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.”

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.”

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?”