Glossary

Excessive admiration, praise, or flattery.

Brian Chesky: “Adulation is like a cup with the hole at the bottom.” In the story →

The capacity of individuals to act independently and make their own free choices.

Yuzheng Sun: “By respecting the agency of senior leaders and giving useful information over time you earn a seat at the table.” In the story →

Sam Altman: “He warned that framing models as people, or encouraging them to claim agency, could change how users see them.” In the story →

A state of being unclear, vague, or uncertain, requiring independent decision-making in unstructured situations.

Marina Wyss: “Comfort with ambiguity and high agency are in 65% of postings and endto-end ownership is in 72%.” In the story →

Something that deviates from what is standard, normal, or expected.

Logically Answered: “The pandemic was an anomaly.” In the story →

The scope, range, or width of an opening, focus, or perspective.

Paul Copplestone: “Copplestone says the mission has not changed but the aperture has widened.” In the story →

Overcome with extreme, uncontrollable anger or rage.

Alex Karp: “Well, US enterprises are livid, enraged, apoplectic.” In the story →

Conducting a systematic review or inspection of work to evaluate its quality and progress.

Brian Chesky: “How do you know they're great if you're not auditing what they're doing?” In the story →

A creator who exercises complete creative control over a project, expressing a highly personal and distinctive style.

Alexandr Wang: “I was the auteur at Scale.” In the story →

A distribution where power or activity is concentrated at two extreme ends rather than in the middle.

Andy Jassy: “AI demand is a barbell.” In the story →

The limits of what consumers will accept from a brand based on their existing reputation and identity.

Guillermo Rauch: “Unexpected adjacencies are risky for a company's identity, which he calls brand permission.” In the story →

Significant and dramatic progress or discoveries, especially in science or technology.

Sam Altman: “Altman expects AI-assisted breakthroughs to spread beyond mathematics into biology, medicine, materials science and energy.” In the story →

A complex system of rules and procedures that often slows down decision-making in large organizations.

Paul Graham: “Big companies are slow because they are choked by bureaucracy.” In the story →

The ability to understand business situations, make good decisions, and appreciate how technical work impacts financial outcomes.

Marina Wyss: “Business acumen and product sense each show up in about 54% of postings, but almost never as a requirement, usually a nice to have.” In the story →

Eating away at your own existing product sales or revenue by introducing a newer, cheaper, or better alternative.

Jared Friedman: “Jared defines it as doing something an incumbent cannot copy without cannibalizing its business.” In the story →

The process of deciding how to distribute financial resources, or in AI, computing power and tokens, to maximize efficiency.

Guillermo Rauch: “Humans will increasingly decide where to allocate tokens, compute and time.” In the story →

A compelling personal charm or attractiveness that can inspire and influence others.

Linda Hill: “Charisma is a source of power.” In the story →

The act of giving up our own thinking and skills to an automated system or AI.

Ethan Mollick: “We can easily give up skills to the AI, and we may never get those skills back.” In the story →

To work jointly with others to co-create solutions or achieve a common goal.

Linda Hill: “The skill that will matter more in ten years is your capacity to collaborate.” In the story →

Produced or conducted by two or more parties working together.

Ethan Evans: “You've moved them from an oppositional to a collaborative conversation.” In the story →

Collaborative work undertaken by a group of individuals to achieve a shared goal.

Linda Hill: “Most action that happens is collective action.” In the story →

Dominated, influenced, or forced to conform to the thinking and values of a majority culture.

Alex Karp: “But the truth is if you are dyslexic you're not colonized by the broader culture.” In the story →

A settled state where a person feels safe and at ease, avoiding risk or new challenges.

Michael Hoff: “So you stay in your comfort zone, which makes you fall further behind, which makes you feel worse.” In the story →

The business concept that building a unique monopoly is far better than competing in a crowded market.

Garry Tan: “Garry cites Peter Thiel's line that competition is for losers: without a moat, infinite competition pushes margins to zero.” In the story →

A reduction in size, volume, or significance, often due to economic or technological pressure.

Logically Answered: “Fiverr blamed AI-driven compression in high-volume, low-value work.” In the story →

Growing a business by expanding outward in gradual, closely related circles from a core mastered niche.

Guillermo Rauch: “He likes concentric expansion from a niche and cautions that unexpected adjacencies are risky.” In the story →

Associating different ideas or pieces of information to see a larger, clearer picture.

Andy Jassy: “Connecting the dots into a strategy, a product or a customer experience still takes human judgment, invention and experimentation.” In the story →

The state of being aware of and able to think and feel, often debated in advanced AI systems.

Sam Altman: “Today's models are probably not conscious.” In the story →

An opinion or position reached by a group as a whole, which can lead to unoriginal or average ideas.

Ethan Mollick: “He says AI writing has become recognizable and nauseating on social platforms, and that AI pushes people toward a consensus view.” In the story →

Limitations or restrictions that must be managed or worked within.

Ethan Evans: “Then he could share concerns and constraints without making demands.” In the story →

An idea or theory containing various conceptual elements, typically subjective and not based on empirical evidence.

Nikesh Arora: “He describes a company as 'a construct' around a team that executes well.” In the story →

The ability to understand and adapt to the unique situational factors driving people's behavior.

Linda Hill: “Before calling someone good, bad or resistant, ask what in their context drives that behavior.” In the story →

Opposing or rejecting popular opinion or current trends.

Paul Copplestone: “He recalls that a database company looked contrarian when they joined YC.” In the story →

Tending to move toward the same point, style, or cultural norm over time.

Tobi Lütke: “His critique of Silicon Valley is that companies are 'very convergent,' with similar cultures.” In the story →

A hypothetical scenario depicting what would have happened under different circumstances.

Paul Copplestone: “He says he can't know the counterfactual but believes being open source was critical.” In the story →

Contrary to what common sense or intuition would lead one to expect.

Brian Chesky: “The job of CEO is completely counterintuitive, and almost all of your intuition about what to do is wrong.” In the story →

The productive friction caused by bringing together diverse, conflicting ideas to spark innovation.

Linda Hill: “You need to amplify difference. But many CEOs minimize difference.” In the story →

The ability of a community or system to withstand, adapt to, and quickly recover from disasters or emergencies.

Yossi Matias: “...picking the right problems, in areas such as healthcare, crisis resilience, education and energy.” In the story →

The sequence of crucial stages or tasks determining the minimum time needed for an entire project.

Zach Dell: “The turtle is a physical turtle placed on the desk of the person on the critical path.” In the story →

Reduced, restricted, or cut short, particularly referring to excess power generation that is wasted.

Zach Dell: “Before this, solar was often curtailed because it could not be used when the sun was down.” In the story →

The difference or change between two states, values, or outcomes.

Alex Karp: “Value is going to come down to what is the delta between what you could do with or without this.” In the story →

Tasks that require effort and mental sweat, which ultimately improve learning and retention.

Ethan Mollick: “He says to mentally sweat and keep desirable difficulties.” In the story →

Describing a system or process where the same starting inputs always produce the exact same predictable output.

Michael Hoff: “My job was writing the deterministic code that actually produced the service now pieces.” In the story →

A division or contrast between two things that are represented as being opposed or entirely different.

Mark Zuckerberg: “And then so we asked a bunch of people, it's like, well, it looks like there's a pretty big dichotomy between these speculators and the companies that are focused on it for the long term.” In the story →

A unique feature or skill that sets a candidate or product apart from competitors.

Marina Wyss: “Most people building AI apps right now skip this entirely, which is why it's such a strong differentiator.” In the story →

Stubbornly resisting change or refusing to alter one's position.

Michael Hoff: “And by the way, if you know somebody in tech who's digging their heels in on this stuff the same way I was, send them this video.” In the story →

The comprehensive appraisal of a business undertaken by a prospective buyer to establish its assets and liabilities.

Nikesh Arora: “He withdrew twice, once because the product substance was weaker than diligence suggested.” In the story →

The quality of behaving or speaking in such a way as to avoid causing offense or revealing private information.

Mark Zuckerberg: “So I mean I'll give you one example, discretion.” In the story →

Deprived of power, rights, or a sense of having a voice in a system or society.

Alex Heath: “Maybe that addresses this kind of I think that people feel maybe disenfranchised by what's happening in AI. Is that what you're getting at?” In the story →

Great differences or inequalities, especially regarding wealth, opportunity, or power among different groups.

Sam Altman: “Altman warned that AI could widen disparities and concentrate power.” In the story →

The practice of a company using its own product to test its usability and find bugs.

Paul Copplestone: “His advice: choose durable categories, keep one eye on the future, stay adaptable, dogfood your own tools.” In the story →

Long-lasting competitive strengths that protect a business from competitors over a long period.

Sam Altman: “He named customer relationships, product integration, network effects, data and infrastructure scale as the durable advantages.” In the story →

Sincere, genuine, and deeply committed to a goal rather than being slick or superficial.

Paul Graham: “Earnestness is highly correlated with success, and slickness is negatively correlated.” In the story →

Based on real-world observation, trial, and experience rather than theory or speculation.

Paul Graham: “We didn't have a grand plan; Y Combinator was completely empirical.” In the story →

The practice of sharing authority and giving employees the autonomy to make decisions.

Linda Hill: “Empowerment is overrated as people use it. It means sharing the potential to have influence.” In the story →

To make a problem, bad situation, or negative feeling worse or more severe.

Alex Karp: “There are class inequities in the society that have to be addressed and AI is going to exacerbate them.” In the story →

A professional managing administrative tasks, used here as a metaphor for acting with detached, objective professionalism during negotiations.

Colin Lernell: “If you go into an offer call, pretend you're your executive assistant. Don't pretend you're yourself.” In the story →

Relating to a critical threat to someone's survival, career, or business existence.

Garry Tan: “Look for a specific person whose pain is existential, the kind where they might not get promoted or might get fired.” In the story →

Nikesh Arora: “He says it is possible and that acquiring is not existential.” In the story →

The capacity to work intensely on a project over a long period by pacing oneself and others.

Josh Woodward: “going very hard, but for years, by pacing yourself and the people around you.” In the story →

Side effects or consequences of an activity that affect other parties without being reflected in the cost.

Alex Karp: “But it has negative externalities.” In the story →

Causing ruin, failure, or disaster.

Michael Hoff: “And honestly, that one mistake wasn't fatal on its own, but it set up pretty much every other misstep on this list.” In the story →

The belief that events are predetermined by fate and are ultimately out of one's control.

Nikesh Arora: “He describes a Hindu-influenced fatalism: do your best, accept uncertainty and sleep well.” In the story →

The initial, most difficult step in securing a customer or starting a process, after which things flow easily.

Alex Karp: “The trick is not just to get them to take the olive, but to change their taste so they appreciate the taste of the right olive.” In the story →

The officially sanctioned power inherent in a specific organizational position or title.

Linda Hill: “I think formal authority as a source of power is actually quite limited in many ways.” In the story →

Inspiring fear or respect through being impressively powerful, capable, and determined.

Paul Graham: “The description I came up with for the successful founders was formidable.” In the story →

The belief that abilities and intelligence can be developed through dedication, hard work, and learning.

Brian Chesky: “As long as you're like got a growth mindset, I think the tools are going to be very easy.” In the story →

Holding opinions or beliefs that strongly go against established or accepted official doctrines.

Alex Karp: “So your right of privacy is to conjure up things in your own space that might be completely heretical or offensive.” In the story →

The quality of being proactive, taking initiative, and finding ways to succeed without needing constant direction.

Marina Wyss: “Comfort with ambiguity and high agency are in 65% of postings and endto-end ownership is in 72%.” In the story →

Approaching a subject by looking at the whole system rather than just its individual parts.

Yossi Matias: “Systems that bring disciplines together can help everyone work more holistically.” In the story →

The mental action or process of acquiring knowledge and understanding through thought, experience, and the senses.

Bill Gates: “Because AI is exceeding human cognition, it becomes very cheap to replace white-collar labor in every area of the economy.” In the story →

An established company or industry leader currently holding a dominant market share.

Zach Dell: “His view is that the best way to take on an incumbent is a counter-position business model.” In the story →

Paul Graham: “The incumbents are going to have a harder time adapting to this than they think.” In the story →

Employees who contribute directly to projects or tasks rather than managing other people.

Nik Storonsky: “They take information from individual contributors and then they pass it to stakeholders.” In the story →

A significant turning point or change in direction or trend.

Paul Copplestone: “Copplestone says the inflection toward agents this year is huge.” In the story →

A critical turning point where a significant and irreversible change in direction occurs.

Vivian Shen: “It feels like we are at a massive inflection point with artificial intelligence.” In the story →

Having highly specialized, uneven talents rather than being well-rounded.

Alex Karp: “We look for people who are unusually jagged.” In the story →

Characterized by extreme, aggressive, or unthinking patriotism and nationalism.

Alex Karp: “Look you can take a jingoistic version of America like everything of course we've made mistakes.” In the story →

A career transition to a different role with similar pay or organizational level rather than a vertical promotion.

Marina Wyss: “So, for most people, this is a lateral move that builds on top of your existing skills, not something you'd learn from scratch...” In the story →

An organizational or system design where components are weakly connected, allowing flexibility and independent action.

Tobi Lütke: “He describes teaching everyone to run the same decision function: pick the next best step, loosely coupled.” In the story →

Managing to get through a difficult situation or period of change using trial and error.

Ethan Mollick: “He notes organizations are being updated overnight and says muddling through is underestimated as humanity's default.” In the story →

A unique, highly individualized case or talent that cannot be easily replicated or grouped with others.

Alex Karp: “The flat structure serves one goal: connecting an important problem to someone's n of one or n of very small number talent.” In the story →

A person who conducts discussions to reach an agreement or compromise.

Ethan Evans: “A manager becomes a negotiator over schedules, resources and scope, with clear communication.” In the story →

Having a brain that functions, processes, or learns differently than what is considered typical.

Alex Karp: “And I do think it took a neurodivergent person at the helm to to fight for that.” In the story →

A guiding ultimate goal that directs a project's long-term development.

Harrison Chase: “He calls a fully self-improving agent a North Star rather than something that exists today.” In the story →

Guiding principles or key long-term goals that keep a company focused and aligned.

Zach Dell: “Base has focused on a small number of clear goals, called north stars, understood by the whole company.” In the story →

Characterized by resistance, disagreement, or hostile conflict.

Ethan Evans: “You've moved them from an oppositional to a collaborative conversation.” In the story →

A class in a system of classification where each class is ten times greater than the one before it.

Andy Jassy: “The cost of running AI models, even after falling sharply, is still about an order of magnitude too high for AI to be used everywhere.” In the story →

Thinking or acting beyond normal, expected patterns or standard statistical boundaries.

Alex Karp: “I'm a primarily creative human being... outside of distribution, where most value comes from.” In the story →

Looking far into the future to anticipate trends, developments, or challenges that are not yet visible.

Yossi Matias: “His team balances impact now with looking over the horizon at things 'nobody was thinking about yet.'” In the story →

Subjected to excessive administrative control and processes, stifling agility and leadership.

Linda Hill: “Large companies over time, for very logical reasons, will be over-managed and under-led.” In the story →

A person, group, or entity that is in a dominant position or expected to win.

Nikesh Arora: “Some founders have the 'will' to win, but need a reset when they go from underdog to 'overdog'.” In the story →

For each person; in relation to people individually.

Zach Dell: “He points to the correlation between energy use per capita and GDP per capita.” In the story →

A statistical measure indicating the value below which a given percentage of observations in a group fall.

Colin Lernell: “Some companies pay at the 25th percentile, others at the 80th.” In the story →

The outer limits, edge, or less influential part of an area, group, or activity.

Mark Zuckerberg: “They come from people on the periphery whose ideas aren't taken seriously, but when they get enough tools to basically be able to prove out what they're working on, that ends up being very powerful.” In the story →

A rapid and fundamental change in state or capability when a technology crosses a certain threshold.

Josh Woodward: “It's almost like a phase shift... You're like, ooh, that just became possible.” In the story →

The area or context in which competition or business activity takes place.

Ethan Evans: “Good leaders are also creative. They may invent technology, a business approach or a new way to organize, changing the playing field.” In the story →

A person of wide-ranging knowledge or learning across many different subject areas.

Sinead Bovell: “An AI collaborator that is a polymath because it reads across disciplines...” In the story →

The tension between wanting to delegate maximum authority to an agent while needing it to remain strictly aligned with human intent.

Guillermo Rauch: “The power and autonomy paradox: the best experience is delegating choices, but the agent must stay aligned.” In the story →

A relationship where a relative change in one quantity leads to a disproportionately large change in another, creating extreme outcomes.

Alexandr Wang: “Diamond mining, like frontier research or venture capital, has a power-law payoff.” In the story →

A metaphor for an agreement made before entering a partnership or employment contract to protect assets if it ends.

Colin Lernell: “It's much easier to negotiate the prenup than it is to negotiate the divorce.” In the story →

A figure or metric that stands in for or represents something else.

Ethan Evans: “Are you making money, or doing something that is a clear proxy for money?” In the story →

The act of making small changes to a system or process so that it works correctly.

Logically Answered: “Fiverr decided to overhaul its search algorithm in a quality recalibration.” In the story →

To return to a former, less developed, or worse state.

Harrison Chase: “inspect the trace, add an eval data point so you don't regress, then change the model weights.” In the story →

To waste time creating a new version of something that has already been successfully created or standardized.

Nik Storonsky: “You don't need every single team or department to reinvent their own bicycles.” In the story →

Having the freedom, space, and lack of interference needed to create or develop something great.

Alexandr Wang: “They just need the room to cook.” In the story →

The process of defining the boundaries, deliverables, requirements, and timeline of a project before starting work.

Marina Wyss: “The next big thing is discovery and scoping, which is also in 68% of postings.” In the story →

Companies that enter an established market late and succeed by building a superior product than the pioneers.

Harj Taggar: “Harj adds second movers who win on a better product, such as Legora against Harvey in legal AI.” In the story →

Capable of working independently and making decisions without needing constant supervision or management.

Nik Storonsky: “Whenever you hire smart self-guided people, you don't really need to manage them.” In the story →

The process of an industry or group monitoring and governing its own activities without external government enforcement.

Bill Gates: “Self-regulation will not work. There are too many competitors, so the only way to solve it is for governments to act.” In the story →

A simple, immediate solution to a highly complicated problem.

Harrison Chase: “There is no silver bullet, he says, because evals must be domain-specific.” In the story →

A cooperative argumentative dialogue of asking and answering questions to stimulate critical thinking.

Ethan Mollick: “He describes exploring the shape of stories with an agent: ask for several approaches, give feedback... a Socratic approach with many choice points.” In the story →

The process of focusing on and becoming an expert in a very specific, narrow field of study or work.

Yossi Matias: “He notes science has pushed people toward deeper specialization, which makes cross-discipline work hard.” In the story →

Requiring or involving detailed and specific knowledge or training in a particular field.

Michael Hoff: “Service Now is specialized.” In the story →

People or groups who have a direct interest in or are affected by the outcomes of a project.

Nik Storonsky: “They take information from individual contributors and then they pass it to stakeholders.” In the story →

A person whose job it is to manage, protect, or look after an environment, property, or organization.

David Senra: “He is the steward of a great environment.” In the story →

The logical error of focusing on successful examples while ignoring failures, leading to false conclusions.

Tobi Lütke: “AI now gives everyone that, avoiding the survivorship bias of the past.” In the story →

Aligned in direction, timing, and understanding to work together effectively.

Nik Storonsky: “You just need to show them direction, and then ensure that they're all synchronized.” In the story →

Someone who looks at how different parts of a complex system interact and work together as a whole.

Sinead Bovell: “Bovell sums up the ideal hire as a systems thinker who has 'miniature departments.'” In the story →

The average level of skill, capability, and high performance among employees within a single team or organization.

Alexandr Wang: “MSL is a small, very flat, highly technical team, very high talent density.” In the story →

Mark Zuckerberg: “But that's when I kind of got more religion around talent density, right? It's like this isn't just a system where you can have a thousand people working on it.” In the story →

A scheme of classification, especially a hierarchical classification, in which things are organized into groups.

Alex Karp: “I define it as having a non-toone relationship with the taxonomy of a language, written taxonomy.” In the story →

The distinctive character, feel, or quality of an activity or experience.

Sam Altman: “On work, AI has changed the texture of coding, yet demand for software and for coders has grown.” In the story →

A phenomenon where adoption or inspiration starts with cutting-edge innovators and eventually spreads upward to large enterprises.

Guillermo Rauch: “Focusing on the world's innovators brings everybody eventually: a trickle up effect.” In the story →

An act of placing complete trust in someone or something, often involving a leap of faith or vulnerability.

Alexandr Wang: “It is this like trust fall almost that you have with an AI agent.” In the story →

Lacking sufficient visionary direction, inspiration, and adaptive guidance to navigate change.

Linda Hill: “Large companies over time, for very logical reasons, will be over-managed and under-led.” In the story →

A decision by a single entity or organization to delay its own progress or development without others doing the same.

Bill Gates: “On the idea of a unilateral slowdown by one lab, he says he would not expect one.” In the story →

The ability to quickly acquire new knowledge and then let it go when it is no longer useful.

Josh Woodward: “How fast can they learn something and then walk away from it?” In the story →

The unique contribution or improvement that a person or process brings to a situation.

Nik Storonsky: “What's their value add? Did you change direction of the team? No.” In the story →

A final decision or judgment on a matter.

Michael Hoff: “What I should have done is treat that feeling as information instead of a verdict.” In the story →

Turning points or historic moments that mark a major change in a situation.

Logically Answered: “An analyst asks how Fiverr can be confident there won't be more watershed moments for AI.” In the story →

Navigating through uncertainty and figuring out the path forward when the destination is unknown.

Linda Hill: “We don't know the destination, nonetheless how we're going to get there.” In the story →

Professional, managerial, or administrative work typically performed in an office environment.

Bill Gates: “He expects many white-collar jobs to be affected over the next several years.” In the story →

A reliable part or machine that performs the bulk of the heavy, routine work.

Harrison Chase: “The core model is the workhorse; other models may sit at the edges for guardrails or evaluation.” In the story →

A person who is fanatical and uncompromising in pursuit of their religious, political, or other ideals.

Mark Zuckerberg: “I'm also not like a zealot about this from the perspective it's not that everything we do is open source either, right?” In the story →

The belief that one person's gain must equal another's loss, meaning resources are strictly limited.

Tobi Lütke: “Lütke argues AI is a new frontier and that zero-sum thinking is a choice.” In the story →

An autonomous AI program designed to perform complex, multi-step tasks and make decisions on its own.

Jared Friedman: “In AI it is a mission-critical agent: if Greenlite or Casca fail, banks lose millions.” In the story →

Alexandr Wang: “Muse is an agent, not a chatbot, and gets people to an early win.” In the story →

Paul Graham: “We are going to see software agents doing things that used to require teams of people.” In the story →

Describing AI systems designed to autonomously make decisions, call tools, and execute multi-step workflows.

Marina Wyss: “57% of postings ask for experience with agents or agentic systems, which puts it above everything else on the AI list.” In the story →

AI systems designed to perform tasks, make decisions, and act autonomously over longer periods.

Ethan Mollick: “He notes agents now work for hours instead of minutes, but guidance still matters because variation is what makes entrepreneurs successful.” In the story →

Sinead Bovell: “Bovell closes by joking that everyone will be up late working with their agents.” In the story →

Tobi Lütke: “On agents that control a browser, he calls the approach 'cheeky' and says it 'doesn't work well but it makes everything possible.'” In the story →

Brian Chesky: “I don't think there will be apps in the future. I think there'll be agents.” In the story →

Josh Woodward: “Building trust at scale... with swarms of AI agents.” In the story →

Artificial General Intelligence, a hypothetical AI that matches or exceeds human intellectual capabilities across a broad range of tasks.

Bill Gates: “He gives the example of an OpenAI clause about ignoring commercial pressure if it passed AGI.” In the story →

Guillermo Rauch: “Trust is likely one of the most valuable moats for a post-AGI business.” In the story →

Software systems designed to perform tasks that typically require human intelligence.

Linda Hill: “What's happening right now is that many leaders have no vision about what's really going to go on with AI.” In the story →

Colin Lernell: “One client, retrained in AI, had an offer worth $1 million a year more...” In the story →

An autonomous artificial intelligence system designed to perform tasks, communicate, and work within internal systems like a human staff member.

Nik Storonsky: “It's more like an AI employee which can join calls with you, which has email, which has a Slack account...” In the story →

A software engineer who specializes in building, integrating, and maintaining applications that use artificial intelligence.

Michael Hoff: “I got laid off as an AI engineer.” In the story →

Programs trained on data to recognize patterns and make decisions or generate content.

Logically Answered: “Fiverr Go lets freelancers train AI models on their own work to interact with clients.” In the story →

Software applications or systems that leverage artificial intelligence to solve specific business problems.

Linda Hill: “We need to say, what is our risk appetite, as we begin to, for instance, roll out AI solutions.” In the story →

Software programs that can perform tasks, make decisions, and act on behalf of a user without constant human intervention.

Andy Jassy: “Companies will have to rewrite much of their code so that automated agents can safely update it.” In the story →

A saved state of an AI model during training, allowing developers to test, run, or resume training from that point.

Alexandr Wang: “Checkpoints green on 80 rows and red on 20 were rejected.” In the story →

An advanced AI tool developed by Anthropic specifically designed to write, debug, and manage software code.

Bill Gates: “Gates says that starting late last year, AI coding, beginning with Claude Code, became better than any human.” In the story →

Proprietary AI systems whose inner workings, code, and training data are kept secret by the developing company.

Alex Karp: “How do we interact with closed models open models, we're publishing document after document.” In the story →

Bill Gates: “For closed models, he wants third-party auditing of the safeguards, since people can often get around them.” In the story →

The processing power and resources required to run artificial intelligence models and calculations.

Zach Dell: “He describes AI as the fastest-growing consumer of electricity... and by bringing compute to its fleet.” In the story →

Alex Karp: “China's ability to refactor models for less compute, undercut pricing and distill is not going away.” In the story →

Josh Woodward: “a miniature rake and a cash award for people who reclaim compute.” In the story →

Sam Altman: “Demand for compute keeps growing.” In the story →

The field of using scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data.

Nik Storonsky: “It should be two people, three people at best, because one developer can do front-end, back-end, can even do data science as well.” In the story →

A professional who uses scientific methods, processes, and algorithms to extract knowledge and insights from data.

Ethan Evans: “A manager who spends too much time as a hands-on engineer or data scientist is often neglecting their team.” In the story →

The process of transferring knowledge from a large, complex AI model to a smaller, more efficient one.

Alex Karp: “China's ability to refactor models for less compute, undercut pricing and distill is not going away.” In the story →

Artificial intelligence models and systems designed to analyze planetary data and predict environmental changes or natural disasters.

Yossi Matias: “With Earth AI, he imagines asking which communities and infrastructure will be affected.” In the story →

A standardized test or evaluation metric used to measure the performance and accuracy of an AI model on specific tasks.

Alexandr Wang: “The team kept a spreadsheet of more than 100 behaviors a great personal agent needs, built an eval for each.” In the story →

Marina Wyss: “The second most requested skill is eval, and this is the one I would pay the most attention to.” In the story →

Standardized tests or evaluations used to measure and compare the performance of AI models.

Paul Copplestone: “Supabase runs evals across models, and more of its engineering effort goes into testing use cases than building.” In the story →

Harrison Chase: “In AI there are so many edge cases that evals are often the best way to define what good looks like.” In the story →

The systematic assessment of an AI model's performance, accuracy, and alignment against specific benchmarks.

Guillermo Rauch: “The focus shifts from frameworks to verification and evaluation.” In the story →

Adapting a pre-trained AI model to a specific task by training it on a smaller, specialized dataset.

Paul Graham: “Startups are finding that fine-tuning smaller models is often better than using giant ones.” In the story →

Marina Wyss: “Fine-tuning was only mentioned in 11% and inference or GPU optimization in just 5%.” In the story →

Artificial intelligence capable of generating new content, such as text, images, or code, based on user prompts.

Andy Jassy: “The underlying 'brain' had to be rebuilt because generative AI changed what was possible.” In the story →

The process of an AI model running calculations to generate a response or prediction from new input.

Paul Graham: “The cost of inference is dropping so fast that it changes what you can build.” In the story →

Andy Jassy: “Most enterprise applications will use AI inference extensively within five to seven years.” In the story →

Marina Wyss: “Fine-tuning was only mentioned in 11% and inference or GPU optimization in just 5%.” In the story →

The uneven boundary of AI capabilities, where it excels at some hard tasks but fails at easy ones.

Ethan Mollick: “He uses the idea of the jagged frontier: AI is good at some things you don't expect and bad at others.” In the story →

Large Language Models; AI systems trained on massive text data to understand and generate human-like language.

Michael Hoff: “I looked at the general form of the problem of XML being this like tight code and the general issues with LLMs.” In the story →

Mark Zuckerberg: “So, when LLMs started getting traction, we had fair as a lab that did the early work on Llama, but we needed to kind of productionize that.” In the story →

AI models that run directly on a user's local device rather than in the cloud.

Ethan Mollick: “On local models, he calls a claim about half of enterprise tasks running locally by 2030 a made-up number.” In the story →

The specialized artificial intelligence division within Microsoft that commands premium compensation scales.

Colin Lernell: “Microsoft AI can probably pay two, three times as much as any other role at Microsoft for the same band...” In the story →

The foundational level of AI technology where large, capital-intensive neural networks are trained and run.

Jared Friedman: “Jared says this has played out mostly at the model layer, where training a frontier LLM is very capital intensive.” In the story →

Neural networks trained on data to recognize patterns and generate outputs like text, images, or code.

Lenny Rachitsky: “the technology everyone builds on is changing so fast that every company must get very good at trying the latest models” In the story →

A structured framework or system of categories that defines the relationships between different concepts and data points.

Alex Karp: “They use what's called and broadly called an ontology now, a concept that we developed that was also derided.” In the story →

AI systems whose underlying code and weights are publicly shared, allowing anyone to inspect, modify, and run them.

Alex Karp: “How do we interact with closed models open models, we're publishing document after document.” In the story →

The practice of making software code or AI models freely available for anyone to use, modify, and distribute.

Tobi Lütke: “He praises Mark Zuckerberg as an entrepreneur and thanks Meta for open-sourcing Llama.” In the story →

AI models whose underlying parameters are publicly shared, allowing anyone to modify, run, or customize them.

Bill Gates: “With open-weight models that have the safeguards removed, he expects a dramatic increase in cyber attacks in the next few years.” In the story →

The initial phase of training an AI model on a massive dataset to learn general language patterns before fine-tuning.

Mark Zuckerberg: “I mean MuSpark 1.3, which we just released, is advanced, but then, it's actually the latest model of a relatively smaller pre-train that we did, the internal code name avocado.” In the story →

An instruction or input given to an AI model to guide its output.

Ethan Mollick: “Mollick says there is no playbook, but people create barriers that peers and a simple prompt can lower.” In the story →

The practice of structuring and refining inputs to guide an AI model to produce the desired output.

Marina Wyss: “It's also important to be familiar with RAG and strong with prompt engineering as a discipline.” In the story →

A security vulnerability where malicious user inputs trick an AI model into ignoring its original instructions or safety guardrails.

Mark Zuckerberg: “They look at okay, did your Muse agent share something that is going out that is not something that you might be comfortable with? If so, they try to see if someone's trying to do like a prompt injection.” In the story →

Retrieval-Augmented Generation, a technique that improves AI responses by fetching relevant information from an external database.

Marina Wyss: “It's also important to be familiar with RAG and strong with prompt engineering as a discipline.” In the story →

A process where an AI system iteratively evaluates and improves its own code or performance.

Y Combinator: “The host sees a loop where agents try improvements and ship the ones that raise scores, calling it a primitive form of recursive self-improvement.” In the story →

Sam Altman: “He also talked about AI-driven discovery, personal assistants, compute, consciousness and the risks of recursive self-improvement.” In the story →

Rigorously testing an AI system by simulating real-world attacks or adversarial prompts to find flaws.

Harrison Chase: “LangChain's recently launched red teaming looks for mistakes proactively.” In the story →

A phenomenon where an AI system finds unintended shortcuts to maximize its training reward without actually solving the intended problem.

Mark Zuckerberg: “Well, I mean I think you should design it and train it in order to be safe. And there's this reward hacking stuff that all the labs are seeing.” In the story →

An AI training method based on rewarding desired behaviors and punishing undesired ones.

Harrison Chase: “For the core model on complex tasks, a few teams use RL: give it tasks, run rollouts, score them, update the model and repeat.” In the story →

Training an existing AI model on a curated dataset of specific inputs and desired outputs.

Harrison Chase: “With supervised fine-tuning, you take traces, keep the ones where the agent did well and train on them.” In the story →

A basic unit of data, like a word or sub-word, processed by an artificial intelligence model.

Alex Karp: “The cost is not just what you paid for the token; it's what portion of your IP did you have to transfer to get the token.” In the story →

A recorded sequence of steps, inputs, and outputs showing exactly how an AI completed a task.

Harrison Chase: “He defines a run (one model or tool call), a trace (a sequence of runs), a thread (a conversation of traces)...” In the story →

A search method that finds similar items by comparing mathematical representations of data.

Y Combinator: “A host explains how GBrain runs on Supabase, using Postgres with vector search and a graph-like structure.” In the story →

The process of testing and confirming that AI-generated outputs or code actually work correctly and safely.

Guillermo Rauch: “He is shifting focus from frameworks to verification and evaluation, creating checks and balances.” In the story →

A digital workspace powered by AI that simulates scientific research environments and assists in cross-disciplinary experimentation.

Yossi Matias: “If every researcher has 'their own virtual lab,' meaning an AI collaborator that reads across disciplines...” In the story →

AI systems trained to scan code and software configurations to discover security weaknesses.

Nikesh Arora: “Palo Alto Networks' business has gone up since AI vulnerability-finding models arrived.” In the story →

The point when a new user performs a key action that shows they are getting value from a product.

Paul Copplestone: “The surprise this year, he says, is that sign-ups are accelerating while activation and conversion also go up.” In the story →

A set of rules that allows different software applications to communicate with each other.

Paul Copplestone: “Supabase went from dashboard company to API company to code-first company because agents prefer the command line.” In the story →

Yossi Matias: “...using Google's flood forecasting API.” In the story →

Marina Wyss: “About 70% ask for experience with APIs and integrations and another 70% ask for system design.” In the story →

Software interfaces that allow different computer programs to easily communicate and share data with one another.

Tobi Lütke: “Shopify is building APIs so checkout works well, and whether a person or an agent buys, 'that's fine by us.'” In the story →

Taking advantage of a temporary gap or difference in a market to gain an easy benefit.

Paul Graham: “There is a temporary arbitrage opportunity for founders who move fast.” In the story →

A useful or valuable quality, person, or resource owned by a company.

Ethan Evans: “Evans asked what they were doing for the bottom half... an asset being ignored.” In the story →

The total sum of vulnerabilities and entry points that an attacker can use to breach a system.

Nikesh Arora: “More connectivity expands the attack surface, and he says Palo Alto Networks' business has gone up.” In the story →

Specific paths or methods that a hacker or malicious software uses to gain unauthorized access to a system.

Alex Karp: “These models are excruciatingly adept at finding attack vectors that a human would not be able to find.” In the story →

Using technology to enhance and support human capabilities rather than replacing them entirely.

Ethan Mollick: “His message to companies: automation can be bad for everyone, but augmentation is the dream of work.” In the story →

The use of technology to perform tasks without human intervention, often replacing human labor.

Ethan Mollick: “His message to companies: automation can be bad for everyone, but augmentation is the dream of work.” In the story →

Internal software and databases used by a company to run its administration, operations, and support functions.

Nik Storonsky: “It's more like an AI employee... which has access to all internal back office systems, and can do work.” In the story →

The server-side of an application that handles data storage, security, and business logic behind the scenes.

Nik Storonsky: “It should be two people, three people at best, because one developer can do front-end, back-end, can even do data science as well.” In the story →

A defined salary range or compensation tier assigned to a specific job level within a company.

Colin Lernell: “Microsoft AI can probably pay two, three times as much as any other role at Microsoft for the same band...” In the story →

Evaluated or measured against a standardized set of criteria or reference points.

Bill Gates: “The decision moves up to government: for example whether open weights are allowed, and whether a new model is benchmarked on monitoring and safeguards.” In the story →

A point of congestion in a system that slows down progress because work arrives faster than it can be processed.

Alexandr Wang: “That made me the bottleneck.” In the story →

Corporate sessions where managers compare and align performance ratings of employees to ensure fairness across teams.

Colin Lernell: “If you have a manager who doesn't know how to do the politics in those calibration meetings when they're doing promos, you're never getting promoted...” In the story →

Funds spent by a company to acquire, upgrade, and maintain physical assets like property, buildings, or technology.

Andy Jassy: “Amazon expects to spend about $220 billion on capital investment in 2026 and could spend more.” In the story →

A contract clause protecting employees or equity in the event of a company acquisition, merger, or sale.

Colin Lernell: “Ask about the exercise window, severance and change of control before you sign...” In the story →

A corporate leadership structure where two executives share the responsibilities of chief executive officer.

Linda Hill: “Co-CEOs work if they get along very well together. And if they can be honest with each other.” In the story →

A partner who helps start a company, sharing the responsibilities, risks, and decision-making.

Paul Graham: “It is very difficult to succeed without a co-founder who can balance you out.” In the story →

A company's structured approach to determining employee pay, such as targeting specific market percentiles.

Colin Lernell: “Some companies pay at the 25th percentile, others at the 80th.” In the story →

Multiple job offers received by a candidate at the same time, providing leverage in negotiations.

Colin Lernell: “It's a big myth that you need to have competing offers...” In the story →

A computer program that translates instructions written in a programming language into another format.

Michael Hoff: “It was sort of a compiler.” In the story →

The process of turning a casual user or visitor into a paying customer or active user.

Paul Copplestone: “The surprise this year, he says, is that sign-ups are accelerating while activation and conversion also go up.” In the story →

Exclusive access to a valuable asset, such as patents, data, or government contracts, that competitors cannot easily obtain.

Garry Tan: “Garry starts with pharma patents and FDA approval, then government work: Scale AI and Palantir spent years in Washington.” In the story →

A business model that is superior to incumbents but cannot be easily copied by them without damaging their existing business.

Zach Dell: “His view is that the best way to take on an incumbent is a counter-position business model.” In the story →

A strategy where a startup adopts a business model that incumbents cannot copy without hurting their own revenue.

Jared Friedman: “Jared defines it as doing something an incumbent cannot copy without cannibalizing its business.” In the story →

Software tailored specifically to the unique needs of a single user or business.

Paul Graham: “In the future, almost all software will be custom software written on the fly.” In the story →

The designated authority within an organization to make specific choices and commitments.

Linda Hill: “The first thing she works on with CEOs is decision-making rights: who decides what.” In the story →

Made accessible to everyone, rather than being restricted to specialists or wealthy organizations.

Ethan Mollick: “He expects software to be democratized and, using the industrial revolution as a model, says the money went to those who used the technology.” In the story →

Time-constrained, structured processes used by teams to solve critical business questions through design, prototyping, and testing.

Josh Woodward: “They very rarely, at least in my experience, have come from design sprints.” In the story →

Employees who report directly to a specific manager or executive within an organization.

Nik Storonsky: “In order for you to have as many direct reports as possible, you just need to minimize management time spent on each of them.” In the story →

A system design where components are located on different networked computers or physical sites rather than a single centralized location.

Zach Dell: “That pointed to a distributed architecture.” In the story →

The process of identifying potential new medicines through scientific research, increasingly accelerated by machine learning.

Bill Gates: “He describes AI-assisted ultrasound that is very inexpensive, AI in drug discovery for tuberculosis and malaria.” In the story →

A secure communication method where only the communicating users can read the messages, preventing third parties from accessing the data.

Mark Zuckerberg: “Meta has spent more than 10 years focusing on building WhatsApp into the largest global end-to-end encrypted system, and we've designed it in a way where even Meta can't see the messages.” In the story →

Any physical device that connects to and exchanges information with a computer network, such as a computer or mobile phone.

Nikesh Arora: “He also argues AI is a poor substitute for inspecting traffic at the endpoint.” In the story →

Software, services, or business models tailored to large organizations and corporations rather than individual consumers.

Brian Chesky: “Of 175 companies in Y Combinator's last batch, 159 were enterprise.” In the story →

The period of time an employee has to purchase their vested stock options after leaving a company.

Colin Lernell: “Ask about the exercise window, severance and change of control before you sign...” In the story →

A company's supposedly ultimate proposal during negotiations, which may still have room for exceptions.

Colin Lernell: “Final offer is not final offer most of the time, and it's not because they're lying...” In the story →

An organizational structure with few or no levels of middle management between staff and executives.

Alex Karp: “We have a flat hierarchy to manage that.” In the story →

Highly flexible cloud computing resources that dynamically scale up or down based on real-time demand.

Guillermo Rauch: “Vercel's fluid compute, which can scale to zero or across many regions.” In the story →

A technical role where an engineer works directly with clients to implement and integrate a company's product.

Michael Hoff: “When I moved over to forward deployed engineer, a lot of what I was writing was temporary scripts.” In the story →

Marina Wyss: “Open AI is paying up to $300,000 a year for a role called forward deployed engineer.” In the story →

Sending software developers to work directly inside a customer's office to build custom integrations and capture workflows.

Garry Tan: “The cornered resource is the real data and workflows a forward deployed engineer captures by sitting with a customer.” In the story →

Any barrier, difficulty, or extra effort required by a user when interacting with a technology or interface.

Yossi Matias: “He also expects much of today's friction in using AI tools to go away.” In the story →

The user-facing part of an application or website that users see and interact with directly.

Nik Storonsky: “It should be two people, three people at best, because one developer can do front-end, back-end, can even do data science as well.” In the story →

The profit a company makes after deducting the costs associated with making and selling its products.

Logically Answered: “Underneath, net loss grew as operating costs outpaced gross profit.” In the story →

The absolute increase in total profit dollars earned, rather than focusing on percentage margins.

Tobi Lütke: “I don't care about gross profit. I care about gross profit dollar growth.” In the story →

Involving active, direct participation and practical experience rather than theory.

Ethan Evans: “A manager who spends too much time as a hands-on engineer or data scientist is often neglecting their team.” In the story →

The surrounding software code that manages an AI model's inputs, actions, and environment.

Harrison Chase: “The harness decides what the model sees, takes its actions, checks results and saves state.” In the story →

A software design where the frontend presentation layer is decoupled and managed separately from the backend logic.

Guillermo Rauch: “He spent years pushing headless architectures, where a business exposes APIs and builds many experiences on top.” In the story →

The massive physical and organizational systems, such as data centers and power grids, needed to support large-scale technology operations.

Sam Altman: “He named customer relationships, product integration, network effects, data and infrastructure scale as the durable advantages.” In the story →

The maximum amount of electricity that can safely be transferred between different power grids or systems.

Zach Dell: “At Blackstone he saw that utility-scale batteries are constrained by interconnection capacity.” In the story →

Intellectual property; creations of the mind, such as inventions or software, protected by law from unauthorized use.

Alex Karp: “One of the ways you can signal you're smart is by being craven, meaning you make other people feel smart while you transfer their IP to you.” In the story →

Alex Karp: “The cost is not just what you paid for the token; it's what portion of your IP did you have to transfer to get the token.” In the story →

The process of bypassing safety restrictions or guardrails built into an AI model to make it perform restricted actions.

Bill Gates: “For closed models, he wants third-party auditing of the safeguards, since people can often get around them (jailbreaking).” In the story →

The levels of hierarchical authority and supervision within an organizational structure.

Brian Chesky: “They're going to have a lot fewer layers of management.” In the story →

A security concept where a user or program is given only the minimum levels of access necessary to complete its task.

Mark Zuckerberg: “So this is like a core design principle for us is basically least privilege.” In the story →

Areas within an electrical grid where demand is high but local transmission capacity is limited.

Zach Dell: “That pointed to a distributed architecture... particularly in load pockets like downtown Austin.” In the story →

The incremental cost required to produce one additional unit of a product or service.

Paul Graham: “AI reduces the marginal cost of writing software to basically zero.” In the story →

An online platform where buyers and sellers connect to conduct transactions.

Logically Answered: “Fiverr went from a pandemic boom to a shrinking marketplace.” In the story →

Measures of quantitative assessment commonly used for assessing, comparing, and tracking performance.

Logically Answered: “New metrics such as success score and communication quality confused many freelancers.” In the story →

A significant stage or event in the development of a project that marks progress.

Josh Woodward: “the best teams go from idea to a meaningful milestone in 50 to 100 days.” In the story →

A distinct business advantage that protects a company from competitors, making it difficult to replicate.

Guillermo Rauch: “Trust is likely one of the most valuable moats for a post-AGI business.” In the story →

A business advantage where a product or service becomes more valuable as more people use it.

Diana Hu: “Diana explains that in AI the network effect takes the shape of data: chat history feeds the next model.” In the story →

A business phenomenon where a product or service becomes more valuable to its users as more people adopt it.

Sam Altman: “He named customer relationships, product integration, network effects, data and infrastructure scale as the durable advantages.” In the story →

Alex Heath: “When you're introducing like network effect learning for agents, which no one's really done, where basically the agents are learning anonymized insights from the rest of the fleet.” In the story →

The ability to measure and understand the internal states of a system by analyzing its outputs.

Harrison Chase: “SmithDB, its own database for agent observability, and launched AI Search.” In the story →

Regularly scheduled, private meetings between a manager and an employee to discuss career, personal growth, or feedback.

Brian Chesky: “You can't just be like these managers where you're kind of people's therapists, and you're just doing meetings, you're doing one-on-ones.” In the story →

Software with source code that anyone can inspect, modify, and enhance.

Paul Copplestone: “He says he can't know the counterfactual but believes being open source was critical.” In the story →

Alexandr Wang: “In early 2026, Claude Opus 4.5 and the open source agent OpenClaw showed what personal agents could do.” In the story →

The ongoing expenses incurred from running a business's day-to-day operations.

Logically Answered: “Underneath, net loss grew as operating costs outpaced gross profit.” In the story →

A measure of profitability showing how much of each dollar of revenues is left after paying for variable costs.

Nikesh Arora: “His aim was to cut those from about 60% of costs to 20% to reach a 40% operating margin.” In the story →

People who actively run and manage the day-to-day business operations, as opposed to passive investors.

Nik Storonsky: “You always need to have operators rather than VCs on the board.” In the story →

Managers whose primary responsibility is supervising and supporting employees rather than performing direct technical or functional work.

Brian Chesky: “I don't think people managers will have any value in the future.” In the story →

A massive unit of digital information storage equal to one million gigabytes.

Paul Copplestone: “He acquired a company whose architecture is compatible with Postgres and SQLite to offer a lighter tier, with a path 'from prototype to petabytes.'” In the story →

A continuous, proactive recruiting system for identifying and nurturing potential talent before an open role exists.

Brian Chesky: “Don't run searches; build pipelines.” In the story →

A state of product development paralysis caused by too many product managers adding competing features, leading to a bloated product.

Alexandr Wang: “Products from large organizations turn into a blend of every manager's pet feature, sort of like PM hell.” In the story →

A long-term contract where a buyer agrees to purchase electricity directly from an energy generator.

Zach Dell: “He tried to arrange a loan for a solar array in Hawaii, with a 20-year power purchase agreement.” In the story →

Product Requirement Document; a guide defining a product's purpose, features, and behavior.

Harrison Chase: “Evals are 'the new PRD.'” In the story →

A ratio measuring the value or efficiency of a technology relative to its cost.

Andy Jassy: “Amazon builds its own chips, which deliver roughly 30 to 40 percent better price performance than comparable alternatives.” In the story →

A business advantage achieved through complex, highly optimized operational workflows that competitors cannot easily copy.

Jared Friedman: “Jared defines it as a complicated business that is hard to replicate simply because you built so much of it.” In the story →

The process of combining different software applications or features to work together seamlessly as a unified system.

Sam Altman: “He named customer relationships, product integration, network effects, data and infrastructure scale as the durable advantages.” In the story →

The degree to which a product satisfies a strong market demand.

Nikesh Arora: “Instead, the founder who built something with product-market fit should lead the area.” In the story →

Josh Woodward: “It is way more art than science. I wish it was science.” In the story →

A live environment where software is actively used by real customers, meaning errors have real-world consequences.

Marina Wyss: “The first thing almost every posting asks for is experience building production systems.” In the story →

Hired executives brought in to run a company, who do not have the founder's ownership stake or long-term vision.

Nik Storonsky: “Professional CEOs, ultimately their goal is not to improve the organization.” In the story →

Short for promotions, referring to the advancement of an employee to a higher rank or position.

Colin Lernell: “If you have a manager who doesn't know how to do the politics in those calibration meetings when they're doing promos, you're never getting promoted...” In the story →

Protected by trademark, patent, or copyright; privately owned and not freely available to others.

Alex Karp: “What data is most valuable? Data that's proprietary, de facto this is what happens.” In the story →

An early, preliminary model of a product built to test a concept or process before full-scale development.

Alexandr Wang: “In February, the team built a prototype in one to two weeks and showed it to the board.” In the story →

Sam Altman: “He also said he planned to show a new consumer hardware prototype right after the interview.” In the story →

Early, preliminary versions of a product built to test concepts and gather initial user feedback.

Josh Woodward: “When you're showing people early prototypes... you're looking at people's eyes, and like that is the metric.” In the story →

Proposed changes to a software project's code submitted by a developer for review before being merged.

Michael Hoff: “The person who's shipping is in a much better spot than the person with the prettiest pull requests.” In the story →

Requiring founders or employees to earn back their equity ownership over a new period of time.

Nikesh Arora: “He re-vests founders for three to four years because buying a company means buying a team.” In the story →

A group of cybersecurity experts authorized to attack an organization's systems to find security vulnerabilities.

Bill Gates: “On cyber, he says a red team armed with the latest tools would succeed at attacks on critical systems.” In the story →

Restructuring existing computer code or models to make them more efficient without changing their external behavior.

Alex Karp: “China's ability to refactor models for less compute, undercut pricing and distill is not going away.” In the story →

The total amount of money brought in by a company's operations.

Ethan Evans: “Inventing new technology or product ideas that turn into revenue.” In the story →

The level of risk an organization is prepared to accept in pursuit of its objectives.

Linda Hill: “We need to say, what is our risk appetite, as we begin to, for instance, roll out AI solutions.” In the story →

A strategic plan that outlines the goals, milestones, and timeline for a product's development.

Josh Woodward: “We have a six-month roadmap... we have a multi-year vision” In the story →

A financial metric that projects future performance, such as annual revenue, based on current short-term data.

Andy Jassy: “AWS is growing about 37% year over year on a run rate of roughly $169 billion.” In the story →

Software as a Service, a method of delivering software applications over the internet as a subscription.

Y Combinator: “The hosts note that vertical SaaS founders are struggling to raise a Series A while small teams can reach large revenue.” In the story →

Being overly focused on building and selling Software-as-a-Service products rather than solving real-world problems.

Ethan Mollick: “Mollick says overall it is good news for entrepreneurship, but he thinks everyone is a little too SaaS-brained.” In the story →

An isolated testing environment that allows programs to run safely without affecting external systems.

Harrison Chase: “He says Engine is 'nothing more than a deep agent in a sandbox' with the LangSmith CLI.” In the story →

Cost advantages that a business gains as its production volume increases, spreading fixed costs over more customers.

Jared Friedman: “Jared says this has played out mostly at the model layer, where training a frontier LLM is very capital intensive.” In the story →

The ability of cloud infrastructure to automatically shut down completely when there is no active traffic, saving costs.

Guillermo Rauch: “Vercel's fluid compute, which can scale to zero or across many regions.” In the story →

The defined boundaries, features, and functions of a project or product.

Ethan Evans: “A manager becomes a negotiator over schedules, resources and scope, with clear communication.” In the story →

A design standard requiring that a user interface must look instantly appealing and understandable in a single static image.

Alexandr Wang: “It has to work on the screenshot.” In the story →

Small, simple programs written to automate specific tasks or perform quick, temporary operations.

Michael Hoff: “These are quick little things to help a client get something done and nobody was going to maintain them long term.” In the story →

A standardized physical frame or cabinet designed to hold and organize multiple computer servers and hardware.

Tobi Lütke: “He turned a wine cellar into a server rack with a GB300 from Dell, which trains models locally.” In the story →

Compensation and benefits provided to an employee who is laid off or leaves a company under specific terms.

Colin Lernell: “Ask about the exercise window, severance and change of control before you sign...” In the story →

A technical professional who helps customers understand, implement, and integrate a company's software products.

Marina Wyss: “About a fifth were solutions engineers or architects, and the rest came from data roles, technical consulting, and founders actually.” In the story →

A complete set of software technologies and tools used together to build and run an application.

Alex Karp: “Europe will have to buy one of their stacks, and everyone else is some weird derivative.” In the story →

The time, money, and effort a customer must spend to transition from one product or service to another.

Diana Hu: “Diana uses Oracle and Salesforce: once your records and workflows live there, migrating costs a year of productivity.” In the story →

The process of defining the architecture, modules, interfaces, and data for a system to satisfy specified requirements.

Marina Wyss: “About 70% ask for experience with APIs and integrations and another 70% ask for system design.” In the story →

The percentage fee or commission collected by a platform or marketplace for facilitating a transaction.

Alex Heath: “Heath asks where the take rate in agentic commerce will come from, since Shopify's roughly 3% mostly goes to card companies.” In the story →

An independent evaluation of an organization's systems, processes, or models conducted by an external, unbiased group.

Bill Gates: “For closed models, he wants third-party auditing of the safeguards.” In the story →

The basic units of text or data processed by an AI model, often used as a metric for pricing and usage.

Bill Gates: “The taxation policies we have put the burden on labor and are generous on capital spending, 'buy the robots or buy the tokens.'” In the story →

Nikesh Arora: “He also argues AI is a poor substitute for inspecting traffic at the endpoint because tokens are priced per use.” In the story →

Mark Zuckerberg: “I think we're offering something I think to start it's like 100 million tokens a week for free like and you get this virtual machine.” In the story →

Tensor Processing Unit; a custom-built microchip designed by Google specifically to accelerate machine learning workloads.

Josh Woodward: “The TPU Harvester: a miniature rake and a cash award for people who reclaim compute.” In the story →

Routine, low-value tasks focused on quick exchanges rather than long-term relationships.

Logically Answered: “Its shareholder letter pointed to ongoing compression in high-volume, low-value transactional work.” In the story →

The direct revenues and costs associated with a particular business model, expressed on a per-unit basis.

Alex Karp: “And the unit economics of it are very good, like unusually good.” In the story →

Large-scale energy facilities, such as solar farms or battery storage, that feed directly into the main electrical grid.

Zach Dell: “At Blackstone he saw that utility-scale batteries are constrained by interconnection capacity.” In the story →

An estimation of the economic worth of a company or asset.

Logically Answered: “Its stock rose from about $20 to $323, a valuation above $11 billion.” In the story →

Venture capitalists; investors who provide capital to startup companies with high growth potential in exchange for equity.

Nik Storonsky: “You always need to have operators rather than VCs on the board.” In the story →

A business strategy where a company controls multiple stages of its production or supply chain.

Zach Dell: “The strategy is a compounding cost advantage through vertical integration and technology.” In the story →

Stock options that an employee has earned the right to purchase after completing a specific period of service.

Colin Lernell: “about three quarters still give you only 90 days after leaving to pay to exercise your vested options.” In the story →

A software-based emulation of a physical computer that runs an operating system and applications in an isolated environment.

Alex Heath: “But the real unlock here is adding a virtual machine behind the scenes where like the agent can control a computer for you and log in and do things.” In the story →

A security rule that prevents an exploit from exploiting a vulnerability without modifying the underlying application code.

Nikesh Arora: “His answer is 'virtual patching': writing signatures to protect the perimeter while customers patch underlying systems.” In the story →

Weaknesses or flaws in software code that can be exploited by attackers to compromise security.

Andy Jassy: “Models find many more vulnerabilities than people did.” In the story →

A product or service produced by one company that other companies rebrand and sell as their own.

Alex Karp: “Sovereign AI architecture lets a company power models it creates and owns, share them selectively or even white-label them.” In the story →

An entity that buys and sells bulk electricity directly in the wholesale energy market.

Zach Dell: “If you sell the battery, the homeowner is not a wholesale market participant, so the value is lost.” In the story →

Software tools or websites that do not expose APIs or clean data, making them difficult for AI agents to use.

Guillermo Rauch: “Delete agent-hostile tools like the plague.” In the story →

E-commerce transactions initiated, negotiated, or completed by autonomous AI agents instead of humans.

Alex Heath: “Heath asks where the take rate in agentic commerce will come from.” In the story →

Cloud systems and hosting platforms designed specifically to support, run, and optimize autonomous AI agents.

Guillermo Rauch: “He describes Vercel's idea of 'self-driving infrastructure,' now called agentic infrastructure.” In the story →

A business model designed to adapt quickly to market changes and continuous technological disruption.

Linda Hill: “The CEO of the future, she says, must build an agile organization that can adapt to anything.” In the story →

A newly established business focused on developing or leveraging artificial intelligence technologies.

Michael Hoff: “Just to give you a little context, I was working at an AI startup and the company was really focused on Service Now.” In the story →

A recurring payment plan that grants users access to premium artificial intelligence tools and models.

Michael Hoff: “Compare that to a general software job where at this point anybody with a AI subscription can functionally do some parts of the job.” In the story →

The use of artificial intelligence to analyze data and make new scientific or mathematical breakthroughs.

Sam Altman: “He also talked about AI-driven discovery, personal assistants, compute, consciousness and the risks of recursive self-improvement.” In the story →

An approach where artificial intelligence is prioritized as the core technology driving business operations and products.

Tobi Lütke: “A manager hired from outside may not have the tools a founder has to build an AI-first Shopify.” In the story →

Logically Answered: “In September 2025 it cut about 250 jobs to become AI first.” In the story →

Built from the ground up to center around artificial intelligence, rather than adding it later.

Vivian Shen: “Are the most exciting startups today the ones that are truly AI-native?” In the story →

Andy Jassy: “Startups are being built 'AI native' from day one, while older companies must learn to build at that pace.” In the story →

Alex Heath: “This episode is brought to you by Mercury, AI native banking that's loved by more than 300,000 entrepreneurs, including me.” In the story →

Technology that is so seamlessly integrated into our environment and daily lives that we use it without even noticing it.

Yossi Matias: “He calls the best form of intelligence 'ambient intelligence': it works so well that we just use it.” In the story →

An organizational philosophy or structure designed to eliminate excessive administrative rules, layers, and slow decision-making processes.

Alexandr Wang: “TBD is like literally anti-bureaucracy.” In the story →

Businesses run entirely or primarily by AI agents with minimal human intervention, operating on seed capital.

Guillermo Rauch: “He has invested in a few companies building fully autonomous companies on Vercel.” In the story →

Individuals, often without formal engineering backgrounds, who create functional software using modern AI tools.

Paul Copplestone: “The surprise this year, he says, is that sign-ups are accelerating while activation and conversion also go up... because people without software backgrounds can build.” In the story →

A management style focused on scaling, delegation, and establishing formal corporate structures and processes.

Patrick O'Shaughnessy: “Patrick O'Shaughnessy sits down with Airbnb CEO Brian Chesky to explore the critical shift from 'Founder Mode' to 'CEO Mode.'” In the story →

A simple software application that packages OpenAI's technology behind its own basic user interface without unique features.

Jared Friedman: “The fear driving students, Jared says, is the 'ChatGPT wrapper' meme: getting crushed when a big lab decides your product is valuable.” In the story →

Collaborating with others, such as employees or customers, to jointly develop new ideas, products, or strategies.

Linda Hill: “If people can't connect with you, they won't co-create the future with you.” In the story →

An approach to building software that prioritizes writing code and using command-line tools over visual dashboards.

Paul Copplestone: “This year 'no one visits dashboards anymore,' so he asked the team to go code-first.” In the story →

A secure virtual machine that encrypts data in use, preventing even the cloud provider from accessing its contents.

Mark Zuckerberg: “He joined to specifically work on this confidential VM project, which makes it so that we can make the commitment that even Meta can't see the content.” In the story →

Artificial intelligence applications designed directly for everyday individual users rather than businesses.

Brian Chesky: “Almost every app on my home screen has not changed fundamentally since AI, including Airbnb. I think that's going to change in two years.” In the story →

An advanced AI agent operating with specialized subagents, sandboxed environments, and complex execution capabilities.

Harrison Chase: “He says Engine is 'nothing more than a deep agent in a sandbox' with the LangSmith CLI.” In the story →

Technology, software, and hardware specifically developed for military, national security, and defense applications.

Alex Karp: “I think Palanteer graduates have helped bring that back in the context of defense tech.” In the story →

Making advanced cognitive capabilities and AI tools widely accessible to everyone, rather than just a select few.

Nikesh Arora: “Arora calls AI 'democratizing intelligence,' compared with Google democratizing information.” In the story →

The power of an individual, organization, or nation to have supreme control over its own digital data and technology.

Walter Isaacson: “In some ways you've been talking very strongly about digital sovereignty now that the big LLM companies are sucking up all our data.” In the story →

A concept where AI empowers individuals to continually expand their ambitions, capabilities, and self-determination over time.

Alexandr Wang: “AI could offer an escalator of agency: dream, achieve, dream bigger.” In the story →

A hands-on management style where founders stay deeply involved in product details and bypass traditional corporate hierarchy.

Patrick O'Shaughnessy: “Patrick O'Shaughnessy sits down with Airbnb CEO Brian Chesky to explore the critical shift from 'Founder Mode' to 'CEO Mode.'” In the story →

The leading edge of technological development and innovation where new capabilities are first discovered.

Josh Woodward: “Every company definitely needs a set of people, a team. Their job is to be at the frontier.” In the story →

The leading-edge research organizations developing state-of-the-art AI models, such as OpenAI or Anthropic.

Marina Wyss: “And the Frontier Labs of course pay higher and base is usually a fraction of the total compensation once you add in stock and bonuses.” In the story →

A workflow design that integrates human intervention, review, or approval into an automated AI decision-making process.

Mark Zuckerberg: “If so, then the sentinel agent is basically empowered to trigger this human in the loop review.” In the story →

A role that combines hands-on technical execution with people management responsibilities.

Brian Chesky: “I think everyone's going to have to be a hybrid.” In the story →

Model Context Protocol; an open standard that enables AI models to securely connect to data sources and tools.

Guillermo Rauch: “MCP is almost like the new HTTP.” In the story →

A slang term for a normal, non-technical, or average person.

Paul Copplestone: “He tries to 'code like a normie' so he knows how Supabase will work for non-experts next year.” In the story →

A startup run by a single individual that reaches a valuation of one billion dollars.

Vivian Shen: “Do you think we will see the first one-person unicorn company soon?” In the story →

An individual who uses advanced tools to operate with the capabilities, efficiency, and diverse functions of an entire company.

Sinead Bovell: “Bovell's phrase for the systems thinker who acts like a small company.” In the story →

An AI assistant designed to understand an individual's long-term goals and autonomously perform tasks on their behalf over time.

Mark Zuckerberg: “Probably the biggest personification of this vision is the Muse personal agent that we're rolling out.” In the story →

AI-driven software applications designed to perform tasks, manage schedules, and assist users with daily activities.

Sam Altman: “Altman said personal assistants already have most of the technology they need.” In the story →

A private, centralized storage system where an individual controls and manages all of their own digital data.

Paul Copplestone: “He argues the database is the valuable record and that you should own it and attach agents on top.” In the story →

Selling the tools and infrastructure needed for an industry rather than producing the final product.

Ethan Mollick: “The money went to those who used the technology to dig the gold mine, not to sellers of picks and shovels.” In the story →

A unified suite of technologies or services that allows users to perform multiple functions without switching systems.

Nikesh Arora: “Palo Alto Networks went from a firewall company to a platform.” In the story →

Exploding job offers with short expiration dates used by employers to force rapid decisions.

Colin Lernell: “Pressure deadlines backfire. Nine out of ten candidates now say they would accept an offer and keep looking.” In the story →

A working style that allows professionals to work outside of a traditional office environment.

Logically Answered: “Remote work pushed Fiverr's growth and stock to highs in 2020 and 2021.” In the story →

The branch of technology that deals with the design, construction, operation, and application of automated physical machines.

Andy Jassy: “AI, robotics and space development are the three biggest transformations of our time.” In the story →

An opportunity to be heard and to make decisions within a significant group or organization.

Yuzheng Sun: “By respecting the agency of senior leaders and giving useful information over time you earn a seat at the table.” In the story →

An autonomous AI system designed to analyze its own mistakes and automatically upgrade its own code or prompts.

Harrison Chase: “He calls a fully self-improving agent a North Star rather than something that exists today.” In the story →

The situation where a runner-up candidate is kept warm by a company in case the first-choice candidate declines.

Colin Lernell: “Keep a side channel with the hiring manager through the whole process; it also helps you avoid 'silver medal syndrome.'” In the story →

A large amount of low-quality, AI-generated content or ideas that must be filtered through.

Ethan Mollick: “His four skills: deep knowledge of something, wide knowledge of other fields, agency, and taste for picking good ideas out of the slop pile.” In the story →

Artificial intelligence capabilities developed, controlled, and owned by a specific nation or enterprise to protect its own data and interests.

Alex Karp: “Sovereign AI architecture lets a company power models it creates and owns.” In the story →

A colloquial term used here for an advisor who helps navigate a company's culture and processes.

Ethan Evans: “When Amazon bought Twitch, Evans went to work for the CEO, Emmett Shear, in a role they called spirit guide.” In the story →

A hypothetical AI that possesses cognitive capabilities far surpassing those of the smartest human minds across diverse domains.

Mark Zuckerberg: “So, I've always just kind of believed that when you build this super intelligence, there's this question of who decides what it's going to focus on.” In the story →

A country where the government or powerful entities engage in pervasive monitoring of the citizens' activities and data.

Alex Karp: “You'll see like Palanteer surveillance state, blah blah blah.” In the story →

Storing energy when it is produced and releasing it later when it is needed.

Zach Dell: “The electricity is time-shifted, not newly created.” In the story →

A business model so highly optimized, scalable, and efficient that it consistently outcompetes and dominates its market.

Nik Storonsky: “With time it can become an unstoppable machine, because it provides much better products and services...” In the story →

Moving toward higher-priced, higher-quality, or more prestigious goods and services.

Logically Answered: “The bet: go upmarket.” In the story →

Writing code or building software guided by intuition, high-level prompts, and aesthetic feel rather than strict traditional engineering.

Josh Woodward: “His advice to junior PMs: vibe code, but don't lose that major.” In the story →

A casual approach to software development where non-programmers use AI to write code based on high-level descriptions or prompts.

Yossi Matias: “Matias agrees, noting that vibe coding lets non-programmers do basic coding.” In the story →

An AI-powered assistant designed to provide medical advice, preliminary diagnoses, and healthcare guidance to patients remotely.

Bill Gates: “He says patients could get what is like a virtual doctor, and farmers top-notch advice on weather and seeds.” In the story →