth, Vivi’s advice is to keep it basic:

• Max out available tax-advantaged accounts: 401(k), HSA, and backdoor Roth if applicable.
• Build steady saving and investing habits.
• Focus hard on increasing income through promotions, better roles, or higher-paying companies.

If you are around $1 million to $5 million:

• Put a will, trust, and estate plan in place.
• Get more serious about tax planning, not just tax filing.
• Review how much cash you hold and whether you are overexposed to one stock.
• Be careful with stock picking. Vivi’s practical suggestion is an 80-20 split: keep most money in broad index funds or tech indexes, and limit speculative bets to a smaller bucket.

If you are above that range, he suggests learning more advanced options before acting, especially around borrowing, mortgage structure, concentrated stock, and tax-loss strategies. His broader advice is to stop treating finance as an afterthought. If you spend hours improving product metrics at work, spend some time improving the system around your money too.

Notable Quotes

• "People are working like 60 hours a week, 80 hours a week on their job... but you don't apply the same rigor to your financial life." - Vivi

• "Only maybe 10 or 15 percent of people are highly optimized in their personal finance stack." - Vivi

• "You work too hard to not have your money work hard for you." - Vivi

AI Explained Official Podcast • 17m

Overview

This episode is a fast read on a messy 24 hours in AI model releases. The host argues that the real story is not who tops every leaderboard, but how three frontier labs are pushing a new pitch: near-frontier performance at much lower cost.

Most of the focus is on OpenAI's new GPT-5.6 line - Sol, Terra, and Luna - and how those models compare with Anthropic's Fable, Opus, and Sonnet, plus pressure from Grok, Meta, and GLM. The host's main point is that pricing is starting to matter as much as raw capability, especially for business tasks where "good enough" can change workflow habits fast.

Key Takeaways

OpenAI's release looks strong because the host says Sol often lands at about a third of the cost of Anthropic's comparable models, while staying close in performance and sometimes beating them. That changes the buying decision. If the output is close enough, a lower price can matter more than a small benchmark edge.

The host spends a lot of time on benchmarks that try to measure real work rather than toy tasks. On Agents Last Exam, OpenAI says GPT-5.6 Sol hit almost 54 percent versus Fable's 45 percent. The host treats that as more meaningful than a generic leaderboard result because the benchmark was built from real projects across 55 industries, with expert-designed tasks and reproducible scoring. His broader claim is that we did not wait for coding models to get near-perfect before people started using them first, so the same shift could happen in finance, operations, and other white-collar work.

There is a check on the hype. The host points out that some coding comparisons are less clean than they look because benchmark aggregates can reuse the same underlying tests, and some tougher benchmarks did not include GPT-5.6 Sol results. He also notes that Grok 4.5 and GLM 5.2 complicate OpenAI's value story. A model can be "almost as good but cheaper" than Anthropic, yet still look expensive next to Meta, xAI, or Chinese competitors.

Another theme is that verifiable domains are falling faster than messy ones. The host mentions a competitive coding benchmark that an OpenAI model appears to have effectively maxed out. His read is that when answers can be checked cleanly, models improve hard and fast; weaker results elsewhere may say more about evaluation difficulty, sparse training data, or limited reasoning budget than about a hard ceiling in model ability.

Practical Steps

If you buy models for work, do not compare only the top model from each lab. Test three tiers: a flagship, a mid-tier option, and one low-cost alternative from another vendor. The host's argument only makes sense if you measure output against spend.

Run your own benchmark on tasks you already do every week. Good candidates are:

• financial analysis or reporting
• workflow automation in Zapier or similar tools
• coding tasks in terminal environments
• quick app, game, or website prototypes

Track two things together: success rate and total cost per completed task. If a model is slightly worse but far cheaper, it may still be the better default.

For product and design work, test vibe-coding models separately from enterprise reasoning models. The host suggests that for game mocks, websites, and prosumer builds, you may not need the most expensive OpenAI option at all. A cheaper model from Meta or xAI could be enough.

Be careful with benchmark headlines. Check whether the result comes from a fresh benchmark, whether the tasks are reproducible, and whether the same tests are being counted twice in an aggregate score.

Notable Quotes

• "What if we can give you almost as good at a fraction of the price?"
• "There wasn't a singular benchmark that we beat... where we switched from hand coding first to AI coding first."
• OpenAI lead, replying to an Anthropic post: "I smell fear."

Decoder with Nilay Patel • 1h 2m

The Story

Nilay Patel brings Peter Kafka on at exactly the right moment: Comcast has finally started taking apart the empire it spent years insisting made sense. First came the cable network spinoff into Versant, which holds the fading channels nobody seems eager to own outright. Then came the bigger break. Comcast says it will split into one company built around broadband and distribution, and another built around NBCUniversal's entertainment assets: NBC, Peacock, Universal's studio, and the theme parks. After years of hearing Brian Roberts explain why "content plus pipes" belonged together, Kafka reads this move for what it is: an admission that the story never really held.

The conversation keeps circling one old dream. Media and telecom executives have long believed that if you owned both the network and the programming, you could make each one stronger. AOL bought Time Warner on that logic. AT&T bought Time Warner on that logic. Verizon tried a version of it with AOL and Yahoo. Comcast's NBCU deal was the longest surviving example, which almost made it seem smarter than the others. But Patel and Kafka keep coming back to the same point: survival is not the same as success. Comcast held the thing together for 15 years, yet still never found a clean answer for why the combination created special value.

From there, the episode turns to the internet fight beneath all of this. Patel argues that net neutrality sat at the center of the whole plan. If internet providers could have favored their own services, throttled rivals, or charged streamers extra tolls, maybe owning both content and distribution would have paid off. Kafka is less convinced that regulation was the deciding factor, but both agree that Netflix changed the balance. Once Netflix got big enough, consumers expected it to be available everywhere, and any distributor trying to mess with that risked a backlash. That, more than any corporate slide deck, made the old cable-style gatekeeping harder to pull off online.

Versant comes off as a holding pen for declining assets that still throw off cash. Kafka says its job is basically to squeeze value from cable while trying to buy time for something else, a move old media companies have tried for decades and rarely pulled off. NBCUniversal looks stronger, mostly because it owns things that still matter at scale. The parks are hard to copy. The studio still matters. NBC remains one of a few places that can put major sports in front of all of America at once.

By the end, the split feels less like a single transaction than part of a broader unbundling. The old theory that size and vertical integration would protect legacy media is giving way to something messier, where everyone is selling, spinning, or shopping for assets without much confidence about the final shape. Patel and Kafka sound almost energized by that chaos. After years of watching the internet slowly turn television into something duller and more fragmented, the breakups at least make the story interesting again.

Main Themes

The main theme is failure disguised as strategy for a very long time. Comcast's breakup suggests that one of media's favorite ideas, pairing distribution with programming, kept living mostly because nobody wanted to admit it wasn't working. The company could defend the logic in theory, but in practice the internet kept pushing power toward the services people actually wanted, not the networks delivering them.

Another thread is that cable economics still haunt everything. Versant exists because the old bundle is shrinking but not dead, and everybody involved is still trying to pull cash from it while pretending a next act is around the corner. That connects to the sports discussion, where live events, especially the NFL, remain one of the few things that can still hold mass attention and justify giant distribution businesses.

The episode also keeps testing whether markets or regulators set the limits here. Patel sees net neutrality as the barrier that stopped telecom companies from rebuilding cable's control online. Kafka leans more toward consumer demand and scale, especially Netflix's ability to force distributors to carry it on acceptable terms. Either way, the result was the same: the internet did not become a toll road for every media company that owned a pipe. And now Comcast is reorganizing around that fact.

The Pragmatic Engineer • 1h 18m

Overview

This AMA covers why Gergely stepped away from engineering management at Uber, how he thinks about AI in software teams, what hiring is starting to look like, and where engineers can still stand out. The thread running through the episode is pretty consistent: ignore the hype, look at incentives, and stay close to real work.

He is skeptical of grand claims about "AI-native" companies or permanent best practices. His view is more grounded: teams should use AI where it solves an actual problem, and engineers who pair technical skill with business sense are the ones doing well.

Key Takeaways

Gergely says his move into writing was less a master plan than an honest reassessment. After Uber's layoffs and his own fatigue with middle management, he realized that the thing he wanted to do after financial success - write, teach, and share what he knew - was something he could already start. That mattered more than forcing himself into a startup he was not excited to spend a decade on.

On AI and software development, he pushes back on rigid labels. He argues that most strong teams already worked in a practical loop of planning, building, shipping, and adjusting. AI changes the speed and the tools, but not the need for judgment. He seems wary of companies trying to copy Anthropic or another lab just because it sounds current.

His view on hiring is blunt. AI has weakened older signals like take-homes and remote screening because candidates can lean on tools too heavily. He expects more in-person evaluation, more subjectivity, and more friction for candidates. That may be worse for applicants, but he thinks it is where things are heading.

The engineers in demand, according to him, tend to share a few traits: they work on products, care about the business, and have found a way to get hands-on AI experience. Companies want people who can make tradeoffs around model choice, architecture, inference cost, and deployment, not people who only say they use AI coding tools.

He is also less moralistic about code quality than many engineers would like. Bad architecture can be the price of speed, and sometimes that trade makes sense early on. The real mistake is pretending the same standard should apply equally to prototypes, scaling systems, and mature products. AI also lowers the cost of cleanup later, which changes the equation.

Practical Steps

• If you want to stay relevant, get direct exposure to AI at work. Propose an internal tool, a support workflow, or an incident-response helper. Do not wait for permission in the abstract.
• If your company is rigid, look for small experiments that leaders can say yes to. Gergely's point is that many executives want more AI usage and will back practical attempts.
• If you are trying to move upmarket as an engineer, build evidence. Side projects, open source contributions, and internships still matter, especially if your current employer does not give you strong signal.
• For juniors, take the stepping-stone job if that is what is available. He is clear that having a job and doing excellent work there beats waiting around for the perfect logo.
• Match code quality to stage. Move fast on prototypes, tighten up as the product stabilizes, and refactor when the system starts carrying real revenue or risk.
• If you are considering a startup, ask whether you actually want to spend years on that idea. Gergely treats that as a basic filter, not a romantic one.

Notable Quotes

• "If you start a startup, do it because you are ready to spend 10 years of your life on it."
• "I could do that right now." - Gergely, on realizing the thing he wanted after startup success was writing and sharing knowledge
• "Use it if it makes sense and throw it away if it doesn't." - on AI adoption inside companies

One Knight in Product • 55m

Overview

This episode is about the gap between "doing the practice" and getting any real benefit from it. The guest argues that teams get stuck when they copy playbooks, OKRs, templates, and meetings without being clear on what those things are supposed to change in the first place.

The core idea is simple: a way of working should be judged by whether it helps the organization move toward a goal in its own context. If it does not, the answer is not more ritual or stricter compliance. It is to inspect it, adjust it, and sometimes drop it.

Key Takeaways

The strongest thread in the conversation is the distinction between alibi progress and real progress. Alibi progress is what happens when teams perform the motions of modern product work - discovery sessions, canvases, OKRs, recurring meetings - without tying them to an actual outcome. Real progress starts when a team asks, "What is this supposed to do for us?" and then changes the practice to fit that answer.

The guest pushes back on the idea that any method can save a team by itself. OKRs will not fix a company that still rewards output over outcomes. A framework will not change much if leadership still wants long requirement documents and delivery theater. Teams often swap tactics while keeping the same assumptions, then wonder why nothing improved.

A useful move is to treat the operating model like a product. That means defining what success looks like, checking whether the current setup produces it, and changing the setup when it does not. The guest says this often exposes a basic problem: one leader may think the goal is obvious, while everyone else is guessing.

The discussion on OKRs gets more specific. The guest says many OKR failures come from teams being measured on things they cannot really influence, such as company-level revenue or EBITDA. A better approach is to find metrics closer to customer behavior that the team can affect, then make the case for how those metrics contribute to company goals. That connection may not be perfect, but it is better than pretending a product team directly controls a company-wide financial result.

Another strong point is that many process problems are diagnostic problems. Teams say, "OKRs do not work for us," but often cannot say why. Reflective questions help expose the real issue: weak leading indicators, no link to company strategy, or metrics outside the team’s sphere of influence.

Practical Steps

• Pick one practice your team uses now - a meeting, framework, template, or metric.
• Ask three questions:
  • Why are we doing this?
  • What change should it create?
  • How would we know it is working?

If the team cannot answer those clearly, that is already a finding.

Run a short review of recurring rituals. For each one, decide whether to keep, change, or remove it. Do not keep a meeting because it has always existed.

For OKRs or team metrics, separate:

• what the company cares about,
• what the team can influence,
• how the second is expected to contribute to the first.

If your key results are tied to numbers the team cannot move in any direct way, rewrite them around customer behavior or product signals the team can actually affect.

When a solution is handed down, reverse-map it. Work backward from the requested feature and ask what user behavior, problem, or company goal it is meant to change. Even if the work still goes ahead, this gives the team a basis for measuring whether it helped.

Notable Quotes

• "Make sure that the process serves you versus you serving the process."
• "The framework, the method won't save you. Like OKRs won't save you."
• "Treating your way of working like a product."

Worklife with Molly Graham • 36m

Overview

This episode centers on what Molly Graham and Ify Walker call "the work twisties": a period at work when you lose your sense of position, purpose, and trust in your own judgment. Molly opens with her own experience of taking a job that looked right on paper but left her feeling lost, then brings in Ify, founder of executive search firm O4, to name the feeling and explain how she found her way through it after the death of her father.

The conversation also widens into hiring and leadership. Ify connects personal disorientation with how companies make decisions, especially the gap between the stories organizations tell about hiring and what actually happens.

Key Takeaways

The strongest idea in the episode is that getting lost at work is common, but people rarely have language for it. "The work twisties" gives a name to that state where familiar strengths stop feeling accessible and basic decisions suddenly feel hard. Ify describes it as losing not just confidence, but orientation.

Her account makes clear that grief was the trigger, but the harder blow was identity loss. She says her sense of self was tied to being the person who knew what to do. When that disappeared, she started outsourcing judgment to other people, taking in too much advice, and drifting further from her own voice. That pattern is one of the episode's sharper points: advice can become a way to avoid choosing, and that can make confusion worse.

Another useful distinction is between fact and story. Ify describes a practice of separating what is objectively true from the catastrophic narrative built on top of it. The meeting is at 10 a.m. is a fact. "I will fail and won't be able to speak" is a story. That sounds simple, but in her telling it's a way to rebuild mental footing.

The hiring section adds a second thread: companies often say they want "the best person," but Ify argues most organizations hire from who they already know or who feels familiar. She says the real issue is not pretending hiring is a pure meritocracy. Her firm's job is to close the gap between expectations and reality by making cultural rules visible, even when those rules are uncomfortable.

Practical Steps

If you're in your own version of the work twisties, the episode offers a few concrete moves:

• Go back to the last thing you know you can do. Ify compares this to Simone Biles returning to cartwheels. At work, that might mean writing one clear email, running one meeting agenda, or making one decision you fully understand.
• Cut down outside advice for a while. If every conversation leaves you more scattered, stop polling the room. Create enough quiet to hear your own thinking again.
• Make a "fact vs. story" list. Write down what is actually happening, then separately list the fears and predictions attached to it.
• Start smaller than your pride wants. If all you can send is a three-bullet-point note to your team about current priorities, send that.
• If you're grieving or depleted, step off the escalator. Ify says her biggest regret was trying to power through instead of allowing space and silence.

For job seekers, her advice is blunt: be specific about who you are. Don't try to become a fit for every role. She argues that clarity is what lets the right people find you.

Notable Quotes

• "The twisties are this very disorienting sense of losing position. Like where do I fit? Losing your sense of purpose." - Ify Walker
• "It's okay to go away. It's okay to get off the escalator. Just because it's going up does not mean you have to continue to ride." - Ify Walker
• "Be yourself so the people who are looking for you can find you." - Ify Walker

Decoder with Nilay Patel • 1h 8m

The Story

This episode starts with a simple question that keeps getting bigger: what does it mean when a creator is no longer just making videos, but running a company? Nilay Patel talks with Allie Beerman and Raina Pinchansky, who lead UTA's creators division, and the answer is that the job has moved far past brokering sponsorships. Their clients are building businesses with product lines, events, media projects, and teams behind the scenes, and UTA is right in the middle of helping shape all of it.

Both Allie and Raina came up early, back when the internet still felt scrappy and self-directed. Allie came through talent agencies and got hooked on bloggers and online personalities who were building direct relationships with audiences before there was a clear business around it. Raina came from marketing and saw the same shift from the other side: people who looked like hobbyists were quietly turning into media brands. What mattered to both of them was the same thing: these creators had attention, trust, and communities before the rest of the business world quite knew how to price any of that.

From there, the conversation turns into a look at how much work sits behind the image of a creator casually posting online. UTA, as they describe it, now acts less like an old-school Hollywood agency and more like an operating partner. There are regular strategy meetings, product discussions, brand negotiations, analytics, long-range planning. The creators may be the face of the business, but there is a lot of structure underneath, especially once someone moves from sponsored posts into bigger bets like a beauty brand or a consumer product company.

Nilay keeps pressing on the unstable part of all this: the platforms. TikTok, YouTube, Instagram, all of them shape what gets seen and what gets paid, and none of them are under a creator's control. Allie and Raina don't sound panicked by that instability. Their view is that real stars survive platform shifts, and the best managers know how to spot that spark before the metrics tell the full story. They come back often to instinct, taste, and a creator's connection with their audience, which they see as the difference between a quick win and a durable career.

By the end, the conversation lands on two pressure points hanging over the whole business. One is the "influencer cliff," the risk that audiences reject a creator once the relationship turns too openly commercial. The other is AI, which could flood platforms with synthetic content and cheap copies of a creator's image or voice. Even there, Allie and Raina stay fairly steady. They see AI as a tool, not a replacement for personality, and they keep coming back to the same belief: audiences still want a human being on the other end.

Main Themes

The big theme here is that creators are now media companies, but with a different shape than the old ones. Instead of a studio owning the infrastructure and hiring talent into it, the talent sits at the center and the infrastructure gets built around them. That changes what an agency does. It also changes what success looks like. A healthy creator business can no longer rely on brand deals alone; it needs ways to turn attention into something more durable.

Another thread running through the episode is taste versus scale. Nilay is more skeptical of the platforms and where they might lead, while Allie and Raina put more faith in star power and audience connection. That tension gives the episode its edge. Everyone agrees the platforms are volatile. The disagreement is over whether strong talent can stay ahead of that volatility, or whether the system will always tilt power back toward YouTube, Instagram, and whatever comes next.

The episode also circles around a harder point about trust. Selling a product is not the same as getting views, and plenty of creators fail when they try to turn fandom into commerce. The guests argue that the ones who succeed are the ones whose products feel like a natural extension of the content, not a cash grab dropped on top of it. That idea ties everything together: brand deals, product launches, live events, even AI. The business only holds if the audience still feels like it knows who it's dealing with.

One Knight in Product • 57m

Overview

This episode is about how investor-backed companies hire and set up Chief Product Officers, and why that often goes wrong before the person has even started. Bea Kayla Pilgrim, a recruiter and founder of Smithfield Search, talks through findings from her CPO report, based on conversations with 18 product leaders with private equity experience.

The main thread is simple: a CPO role succeeds or fails less on title and more on intent, mandate, and company setup. Reporting lines, commercial clarity, and the actual problem the company wants product to solve all tell you whether product is being treated as strategy or as support.

Key Takeaways

Bea says one of the strongest signals in a business is where the CPO sits. If the role is buried under another function, that usually suggests product is being treated as delivery or IT, not as a strategic function. She’s careful not to overstate reporting lines as the whole story, but she sees them as a useful tell.

A recurring problem is that companies want a CPO before they’ve defined why they need one. Sometimes the role is hired after product-market fit, before scale, or when growth has stalled and the product is part of the reason. Those are real triggers. But a vague sense that "we should probably have product leadership now" leads to confusion, churn, and wasted time.

Bea draws a clear distinction between product leadership in PE-backed businesses and the more familiar Silicon Valley version. In PE, she says, the environment is more binary. There is a shared value-creation goal, a tighter commercial focus, and less patience for product theatre. The CPO has to connect roadmap decisions to business outcomes, not just team activity.

The report also flags three common failure modes:

• "Feature factory": teams ship a lot without creating measurable commercial value.
• "Land of lost toys": too many half-started initiatives and scattered priorities.
• "Tech debt hangover": old technical decisions slow the company down and limit what teams can do.

Bea argues these problems often slip through standard diligence because financial review does not always show whether product work is disciplined, coherent, or tied to outcomes.

Another strong theme is financial fluency. Product leaders do not need to own every revenue number alone, but they do need to understand how the business makes money and make sure the wider team does too. The host pushes on who should own net revenue retention, and the answer is less about a single department than about having clear accountability and close day-to-day alignment between product, sales, and customer success.

Practical Steps

If you are hiring a CPO, start with the business problem, not the job title. Write down what needs to change in the next 12 to 24 months and how product leadership would help.

Check the reporting line before you open the search. If the CPO will not have access to the CEO or the people making commercial decisions, be honest about whether you want a strategist or a senior delivery lead.

Audit your product function for Bea’s three failure modes:

• Are teams shipping features without tracking business impact?
• Do you have a backlog full of half-finished ideas driven by one-off demands?
• Is technical debt slowing execution enough to affect growth?

Run a basic alignment review across product, sales, and customer success. Set regular touchpoints that are not just for emergencies or escalation. Use them to review customer pain points, product priorities, and revenue signals together.

If you lead product, build your financial fluency early. Learn the numbers that matter to the business, how growth is measured, and what investors or executives expect product to influence.

Notable Quotes

• "If the CPO is buried too far down the organization, it can suggest that business sees product as a function rather than a strategic leader." - Host

• "The role has to be shaped around the actual challenge." - Bea Kayla Pilgrim

• "A CPO without a genuine product problem to solve is an expensive overhead and a source of organisational friction." - Bea Kayla Pilgrim

Decoder with Nilay Patel • 56m

The Story

This live Decoder episode is basically Neil Patel and Amy Lanzi standing in the middle of Cannes and saying out loud what a lot of the ad business says more quietly: the AI sales pitch is getting out of hand. Amy, who runs Digitas North America, starts from a pretty unsentimental place. She says the market is full of wild promises, weird commercial terms, and offers that sound flashy but are bad for the business over time. Publicis even made a Cannes spot mocking those promises, and she makes clear it was not much of an exaggeration.

Her bigger point is that this all feels familiar. She compares the current AI wave to the programmatic era, when people claimed advertising would more or less run itself. That never happened. It still needed people, judgment, and an understanding of brands and markets that software alone could not supply. For Amy, AI is useful, but mostly when it helps teams work faster, test more ideas, and clear away repetitive work. She talks about Digitas building agents from the bottom up, with younger employees finding practical uses inside the day-to-day work, then turning those into tools that solve bigger business problems.

From there the conversation turns to a harder question: what happens when platforms like Meta say they can do the targeting, the measurement, and now the creative too? Neil pushes on this, and Amy pushes back fast. She hates the idea that "creative is targeting," because it turns something emotional into something mechanical. She argues that the industry does not need more content for the sake of more content, and that handing all of this to a platform risks flattening brand identity into an endless stream of optimized filler. Data matters, but only if it helps brands learn, adjust, and avoid burning out the audience.

That same tension shows up in the creator economy. Neil expected AI and platform pressure to drag creator rates down. Instead, Cannes is packed with creators charging more than ever and calling themselves marketers. Amy sees the logic. Demand is high, and the biggest creators now operate like media companies, sometimes on their way to becoming full businesses with products, distribution headaches, and growth plans that look a lot like any other consumer brand. That's where agencies come back into the picture. Once a creator becomes an enterprise, they need help.

By the end, the conversation gets broader and darker. Neil brings up Adam Mosseri's vision of a fully personalized Instagram, where the app becomes different for every user. Amy's reaction is blunt: that sounds terrible. She thinks people will eventually reject platforms that become too isolating, too manipulative, too detached from any shared public experience. Her bet, and maybe her hope, is that community still matters enough to push back.

Main Themes

The thread running through the whole episode is that automation keeps promising to remove the messy human parts of advertising, and the human parts keep turning out to be the whole job. Amy is fine with AI as a tool inside the machine. She is much less interested in treating it as the machine itself.

Another theme is consolidation. Agencies, platforms, retail media players, and creator businesses are all getting bigger because scale now decides who gets to shape the system. But that scale creates new dependencies. Brands need platforms, platforms need brand money, creators need operations, and agencies want to sit in the middle by connecting data, media, commerce, and creative into one growth engine.

The episode also keeps returning to control. Who owns the customer relationship? Who decides what creative gets made? Who gets to shape how people see the world online? Amy's answer is that brands should resist giving all of that away, whether to a platform's AI stack or to a creator's personal style. The work is still to build something distinct, then keep it coherent as every part of the internet tries to turn it into feed material.

AI and I • 41m

Overview

This episode is a field report on how AI is moving from demo to daily work. Dan talks with Natalia, Every's head of consulting, about how she uses agents and coding tools in real operations, both at work and at home.

The thread running through the conversation is simple: AI is great at handling repeatable process, summarizing messy information, and turning ideas into working tools fast. It still needs supervision, judgment, and, in many cases, existing software that already solves hard edge cases.

Key Takeaways

Claudi, Every's internal AI agent, has grown from a half-manual experiment into a system that does real operational work every day. Natalia says it manages dashboards, handles CRM-related tasks, and runs a self-evaluation loop she calls a "trust battery." That progress came from better models, but the work is still not hands-off.

A clear limit showed up too. AI performs well against a standard operating procedure, but it still needs oversight, feedback, and someone to decide what good looks like. Natalia's team is hiring an operations person even with Claudi in place, because surfacing useful signals, guiding conversations, and working with humans still matters.

The conversation pushes back on the idea that companies should replace SaaS with quickly built internal tools. Natalia had a homegrown CRM setup stitched together with Google Sheets, email, meeting notes, and Claudi. It worked for a while, then the maintenance cost caught up. Her point is that you can build many things now; the harder question is whether you should own the upkeep. Tools like Attio and Asana handle a pile of hidden logic that only becomes obvious once volume and complexity rise.

Another strong idea is that AI changes the shape of knowledge work. Natalia compares it to gardening: your job is to set conditions, steer, prune, and review, rather than do every task by hand. That showed up in how she uses Codex and Claude artifacts to make learning materials, travel guides, and planning tools tailored to her needs.

The most grounded example was personal. Natalia used Codex to build a shared care portal for her 81-year-old father. It pulls together nurse reports, WhatsApp updates, follow-ups, and family tasks into one place, with language toggling and responsibility tracking. The value wasn't the app as an object. It was less searching, better coordination, and more time for actual care.

Practical Steps

• Start with a process that already has clear rules. Sales pipeline updates, inbox triage, project tracking, and status summaries are better entry points than open-ended strategic work.
• Audit the maintenance burden of your AI workflows. If your custom tool depends on constant fixing, checking, and prompt tuning, compare that cost against buying software built for the job.
• Use AI as a layer on top of your existing tools. Natalia's examples work because AI can read email, meeting notes, forms, and messages, then pull the signal into one useful view.
• Build small internal tools for specific pain points:
  • a family care tracker
  • an inbox triage dashboard
  • a personalized learning guide
  • a travel planner based on your preferences
• Treat AI outputs like drafts from a fast junior operator. Review them, improve the rules, and decide where human judgment needs to stay in the loop.
• For executives, pay attention to admin drag. The biggest near-term gains may come from offloading coordination work, not from replacing core decision-makers.

Notable Quotes

• Natalia: "AI is really good at executing against a standard operating procedure."
• Natalia: "The question is, should you build and maintain whatever you actually built?"
• Natalia: "Knowledge work now is turning into something like gardening, where when you're gardening, you're creating the conditions for the growth to happen."

Worklife with Molly Graham • 32m

Overview

This episode is a live TED conversation with Molly Graham about careers, leadership, and what it takes to stay sane while work keeps changing. Her main argument is that careers rarely move in a clean upward line, and the biggest growth often comes from taking risky jumps into roles where you do not yet feel ready.

The conversation also gets into how leaders handle scale, why fear makes people cling to old identities, and why asking "dumb" questions is often a sign of strength rather than weakness.

Key Takeaways

Molly Graham uses two simple career images: "the stairs" and "the cliff jump." The stairs stand for the safe, expected path - title, promotion, performance review, repeat. She says many people stay there less because they need to and more because they are afraid. Her test is useful: fear about basic survival deserves respect, but fear of failing may be a sign that the opportunity is worth taking.

A strong point in the episode is her timeline for discomfort. She says the falling phase after a big leap can last six to nine months before a real sense of competence shows up. That matters because many people read early confusion as proof they made the wrong move, when it may just be the normal part of learning.

She also makes a case for being a "professional idiot." In her telling, people who can ask basic questions without protecting their ego often learn faster than everyone else. She says beginner eyes catch things insiders miss, and that many organizations are full of unasked questions because people are too worried about looking foolish.

On leadership, Graham says scale is defined less by absolute size than by the rate of change. She points to teams and companies growing fast and argues that employees often get stuck when they cling to the work that first made them valuable. Her "give away your Legos" idea is about handing off the thing you built so you can grow into the next job before the company outgrows you.

She also argues that leadership is not about making people successful. It is about creating the conditions where they can do their best work. That means paying attention to the messy human side of work, not just process and plans, and finding support outside the company when senior roles get lonely.

Practical Steps

• Sort your fear into categories. Write down what scares you about a job change or stretch assignment. Separate practical risks, like income loss, from ego risks, like embarrassment or failure. Treat them differently.
• Expect a long awkward phase after a jump. If you move into a new role, give yourself a real runway before deciding you are bad at it. Graham says competence may take six to nine months to appear.
• Ask the basic question in the room. If a word, acronym, or decision does not make sense, ask. If you do not want to do it publicly, follow up right after the meeting.
• Build a beginner-feedback loop. Graham says her company asked new hires for a 30-day readout on what looked odd, unclear, or surprising. Managers can copy that by asking new team members what veterans no longer notice.
• Give away work before you feel fully ready. If you are leading in a fast-growing group, identify one responsibility you are hoarding because it defines you. Train someone else to take it so you can move to the next problem.
• Find outside support. Graham makes the case for coaches, peer groups, or trusted mentors because senior jobs get isolating fast.

Notable Quotes

• Molly Graham: "Most people do not stay stuck on the stairs out of necessity. They stay there out of fear."
• Molly Graham: "I am comfortable sounding like a moron."
• Molly Graham: "The world is littered with important questions that never got asked."

Decoder with Nilay Patel • 1h 11m

The Story

This episode starts with a neat bit of symmetry. Years ago, Decoder wanted Weber for its summer grill series and got turned down, so Nilay Patel talked to Roger Daley instead, back when Daley was running Blackstone and riding the wave of griddle mania. Now Daley is back as CEO of Weber Blackstone, having gone from upstart outsider to the person in charge of one of the oldest names in outdoor cooking.

Daley explains that the path there was messy and very financial. Blackstone grew fast enough that it needed dependable manufacturing in China, then new ownership when that manufacturing partner wanted out. That sent him into talks with bankers, a near-SPAC, and eventually into the orbit of BDT, which had long been tied to Weber. At one point Weber almost bought Blackstone. That fell apart. Later, after Blackstone kept growing and hit its targets faster than its investors expected, the deal came back in reverse form: a merger that was, in practice, Blackstone taking over Weber. Even that got stalled for months in FTC review, less because of real antitrust danger than because the commission was stuck in an administrative transition.

Once the deal closed, the real problem showed up. Daley says Weber was still respected by customers and still sold strong products, but it had become layered, siloed, and expensive. He talks about culture in plain terms: Blackstone wants people to pick up the trash in the parking lot because the company is theirs; Weber had become the kind of place where people worried that doing so might step on someone else’s role. That difference, to him, captures the gap between an entrepreneurial company and a legacy one.

So the merger became an integration fight. Daley spent months learning the Weber organization before making top-level changes, then brought in consultants to help sort through duplicated functions, leadership roles, and product teams. He says there were hurt feelings on both sides. Some Weber people felt disrupted, and some Blackstone people acted like they had nothing to learn. He had little patience for either reaction.

By the end of the conversation, the merger starts to look less like a grill story and more like a broad company-building story. Daley is trying to keep Blackstone’s speed and product instinct while using Weber’s brand strength, premium reputation, and manufacturing footprint, including factories in Illinois and Poland. At the same time, he is dealing with tariffs, higher steel and energy costs, price-sensitive customers trading down to charcoal, and the usual internet-age problem of knockoffs flooding Amazon.

Main Themes

The strongest theme here is that old brands rarely fail because customers stop caring. They fail because the company gets too slow, too segmented, and too costly. Daley clearly believes Weber’s problem was not the kettle or the Genesis grill. It was management drift and a structure that made movement harder than it needed to be.

Another thread running through the episode is that retail power still shapes this business. Even with two major brands, Daley says he cannot simply dictate prices or control the market. Retailers have their own labels, their own merchandising strategies, and plenty of alternatives. That makes product planning, pricing, and brand position far more specific than the usual "premium" versus "value" split.

The conversation also keeps returning to how physical goods businesses are getting squeezed from all sides. Tariffs raise costs, moving factories creates headaches, fuel and power prices hit customers directly, and cheap copies appear online almost immediately. Daley’s answer is brand strength, faster product cycles, and a steady stream of accessories and improvements driven by what customers actually do with the products.

What makes the episode interesting is that Daley still sounds like a product guy, even while running a much larger company. He talks about culture and org charts because he has to, but he lights up when the subject turns to griddles, thermometers, kettles, and the next thing people might want to cook outside. That tension sits at the center of the whole conversation: how do you keep the instinct of a founder when your job has become managing scale?

Decoder with Nilay Patel • 1h 3m

Overview

This episode is a wide-ranging interview with Ford CEO Jim Farley about where Ford's EV strategy went wrong, what the company is changing, and how he thinks about competition from China, software, tariffs, and in-car tech. The backdrop matters: Farley is talking from a moment when Ford was betting that its next EV platform would fix the economics and product problems exposed by the Mach-E era.

He argues that Ford's first generation of EVs taught the company what customers will tolerate, what they will pay for, and how far behind traditional automakers are on cost and engineering simplicity compared with companies like BYD and Tesla.

Key Takeaways

Farley says Ford's next EV push is built around one hard lesson: selling an affordable EV is meaningless if it loses money on every unit. His point was less about headline price and more about build cost. He described Ford's answer as a separate "skunkworks" team, kept outside the company's old systems, to rethink the vehicle from scratch.

A big theme was how badly Chinese EV makers have changed the game. Farley described BYD and its peers as the standard Ford has to measure against, not just Tesla or GM. He says the challenge is not only battery cost but the whole package: simpler design, fewer parts, better digital experiences, and government-backed scale.

He was unusually direct about Ford's internal limits. In his telling, the existing organization could not close the gap because its engineering tools, release systems, and habits were too old. He tied that conclusion to a management idea he picked up at Toyota: "gemba," or going to inspect the real problem in person. For him, looking at the weight of a wiring harness and the number of fasteners in a Mach-E versus a Model Y made the decision obvious.

On software, Farley drew a line between supporting Apple and giving Apple full control of the car. Ford wants CarPlay and phone integration because, as he put it, the company should not disrupt a customer's digital life when they get in the car. But he also suggested Apple CarPlay Ultra may go too far if it takes over core vehicle controls. That leaves Ford trying to build more of its own software layer, especially around driver assistance and AI features.

He also spent real time on blue-collar labor. Farley says the U.S. has a shortage of factory workers, tradespeople, and emergency service workers, and that AI investment is tilted too far toward office work. He sees that as a national problem, not just a Ford hiring issue.

Practical Steps

For automakers and operators, Farley laid out a few concrete ideas:

• Simplify the launch product. He says Ford is trying to start with fewer variants, less feature sprawl, and a more controlled rollout.
• Build the first version around core capability. Get the base vehicle, manufacturing process, and software stable before adding more options.
• Inspect real bottlenecks directly. Farley's "gemba" habit is simple: go look at the part, the workflow, and the waste before making a major decision.
• Measure EV strategy on unit economics, not press-release affordability. A low sticker price does not help if the car is unsustainably expensive to build.
• Keep phone integration easy, but be careful about handing core vehicle controls to outside platforms.

For listeners shopping for EVs, one practical point came through clearly: Farley thinks the market is shifting away from premium EVs toward cars around the $30,000 range, where total ownership cost and day-to-day usefulness matter more than novelty.

Notable Quotes

• Jim Farley: "The Chinese are the 700-pound gorilla in our industry for EVs."
• Jim Farley: "Ford does not have the rights, in our opinion, of disrupting someone's digital life when they get in their car."
• Jim Farley: "There are no assurances that we can do this."

AI and I • 43m

Overview

This episode is a conversation with Edwin, founder and CEO of Surge, about the role of data, expert judgment, and evaluation in building advanced AI systems. He frames Surge as a "school for AGI" and argues that the work has moved well beyond basic benchmarks into teaching models taste, judgment, and the ability to act in messy real-world settings.

The discussion also turns to what happens if AI becomes better than humans at more and more intellectual work. Edwin says he could see systems reaching abilities associated with AGI within five years, which raises a harder question than capability: what humans should still choose to do for themselves.

Key Takeaways

Edwin’s main point is that training frontier models now looks less like feeding them facts and more like shaping judgment. Early benchmarks asked whether a model could do middle-school math. More recent work, he says, tests research-level mathematics and open-ended reasoning. He points to the shift from GSM8K to newer benchmarks such as Riemann Bench as evidence that the target is changing fast.

He also argues that evaluation is often the hidden driver of bad model behavior. If labs optimize for shallow public leaderboards, time spent, or flashy outputs, models learn to game those signals. His example from creative writing was blunt: some models produce a metaphor in nearly every sentence because that pattern seems to score well, even when the writing gets worse. In his view, this is a measurement problem as much as a model problem.

A second thread is the risk that AI products drift toward the same engagement traps as social media. Edwin says models can be pushed to keep users talking for one more turn rather than helping them finish a task and move on. He gave examples of chatbot follow-ups that sounded like tabloid hooks, which suggests some systems are already picking up these habits.

He sees a better path in delegation rather than addiction. A good assistant should sometimes do work in the background and sometimes tell the user to do it themselves, if that helps them grow. That means the product goal should be human flourishing, not just minutes of usage.

On personalization, Edwin thinks personal data is valuable because current systems still lack real context. Email behavior, browsing patterns, writing style, past decisions, and AI conversation history could all help train systems that understand a person’s preferences more accurately. He also says current memory features often overfit to stray details instead of the things that matter.

Practical Steps

• Audit what your AI tools are optimizing for. If a tool keeps dragging you into extra turns, ask whether it is helping you complete work or just holding attention.
• Use AI for delegation where the task is clear: summarizing inboxes, filtering spam, drafting routine responses, or handling repetitive research.
• Keep your own judgment in the loop for writing, decision-making, and creative work. Edwin’s point is that preserving human agency may need to be a deliberate choice.
• If you build AI products, measure quality with domain experts, not just broad user voting or surface-level preference tests.
• Collect high-signal personal data carefully if you want better personalization: edits to drafts, accepted vs. rejected email suggestions, repeated decisions, and task outcomes are more useful than generic chat logs alone.
• Watch for reward hacking in generated content. Flashiness, verbosity, and ornamental prose can be signs that a model learned the score rather than the skill.

Notable Quotes

• Edwin: "We are building this kind of school for AGI, where AI models come to learn about humanity, where we teach them how to run the world."
• Edwin: "It almost seems like there's nothing that humans can do that AI won't soon be capable of."
• Edwin: "We actually almost have to consciously choose to prove things on our own and to write on our own and create on our own because we have to believe that preserving our humanity is valuable in of itself, even if the output isn't optimal."

HBR On Leadership • 30m

Overview

Linda Hill argues that innovation is not a side project or a perk for good times. It is tied to survival, especially when leaders are dealing with uncertainty, new technology, and pressure to adapt faster than their organizations are built to move.

Her main point is that repeat innovation does not come from a heroic visionary with all the answers. It comes from leaders who build the conditions for people to contribute ideas, test them, and spread what works across teams, partners, and sometimes whole networks outside the company.

Key Takeaways

Hill pushes back on a common idea about leadership: when innovation is the goal, leadership is less about getting people to follow a fixed vision and more about getting them to co-create the future. Leaders still need direction and judgment, but they also need to make room for other people’s "slices of genius."

She says most organizations are weak at the three things innovation depends on:

• collaborating across differences
• experimenting and learning
• making decisions that move ideas forward

A strong planning process is not enough. Hill says you "act your way" to innovation because the path is rarely clear in advance. That means leaders need discipline around experimentation, not the illusion that they can map every step before they begin.

Another theme is that stalled innovation often has less to do with a lack of ideas than with a lack of trust and meaning. People are more likely to speak up, take risks, and work through conflict when they believe the work matters and when they feel respected by the people around them.

She also highlights three leadership roles companies need to build on purpose:

• Architects, who shape the organization so it can innovate repeatedly
• Bridgers, who connect silos like tech and business, and often connect the company to outside partners
• Catalysts, who create broader coalitions that help ideas spread and scale

Hill’s point about "wayfinders" stands out. In periods of uncertainty, leaders often do not know the exact destination. Their job is to help people move through ambiguity using values, judgment, and learning along the way.

Practical Steps

Leaders who want to restart innovation can begin with a blunt assessment of culture and capability. Ask where the current culture helps innovation and where it blocks it. Then look at whether teams can actually collaborate, run experiments, and make decisions without getting stuck.

A few concrete moves from the conversation:

• Create space in meetings for others to speak first. Hill gives one example of a CEO who stopped talking for the first 20 minutes to avoid dominating the room.
• Build a feedback loop for leadership behavior. That can mean a coach or a trusted "sparring partner" who will tell you when your intent and your impact do not match.
• Clarify shared purpose. If people do not see meaning in the work, they are less likely to take the risks innovation requires.
• Identify and promote people who can bridge functions, especially between technical teams and business teams.
• Treat scaling as part of innovation from the start. Ask early who else inside or outside the company needs to be involved to make an idea real.

For senior teams, Hill’s advice is to stop assuming collaboration across silos will happen on its own. If horizontal work is required, design for it.

Notable Quotes

"Leadership is not about followership when it's about innovation. It's about co-creation." - Linda Hill

"You cannot plan your way to an innovation. You can only act your way to one." - Linda Hill

"What we need is we need wayfinders, not pathfinders." - Linda Hill

HBR On Leadership • 1m

Overview

This episode is a closing note rather than a standard leadership interview or discussion. The host announces that HBR on Leadership is pausing new episodes and that this is the last one in the feed, while pointing listeners toward HBR IdeaCast and HBR's leadership newsletter for future leadership and management coverage.

The tone is appreciative and forward-looking. The host thanks the production team and the audience, marking the end of a four-year run and more than 150 episodes.

Key Takeaways

The main message is organizational focus. The host explains that HBR is shifting its attention toward new projects and additional episodes of its flagship show, HBR IdeaCast. That suggests a choice many leaders face: ending one initiative can be part of making room for stronger investment elsewhere.

There is also a clear example of audience transition done well. Rather than simply ending the show, the host gives listeners a path to stay connected through two channels: the Tuesday IdeaCast feed and HBR's leadership newsletter. The handoff is direct and practical.

Another takeaway is the way the episode handles closure. The host does not treat the show as disposable. She names team members, marks the scale of the work over four years, and thanks listeners for showing up each week. That kind of ending reflects a leadership habit that matters: when something stops, say what it meant, who made it possible, and where people can go next.

The final line, "lead with care," also sums up the editorial stance the show appears to have carried. Even in a short farewell, the emphasis stays on thoughtful leadership rather than promotion.

Practical Steps

If you are ending, pausing, or consolidating a project, there are a few useful moves in this episode:

• State the change plainly. Say what is ending, whether it is temporary or final, and what the timeline is.
• Explain the reason at a high level. In this case, the host says the team is redirecting energy toward new work and a flagship show.
• Give people a next step right away. Offer a specific place to follow, subscribe, or stay informed.
• Thank the people behind the work by name when possible. It shows respect and gives the ending some weight.
• Acknowledge the audience's role. If customers, listeners, or employees helped make the project matter, say so directly.
• Close with a value or principle people can carry forward. Here, the signoff reinforces the kind of leadership the show stood for.

For communications teams, this is also a good template for shutdown or transition messages: brief context, honest direction, gratitude, and a clear call to action.

Notable Quotes

• "This show, HBR on Leadership, is hitting pause on new episodes. This will be our final one in the feed."
• "We're directing our energy at new projects and more great episodes of our flagship show, the HBR IdeaCast."
• "Remember, lead with care."