In the beginning, VFA was an institution beloved by many of its fellows. “It was a wonderful way to leave college and enter the real world because you’re surrounded by a community and there’s support from the organization,” says Jamie Norwood, co-founder of feminine hygiene brand Winx Health. Norwood and her co-founder, Cynthia Plotch, are a VFA success story. They met as fellows in 2015 and VFA eventually helped them launch their company with a grant and advisement. “We always say, Winx Health would not be here without VFA,” Norwood says.

Norwood and Plotch went through the standard VFA admissions protocol, which was rigorous. It required two written applications, a video interview, and in-person interviews at an event called “Selection Day,” many of which were held in New York City and Detroit over the years. By the end of each university term in May, accepted fellows would get access to Connect, VFA’s job portal, and have until November to land a job. For each fellow hired in a full-time job, VFA received a $5,000 placement fee, paid by partner companies. This fee became a crucial revenue stream for the organization—effectively wedding the professional success of its fellows to its bottom line.

Selection Day interviews were conducted by judges who often pitted interviewees against each other. Candidates were told to organize themselves in order of least to most likely to be successful, or according to whose answers had the most value per word. The format felt ruthless. “People cried” during the interview process, Plotch remembers.

The problems with the business bled into the fellows’ experience in 2023 and 2024, leaving them disenchanted, financially struggling, or expelled en masse from the program for reasons they believe were beyond their control. Despite a multitude of financial red flags, VFA leadership still insisted on recruiting for the 2024 class. “The talent team was traveling nonstop, using prepaid Visa cards since the corporate cards didn’t work,” explains a former director who worked closely with fellows.

Onboarding fresh recruits became increasingly crucial if VFA was going to survive. The organization asked companies for placement fees upfront in 2023, according to internal VFA documents and conversations with former employees. The policy change gave companies pause. Fewer companies signed up as partners, meaning fellows weren’t getting jobs and VFA was losing money.

In the spring of 2023, “there were 15 jobs on opening day,” for a class that eventually grew to over 100 fellows, the former director explains. Gabriella Rudnik, a 2023 fellow, estimates that when training camp began in July 2023, less than half of her peers had jobs, “whereas in previous years it would be closer to like 80 percent.”

Fellows were made to pay the price for the shortage of companies partnering with VFA in 2023. “We weren’t getting more jobs on Connect, and that’s what led to so many fellows being off-boarded,” explains a former director who worked closely with fellows.

Traditionally, VFA gave fellows a deadline of November of their class year to find a job, which typically meant a few stragglers were given extra help to find a position if they were late. In those rare cases during earlier years, fellows were offboarded by the organization, a former director says.

In previous years, expulsion was a much more serious and infrequent occurrence. “Removal from the fellowship was not something done lightly. During my tenure, we instituted an internal investigation process, similar to an HR investigation,” says the former executive who worked at VFA from 2017-20.  In total, at least 40 fellows from the 2023 class were expelled for failing to get jobs that weren’t available, according to research by former VFA fellows who tracked the number of fellows purged from a Slack channel. Records of their participation were removed from the VFA website, the fellows say.

Many fellows had made sacrifices to be part of the highly selective and prestigious VFA, which cited acceptance rates of around 10 percent of applicants. “There were fellows who turned down six-figure jobs to be a part of this program, and were told that the program that Andrew Yang started would live up to its reputation,” says Paul Ford, a 2024 fellow.

Though internal documents show that VFA was slowly imploding for months, in all external communications with fellows, the nonprofit still maintained that 2024 training camp would take place in Detroit.

“From an ethical perspective, it does reek of being problematic,” says Thad Calabrese, a professor of nonprofit management at New York University. “You entered into an arrangement with people who don’t have a lot of money, who believed that you were going to make them whole. Then you’re going to turn around and not make them whole.”

people mix up two very different types of pivots and that it’s important to differentiate which path you’re on: Ideation pivots: This is when an early-stage startup changes its idea before having a fully formed product or meaningful traction. These pivots are easy to make, normally happen quickly after launch, and the new idea is often completely unrelated to the previous one. For example, Brex went from VR headsets to business banking, Retool went from Venmo for the U.K. to a no-code internal tools app, and Okta went from reliability monitoring to identity management all in under three months. YouTube changed direction from a dating site to a video streaming platform in less than a week. Hard pivots: This is when a company with a live product and real users/customers changes direction. In these cases, you are truly “pivoting”—keeping one element of the previous idea and doubling down on it. For example, Instagram stripped down its check-in app and went all in on its photo-sharing feature, Slack on its internal chat tool, and Loom on its screen recording feature. Occasionally a pivot is a mix of the two (i.e. you’re pivoting multiple times over 1+ years), but generally, when you’re following the advice below, make sure you’re clear on which category you’re in.

When looking at the data, a few interesting trends emerged: Ideation pivots generally happen within three months of launching your original idea. Note, a launch at this stage is typically just telling a bunch of your friends and colleagues about it. Hard pivots generally happen within two years after launch, and most around the one-year mark. I suspect the small number of companies that took longer regret not changing course earlier.

ou should have a hard conversation with your co-founder around the three-month mark, and depending on how it’s going (see below), either re-commit or change the idea. Then schedule a yearly check-in. If things are clicking, full speed ahead. If things feel meh, at least spend a few days talking about other potential directions.

Brex: “We applied to YC with this VR idea, which, looking back, it was pretty bad, but at the time we thought it was great. And within YC, we were like, ‘Yeah, we don’t even know where to start to build this.’” —Henrique Dubugras, co-founder and CEO

Many people try out careers in venture and then wind up leaving after a year when it stops feeling novel and starts feeling like they’re floating in lonely limbo without any markers of success. That’s because the craft of venture is not for people who derive their satisfaction from external indicators of progress — it’s for people who find the development of their relationships and refinement of their internal model of the world to be motivation enough to keep going.

If you derive satisfaction from refining a craft, don’t go into venture yet.

Here are five individual investor archetypes I’ve noticed can produce outsized returns in the early-stage venture game:Philosopher: hangs one’s reputation on their predictions about the futureHustler: simply outwork everyone else and are great at networkingHawk: most competitive, gets a thrill out of the fight to win a deal Friend: confidante and coach to founders, often founders’ first callCelebrity: a person widely respected for their work/knowledge/skill

A good archetype for you is whichever one you can sustain the longest. A great archetype for you is one that no one else is doing and you have some sort of signal works.

Some good advice I got: build your fund’s structure and strategy around allowing yourself to invest in whichever way you most enjoy and are naturally good at (admittedly, it will probably get harder and harder to stick to this as your fund scales).

Examples: if you like being a friend to founders and want your fund to function as Switzerland (i.e. not compete with anyone), write small checks. If you like to fight and are naturally hawkish, it might make sense to set yourself up to try to lead rounds. If complex problems and futuristic theories are what get you excited, investing in series A companies that fit into where you see the world going could be quite gratifying. If for some reason you love living in spreadsheets, consider growth investing (and don’t follow literally any of my advice).

In many ways, the job of the writer and the job of a VC are quite similar, in that they both ask you to produce an original end product (in the writer’s case, articulated ideas and stories; in the investor’s case, a differentiated portfolio with outsized financial returns) without much of a map for how you get there. The reason professional writers complain about writing so much is that it’s really difficult to wrangle your brain into producing uniquely interesting thoughts all the time, and highly frustrating when you consider it your job to do so. Making good investment decisions is similar; just with the added element of also being highly social. Taking the quality of your self talk seriously seems superfluous but is an investment that will result in better decisions.

A lot of the game of investing is won by getting people to think of you — a sign that you’ve built the kind of moat we call a strong brand. Remember: a fund is just a pile of money with a person on top to sell it. As an investor, putting down stakes in the ground about what you invest in saves you a lot of time in the long run because it allows people to self-select for fit

I’ve been surprised by how much it’s benefited my fund to make Moth’s brand (i.e. what I invest in and look for) difficult to summarize in a sentence. For small early-stage generalist funds like my own, quality matters much more than quantity. Quality deals almost always come from trusted sources who resonate with my taste, not from a list of random companies for which I have no context.

A brand is a promise to show up in the same way time and time again. Good brands are built on being decent and principled with all of the people you interact with.

Lastly and of utmost importance: remember that fear of failure fades into the background if you focus on leaving everyone you encounter along the way better than you found them.

VCs need to believe that your company could be a billion-dollar business and generally lack imagination — you need to paint a vivid picture of this path for them, starting with the striking protagonist character you play in your company’s story.Your alter ego should never lie, but it should be completely comfortable showing the fullest expression of your ambition to people who probably intimidate you. Fundraising is a snap judgment game — most VCs are trying to pattern-match you to a founder archetype who already won. They index primarily on IQ, self-belief, experience, and personability (in that order). A general rule of thumb is that to be taken seriously in SV, male founders would benefit from acting warmer, while female founders are taken more seriously when they act colder. Both benefit from acting a little entitled.

a VC’s job is to make a diversified portfolio of bets — you are only one. Most founders find being around VCs distracting and draining because they feel pressure to perform the role of ‘impressive person.’ If you can’t immediately capture value from your performance… why waste your energy?

don’t expect the average investor to provide much value beyond money and connections. This makes the 10% of investors who can be legitimately useful to your business worth their weight in gold. Develop litmus tests to identify the valuable ones quickly and avoid wasting your time trying to convince nonbelievers.

our goal here is to spend as little time fundraising as possible — which requires being strategic about the order in which you talk to investors and how you talk about where things stand as you progress through the raise. The combined force of controlling those two variables are what “generates momentum” during your fundraise process.

Make a list of all the investors you know and can get introduced to, ordering them by the ones you most want on board to the ones you couldn’t care less aboutTalk first to a few low-stakes investors at the bottom of your list to practice your pitch and identify common investor questions and critiques you’re going to getIf available to you, next get a few investors who already wanted to give you money on board so you have a dollar amount you can say you’ve raisedWork your way up your investor list, talking to the investors you most-want-on-board-but-still-need-to-convince last (this optimizes your odds they say yes)

This all goes by much faster if you court investors similarly to how hot girls treat their many potential suitors. If your raise is already a little taken and you exude an air that you don’t need them, mimetically-minded investors become much more interested.

If you’re anything like me, you will worry intensely about not making a fool of yourself. It will probably go ok, but not as amazing or illuminating as you’d hoped. You might leave and feel a deep sense of lostness set in. This is all very normal. In time you will see them in increasing clarity, often noticing the differences between your and their values and why you would not enjoy living their life at all.

the people on pedestals probably hate being there. It’s lonely, hard to trust that the intentions of the new people around you are pure, and you often feel like you’re constantly letting people down. In the end, idolization hurts everyone involved.

Beware of intellectual dementors and clout demonsIntellectual dementors will try to eat your ideas and interestingness — not necessarily to copy you, but to wring your brain dry to amass knowledge themselves. They often play mini IQ games/tests of will in conversation and masquerade as investors while never actually investing. Clout demons are similar, but view people less as brains and more as stepping stones towards supreme social status. The power move to protect yourself from both is to simply abstain from playing their games — give as little info on yourself and your ideas as possible and reflect their questions directly back at them.

People will help you if you ask for what you want clearly and concisely

Knowing what you want requires a lot of upfront soul-searching, followed by strategic and long-term thinking once you’ve committed to a thing (I can’t really demystify this more). Once you’re all in, I highly recommend diligently keeping a list somewhere of the top three things you currently need help with so when people ask, you’re ready.

You don’t want to make people feel like you’re using them but you do want to use your social capital for things you care about. General rule of thumb: ask for things either 1) after a positive interaction or 2) completely out of the blue with a concisely written and compelling email/text. Tone matters because you don’t want to sound desperate and you do want to show you know how to play the game (write like the founder you most admire talks).

once we’ve taken action on behalf of something, our brain assigns more value to said thing. Tim Keller: “The feeling of love follows the action of love.” Love is a strong word here, but the point stands — help people help you. Startups are long-term games, so it only makes sense to do them with people you truly want to be around for a very long time.

inside look at how Perplexity builds product—which to me feels like what the future of product development will look like for many companies:AI-first: They’ve been asking AI questions about every step of the company-building process, including “How do I launch a product?” Employees are encouraged to ask AI before bothering colleagues.Organized like slime mold: They optimize for minimizing coordination costs by parallelizing as much of each project as possible.Small teams: Their typical team is two to three people. Their AI-generated (highly rated) podcast was built and is run by just one person.Few managers: They hire self-driven ICs and actively avoid hiring people who are strongest at guiding other people’s work.A prediction for the future: Johnny said, “If I had to guess, technical PMs or engineers with product taste will become the most valuable people at a company over time.”

Typical projects we work on only have one or two people on it. The hardest projects have three or four people, max. For example, our podcast is built by one person end to end. He’s a brand designer, but he does audio engineering and he’s doing all kinds of research to figure out how to build the most interactive and interesting podcast. I don’t think a PM has stepped into that process at any point.

We leverage product management most when there’s a really difficult decision that branches into many directions, and for more involved projects.

The hardest, and most important, part of the PM’s job is having taste around use cases. With AI, there are way too many possible use cases that you could work on. So the PM has to step in and make a branching qualitative decision based on the data, user research, and so on.

a big problem with AI is how you prioritize between more productivity-based use cases versus the engaging chatbot-type use cases.

we look foremost for flexibility and initiative. The ability to build constructively in a limited-resource environment (potentially having to wear several hats) is the most important to us.

We look for strong ICs with clear quantitative impacts on users rather than within their company. If I see the terms “Agile expert” or “scrum master” in the resume, it’s probably not going to be a great fit.

My goal is to structure teams around minimizing “coordination headwind,” as described by Alex Komoroske in this deck on seeing organizations as slime mold. The rough idea is that coordination costs (caused by uncertainty and disagreements) increase with scale, and adding managers doesn’t improve things. People’s incentives become misaligned. People tend to lie to their manager, who lies to their manager. And if you want to talk to someone in another part of the org, you have to go up two levels and down two levels, asking everyone along the way.

Instead, what you want to do is keep the overall goals aligned, and parallelize projects that point toward this goal by sharing reusable guides and processes.

Perplexity has existed for less than two years, and things are changing so quickly in AI that it’s hard to commit beyond that. We create quarterly plans. Within quarters, we try to keep plans stable within a product roadmap. The roadmap has a few large projects that everyone is aware of, along with small tasks that we shift around as priorities change.

Each week we have a kickoff meeting where everyone sets high-level expectations for their week. We have a culture of setting 75% weekly goals: everyone identifies their top priority for the week and tries to hit 75% of that by the end of the week. Just a few bullet points to make sure priorities are clear during the week.

All objectives are measurable, either in terms of quantifiable thresholds or Boolean “was X completed or not.” Our objectives are very aggressive, and often at the end of the quarter we only end up completing 70% in one direction or another. The remaining 30% helps identify gaps in prioritization and staffing.

At the beginning of each project, there is a quick kickoff for alignment, and afterward, iteration occurs in an asynchronous fashion, without constraints or review processes. When individuals feel ready for feedback on designs, implementation, or final product, they share it in Slack, and other members of the team give honest and constructive feedback. Iteration happens organically as needed, and the product doesn’t get launched until it gains internal traction via dogfooding.

all teams share common top-level metrics while A/B testing within their layer of the stack. Because the product can shift so quickly, we want to avoid political issues where anyone’s identity is bound to any given component of the product.

We’ve found that when teams don’t have a PM, team members take on the PM responsibilities, like adjusting scope, making user-facing decisions, and trusting their own taste.

What’s your primary tool for task management, and bug tracking?Linear. For AI products, the line between tasks, bugs, and projects becomes blurred, but we’ve found many concepts in Linear, like Leads, Triage, Sizing, etc., to be extremely important. A favorite feature of mine is auto-archiving—if a task hasn’t been mentioned in a while, chances are it’s not actually important.The primary tool we use to store sources of truth like roadmaps and milestone planning is Notion. We use Notion during development for design docs and RFCs, and afterward for documentation, postmortems, and historical records. Putting thoughts on paper (documenting chain-of-thought) leads to much clearer decision-making, and makes it easier to align async and avoid meetings.Unwrap.ai is a tool we’ve also recently introduced to consolidate, document, and quantify qualitative feedback. Because of the nature of AI, many issues are not always deterministic enough to classify as bugs. Unwrap groups individual pieces of feedback into more concrete themes and areas of improvement.

High-level objectives and directions come top-down, but a large amount of new ideas are floated bottom-up. We believe strongly that engineering and design should have ownership over ideas and details, especially for an AI product where the constraints are not known until ideas are turned into code and mock-ups.

Big challenges today revolve around scaling from our current size to the next level, both on the hiring side and in execution and planning. We don’t want to lose our core identity of working in a very flat and collaborative environment. Even small decisions, like how to organize Slack and Linear, can be tough to scale. Trying to stay transparent and scale the number of channels and projects without causing notifications to explode is something we’re currently trying to figure out.

The “hard tech” in AI are the LLMs available for rent from OpenAI, Anthropic, Cohere, and others, or available as open source with Llama, Bloom, Mistral and others. The hard-tech is a level playing field; startups do not have an advantage over incumbents.

There can be differentiation in prompt engineering, problem break-down, use of vector databases, and more. However, this isn’t something where startups have an edge, such as being willing to take more risks or be more creative. At best, it is neutral; certainly not an advantage.

This doesn’t mean it’s impossible for a startup to succeed; surely many will. It means that you need a strategy that creates differentiation and distribution, even more quickly and dramatically than is normally required

Whether you’re training existing models, developing models from scratch, or simply testing theories, high-quality data is crucial. Incumbents have the data because they have the customers. They can immediately leverage customers’ data to train models and tune algorithms, so long as they maintain secrecy and privacy.

Intercom’s AI strategy is built on the foundation of hundreds of millions of customer interactions. This gives them an advantage over a newcomer developing a chatbot from scratch. Similarly, Google has an advantage in AI video because they own the entire YouTube library. GitHub has an advantage with Copilot because they trained their AI on their vast code repository (including changes, with human-written explanations of the changes).

While there will always be individuals preferring the startup environment, the allure of working on AI at an incumbent is equally strong for many, especially pure computer and data scientsts who, more than anything else, want to work on interesting AI projects. They get to work in the code, with a large budget, with all the data, with above-market compensation, and a built-in large customer base that will enjoy the fruits of their labor, all without having to do sales, marketing, tech support, accounting, raising money, or anything else that isn’t the pure joy of writing interesting code. This is heaven for many.

A chatbot is in the chatbot market, and an SEO tool is in the SEO market. Adding AI to those tools is obviously a good idea; indeed companies who fail to add AI will likely become irrelevant in the long run. Thus we see that “AI” is a new tool for developing within existing markets, not itself a new market (except for actual hard-tech AI companies).

AI is in the solution-space, not the problem-space, as we say in product management. The customer problem you’re solving is still the same as ever. The problem a chatbot is solving is the same as ever: Talk to customers 24/7 in any language. AI enables completely new solutions that none of us were imagining a few years ago; that’s what’s so exciting and truly transformative. However, the customer problems remain the same, even though the solutions are different

Companies will pay more for chatbots where the AI is excellent, more support contacts are deferred from reaching a human, more languages are supported, and more kinds of questions can be answered, so existing chatbot customers might pay more, which grows the market. Furthermore, some companies who previously (rightly) saw chatbots as a terrible customer experience, will change their mind with sufficiently good AI, and will enter the chatbot market, which again grows that market.

the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”

That other people tried your idea without success could imply it’s a bad idea or simply that the timing or execution was wrong. Distinguishing between these cases is hard and where you should apply serious thought. If you think your competitors executed poorly, you should develop a theory of what they did wrong and how you’ll do better.

If you think your competitor’s timing was off, you should have a thesis about what’s changed to make now the right time. These changes could come in a variety of forms: for example, it could be that users have become more sophisticated, the prices of key inputs have dropped, or that prerequisite technologies have become widely adopted.

Startups are primarly competing against indifference, lack of awareness, and lack of understanding — not other startups.

There were probably 50 companies that tried to do viral video sharing before YouTube. Before 2005, when YouTube was founded, relatively few users had broadband and video cameras. YouTube also took advantage of the latest version of Flash that could play videos seamlessly.

Google and Facebook launched long after their competitors, but executed incredibly well and focused on the right things. When Google launched, other search engines like Yahoo, Excite, and Lycos were focused on becoming multipurpose “portals” and had de-prioritized search (Yahoo even outsourced their search technology).

We still believe that there will be a separation between the “application layer” companies and foundation model providers, with model companies specializing in scale and research and application layer companies specializing in product and UI. In reality, that separation hasn’t cleanly happened yet. In fact, the most successful user-facing applications out of the gate have been vertically integrated.

We predicted that the best generative AI companies could generate a sustainable competitive advantage through a data flywheel: more usage → more data → better model → more usage. While this is still somewhat true, especially in domains with very specialized and hard-to-get data, the “data moats” are on shaky ground: the data that application companies generate does not create an insurmountable moat, and the next generations of foundation models may very well obliterate any data moats that startups generate. Rather, workflows and user networks seem to be creating more durable sources of competitive advantage.

Some of the best consumer companies have 60-65% DAU/MAU; WhatsApp’s is 85%. By contrast, generative AI apps have a median of 14% (with the notable exception of Character and the “AI companionship” category). This means that users are not finding enough value in Generative AI products to use them every day yet.

generative AI’s biggest problem is not finding use cases or demand or distribution, it is proving value. As our colleague David Cahn writes, “the $200B question is: What are you going to use all this infrastructure to do? How is it going to change people’s lives?”

There are four signs your idea has legs:People pay you money: Several people start to pay for your product, ideally people you don’t have a direct connection toContinued usage: People continue to use your prototype product, even if it’s hackyStrong emotion: You’re hearing hatred for the incumbents (i.e. pain) or a deep and strong emotional reaction to your idea (i.e. pull)Cold inbound interest: You’re seeing cold inbound interest in your product

Every prosumer collaboration product, including Figma, Notion, Coda, Airtable, Miro, and Slack, spent three to four years wandering in the dark until they stumbled on something that clicked.

organic startup ideas usually don't seem like startup ideas at first. We know now that Facebook was very successful, but put yourself back in 2004. Putting undergraduates' profiles online wouldn't have seemed like much of a startup idea. And in fact, it wasn't initially a startup idea. When Mark spoke at a YC dinner this winter he said he wasn't trying to start a company when he wrote the first version of Facebook. It was just a project. So was the Apple I when Woz first started working on it. He didn't think he was starting a company. If these guys had thought they were starting companies, they might have been tempted to do something more "serious," and that would have been a mistake.