NEW RULES FOR FOUNDERS

The startup game has changed.

AI increases individual leverage, code becomes cheaper, and small teams can operate with capabilities that once required departments.

The founder advantage now comes from judgment, agency, direct knowledge, reusable systems, and the courage to follow truths others ignore.

FOUNDER MINDSET

THE FIRST MISTAKE: CHASING WHAT WAS HOT

After the dot-com crash, web work looked dead. Garry moved toward Windows Mobile because mobile appeared to be the next hot thing.

In hindsight, he already had years of web experience just before social software exploded.

Lesson: abandoning your accumulated edge to chase consensus can be expensive.

EARNESTNESS REQUIRES COURAGE

Being earnest is not simply being naive.

It means being willing to say: “My direct experience tells me something different.”

A blog post, investor, trend report, or popular opinion should not automatically override what you know from customers, products, technical work, or repeated first-hand evidence.

STOP ASKING: WHAT IS HOT?

The wrong question:
“What should I work on because it is hot?”

Better questions:
1. What am I genuinely interested in?
2. What do I know unusually well?
3. What problem have I seen directly?
4. Where do I have years of accumulated context?

Your edge often starts there.

MAP VS. TERRITORY

Garry describes another major mistake: saying no to joining the people who became Palantir because he was focused on career status and promotion.

He was reading the map: titles, prestige, what looked smart.

He should have looked at the territory: unusually capable people pursuing an important problem he could see firsthand.

FOLLOW EXCEPTIONAL PEOPLE

When unusually smart, driven people you deeply respect are gathering around a problem, pay attention.

A market may look unfashionable. The company may sound strange. The category may not exist yet.

The quality of the people, the problem they see, and the speed at which they learn can reveal more than public consensus.

FIND THE FRINGE

Many foundational technologies started at the edge.

Personal computers looked like a hobby for weird enthusiasts. Early social products looked trivial. New categories often begin as toys, obsessions, or niche communities.

A useful signal: if you can find the edge of the conversation but not the intellectual edge, there may still be unexplored territory.

FIND YOUR PEOPLE EARLY

Earlier generations often had to wait until college, work, or geographic relocation to find people who shared unusual interests.

Now niche communities form instantly across Reddit, X, forums, group chats, and specialized networks.

Founders can use this to find collaborators, early users, critics, and people obsessed with the same strange problem.

FOUNDERS & COMMUNITY

IDEAS + EXECUTION BEAT ACCESS

Garry credits YC’s founders with reducing the old social gatekeeping of Silicon Valley.

Instead of needing the right school, party, or introduction, founders could apply through a simple process and be judged by builders.

The principle: where you came from matters less when the system can evaluate what you think, build, and execute.

FOUNDER COMMUNITY NEEDS HONESTY

Founding is solitary.

When your best customer leaves, your strongest engineer quits, or your co-founder loses confidence, generic networking is not enough.

You need peers with whom you can be completely real.

A high-trust founder community is operational infrastructure because it gives you context, emotional stability, and better decisions.

WHY CO-FOUNDERS HISTORICALLY MATTERED

YC historically preferred co-founders because another committed person is evidence that the idea can attract belief.

One person dancing alone can look irrational. The second person changes the signal.

Co-founders also divide uncertainty, provide complementary judgment, and help sustain conviction during periods when the company looks impossible.

AI CHANGES THE SOLO-FOUNDER EQUATION

Garry argues that vibe coding and agentic coding materially change founder leverage.

A single capable person can now produce far more software, research, testing, analysis, and operations than before.

This does not eliminate the value of co-founders. It expands what a founder can attempt before building a large human organization.

THE AI-NATIVE FOUNDER

DO NOT COPY YESTERDAY’S SUCCESS

A dangerous instinct is to imitate the business model that worked for the previous generation of founders.

AI changes cost structures, team size, product speed, and competitive moats.

Garry’s warning: do not chase someone else’s past success. Build for the rules forming now, because the assumptions behind older successes may already be decaying.

PURE PER-SEAT SAAS IS LESS DEFENSIBLE

Garry questions whether a pure per-seat SaaS model will remain as durable over the next 5–10 years.

His implication:
1. SaaS can still be an entry point.
2. The real moat should grow elsewhere.
3. Consider proprietary data, network effects, workflow depth, distribution, or another compounding advantage.

CODE IS NO LONGER PRECIOUS

Previously, shipping software required specs, engineers, coordination, QA, testing, and significant time.

With coding agents, much of that work can be compressed.

This changes founder behavior. You can test small ideas, build prototypes, create internal tools, and discard weak attempts without treating every line of code as a scarce asset.

BUILD TRIVIAL THINGS ON PURPOSE

You do not need every AI experiment to become a company.

Build small, low-stakes things simply to understand what the models can do.

Use different models. Create tools. Automate tiny workflows. Test agents.

The purpose is to develop intuition. Founders who use the technology deeply will see product possibilities before people who only read about it.

AGENCY + TASTE BECOME MORE VALUABLE

If software becomes cheap to create, technical production alone becomes less differentiating.

The scarce skills shift toward:
1. Choosing worthwhile problems.
2. Knowing what “good” looks like.
3. Acting without permission.
4. Iterating rapidly.
5. Rejecting mediocre output.

Agency and taste improve through repeated practice.

YOU CAN BECOME MORE HIGH-AGENCY

Garry describes himself as a late bloomer who made major career mistakes and learned from them.

His point: agency is not necessarily fixed at birth.

You can inspect a poor decision, identify the belief that caused it, and change your future behavior.

A founder’s decision system can improve when mistakes become explicit lessons instead of hidden regrets.

BUILD BUSINESS LOOPS

THE MOST IMPORTANT LOOPS ARE BUSINESS LOOPS

Coding loops matter. Business loops matter more.

The goal is not simply to use AI for one task. The output of the task should improve how the company performs that task next time.

Example:
Do research → inspect errors → improve instructions → save the method → rerun automatically → learn from the next result.

TOKEN-MAX THE FOUNDER

Garry argues that founders and CEOs may rationally spend much more on high-context agents than ordinary users.

The reason is leverage: better decisions at the top affect product, hiring, strategy, operations, and capital allocation.

His framing is provocative: use enough compute and context to experience capabilities that may become normal only years later.

SKILLIFY EVERY REPEATED WIN

When you solve a difficult task, do not leave the solution inside one chat or one employee’s memory.

Turn it into a reusable asset:
1. Instructions.
2. Markdown or structured knowledge.
3. Code where needed.
4. Tests.
5. Automation or a scheduled run.

The result becomes a repeatable company capability.

A MARKDOWN FILE CAN BEHAVE LIKE AN EMPLOYEE

Garry’s shorthand: “a markdown file is an employee.”

The idea is not that a file literally replaces a person. It captures the method an agent needs to perform a job repeatedly.

A strong skill file can preserve context, rules, examples, checks, and expected outputs so the process becomes more consistent every time it runs.

DO THE PROCESS ONCE — PERFECTLY

Start with one business process: sales research, QA, customer support, reporting, recruiting, content, finance, or analysis.

Perform it with the agent. Correct every mistake. Improve the instructions. Add checks.

Once the result is reliable, preserve the workflow so the company does not restart from zero every time.

EVERY FUTURE ERROR BECOMES A BUG FIX

A reusable agent workflow improves differently from a human memory-based process.

When a future case fails:
1. Identify the failure.
2. Fix the instruction, logic, data, or test.
3. Save the correction.
4. Rerun.

If the improvement is retained, the organization becomes less likely to make the same exact mistake again.

AUTOMATE THE BOTTLENECK YOU CAN SEE

Garry describes building systems by repeatedly asking: where am I still doing manual verification, coordination, or decision work?

Each remaining bottleneck becomes the next automation target.

The pattern is recursive:
automate → observe new bottleneck → create tool/instruction → test → automate again.

TINY TEAMS CAN ATTEMPT HUGE OUTCOMES

Garry cites companies reaching very high revenue with only two or three people supported by hundreds of reusable agent skills.

The exact outcome will vary by company. The structural lesson is larger: headcount is becoming less tightly coupled to output.

A small team that systematizes work can compete at a scale that previously required departments.

AI-NATIVE MANAGEMENT

SCALE CREATES A CONTEXT PROBLEM

One reason companies slow down is simple: the organization becomes too complex for any one person to understand continuously.

Meetings multiply. Information fragments. Teams form local interpretations. Dependencies become invisible.

AI memory and retrieval can help reconstruct context across a company and surface what leadership is missing.

PROVENANCE MATTERS

As agents accumulate knowledge, conflicts appear.

Two documents may disagree. An old fact may contradict a newer one. A summary may be detached from its source.

Garry highlights provenance: know where information came from, when it was created, and which source should win.

AI-native companies need maintenance loops for their organizational memory.

EXPERIENCE MAY COMPOUND MORE IN THE AI ERA

Garry sees a possible advantage for founders in their 30s, 40s, and beyond.

Someone who has already built teams, products, and companies knows what failure patterns look like.

Give that person strong agents and the experience can be multiplied across research, execution, and management.

AI can amplify accumulated operating judgment.

USE AI TO SURFACE PRODUCTIVE CONFLICT

Healthy organizations need disagreement. Human conflict often adds status, fear, politics, and emotion.

AI can help compare competing approaches, summarize evidence, identify contradictions, and surface unresolved decisions.

The goal is not to remove human judgment. It is to make disagreement more legible so teams can resolve it using better context.

LEADERSHIP WITH DEEP CONTEXT

Garry describes a CEO using agents to review meeting transcripts across direct reports and deeper layers of the company.

That can reveal blockers, conflicts, and decisions without attending every meeting.

A leader can enter a critical discussion with weeks of context, make a focused decision, and leave the team to execute.

MANAGEMENT BECOMES RETRIEVAL + JUDGMENT

The AI-native executive does not need to personally sit inside every information flow.

A better system:
1. Capture meetings and operational data.
2. Build searchable memory.
3. Retrieve the right context for each decision.
4. Compare claims with ground truth.
5. Intervene only where judgment is actually needed.

HUMAN MEMORY IS A STRUCTURAL BOTTLENECK

Traditional companies assume humans can only hold a small number of active concerns at once.

That limitation shapes meetings, hierarchy, middle management, reporting, and bureaucracy.

Agents can retain and retrieve far more context. This creates a chance to redesign the company itself instead of merely adding AI to old workflows.

MAKE PRODUCTS 10×, 100×, 1000× BETTER

Garry’s aspiration is not merely to make existing work cheaper.

AI should create dramatically better products and services: more responsive, more personalized, more informed, and more capable.

The founder question is not only “What can I automate?”

It is also: “What becomes possible when intelligence and execution are far more abundant?”

REDESIGN THE COMPANY

BUREAUCRACY WASTES HUMAN POTENTIAL

Garry recalls needing extraordinary effort at Microsoft simply to get another team to address a blocking bug.

The problem was not lack of intelligence. It was layers, fiefdoms, priorities, and missing context.

Large organizations can turn simple coordination into weeks of friction.

Startups should design systems that prevent this from forming.

A STARTUP CAN — SO IT MUST

A large incumbent may struggle to redesign itself around agents, loops, and continuously available context.

A startup begins with fewer constraints.

That creates an obligation: organize for speed from day one.

Build shared memory, automate coordination, expose blockers, shorten feedback loops, and let a tiny team behave like a much larger one.

AGENTS CAN ABSORB COORDINATION WORK

One possibility discussed in the conversation is shifting more organizational work to agents:

• Track dependencies.
• Summarize status.
• Surface conflicts.
• Maintain context.
• Route issues.
• Check completion.

Executives set direction. Individuals execute. Agents can increasingly help coordinate the layer between them.

AI CAN ERASE THE API LINE

Traditional systems often force people to obey rigid software workflows.

Garry imagines a more conversational model: an agent encounters a bug, reports it, receives a workaround, and continues.

This turns software from a one-way command structure into a feedback loop.

Users and agents can influence the tools they depend on while work is happening.

TOYOTA’S LESSON: LET THE WORKER IMPROVE THE LINE

Garry connects AI-native work to the Toyota Production System.

The person closest to the work often has the strongest context for improving the process.

A powerful system lets that knowledge change the workflow.

With agents, each correction can become reusable organizational knowledge instead of disappearing after the individual task is finished.

CHAPTER 7 — HOW FAST WILL THIS CHANGE?

HUMANS ARE THE ADOPTION BOTTLENECK

Garry argues that the limiting factor may increasingly be human organizations rather than raw model intelligence.

Companies, governments, institutions, regulations, incentives, and habits move slowly.

This is important for founders: the technical future can arrive before customers and institutions are ready to reorganize around it.

THE 20-YEAR ADOPTION WINDOW

Garry compares today’s AI-native young builders with earlier generations that grew up native to the web and mobile.

Those generations eventually came to run major institutions.

His optimistic view: broad transformation may take decades, not months.

That slower transition creates time for people, companies, and society to adapt.

INCUMBENTS STILL HAVE REAL MOATS

AI increases startup leverage, but large institutions do not disappear automatically.

Incumbents have customers, distribution, regulation, capital, data, contracts, habits, and infrastructure.

A startup should respect those structural moats while exploiting the areas where a smaller organization can move faster, learn faster, and serve users better.

THE NEXT COMPUTER

THE INTERFACE MOVES TOWARD VOICE + MEMORY

Garry expects the near-term computer to look familiar, but he finds it hard to believe the current form factor is permanent.

He points toward voice, memory, computer use, ingestion, and richer personal context.

The emerging assistant is not only something you prompt. It increasingly understands what you are doing and what matters to you.

THE REAL PRODUCT IS CONTEXT

A powerful personal AI would understand your goals, fears, preferences, history, work, and current situation.

It could continuously search for ways to help rather than waiting for isolated prompts.

That requires memory, permissions, trust, retrieval, privacy, and reliable context management.

The race is not only for smarter models. It is for better harnesses.

2027: THE “HARNESS WARS” PREDICTION

Garry predicts that 2027 may become a period of intense competition around AI harnesses: the systems that connect models to memory, tools, context, computer use, workflows, and users.

This is a prediction from the conversation, not a certainty.

Founder opportunity may increasingly sit in how intelligence is packaged and directed.

CONSUMER AI NEEDS BETTER ECONOMICS

Many consumer software products rely on free trials or very low-cost distribution.

High model costs make that difficult.

As capable models become cheaper, founders can build more ambitious consumer AI products with persistent intelligence, richer interaction, and broader access.

Falling inference cost can unlock categories that are currently uneconomic.

BUILD A BETTER WORLD

ATTEMPT THE BETTER WORLD

Garry rejects the need to promise a perfect technological utopia.

The standard can be simpler: use technology earnestly to make products, services, work, and institutions better.

A founder can hold ambition without pretending every consequence is solved.

The job is to build something meaningfully better and keep correcting what fails.

WORK SHOULD BECOME LESS BUREAUCRATIC

If agents absorb reporting, coordination, retrieval, repetitive analysis, and process maintenance, people can spend more time on judgment, relationships, creativity, craft, and direct execution.

The promise is not simply “do more work.”

It is to remove the organizational friction that consumes attention without improving the product or the customer outcome.

ACT LOCAL

The final part of the conversation shifts from startups to civic life.

Garry’s principle is local action: care about the people and systems immediately around you.

Technology may increase capability, but human coordination still requires people to participate, speak up, organize, vote, and improve institutions directly.

THE NEW FOUNDER OPERATING SYSTEM

THE 10 NEW RULES

1. Trust direct experience.
2. Stop chasing what is hot.
3. Follow exceptional people.
4. Explore the fringe.
5. Build constantly with AI.
6. Increase agency and taste.
7. Turn wins into reusable skills.
8. Build business loops.
9. Redesign the organization for agents.
10. Use the leverage to build something meaningfully better.

THE FOUNDER’S WEEKLY LOOP

Every week ask:

1. What did I learn directly from users?
2. What bottleneck did I remove?
3. What process did I turn into a reusable skill?
4. What did the agent get wrong, and did I fix it permanently?
5. What important truth am I ignoring because consensus says otherwise?
6. Did the product become materially better?

FINAL IDEA

AI does not automatically create a great company.

The advantage goes to founders who change how they think, build, decide, learn, and organize.

Use AI as leverage. Preserve direct contact with reality. Capture knowledge into repeatable systems. Keep the team small enough to move fast and ambitious enough to build what old organizations cannot.