The Foie Gras Effect

“More companies die from indigestion than from starvation." - Bill Hewlett, as quoted by David Packard.

In venture capital, capital intensity is often mistaken for competitive advantage. The thinking goes like this: If a startup is tackling a massive opportunity, it should raise aggressively to outspend competitors, dominate its category, and secure an unassailable lead. While this strategy works in select cases — when clear network effects, capital moats, or first-mover advantages exist — it can easily end in catastrophe. We’ve seen this movie time and again: promising companies force-fed capital before they’ve proven operational discipline, only to implode under the weight of their own excess.

Call it the “foie gras effect,” a process by which startups are larded with capital until they burst.

WeWork remains the most infamous example of this phenomenon. With nearly $12 billion in funding from SoftBank, WeWork ballooned into a real estate behemoth with grandiose aspirations and a $47 billion valuation. But no amount of capital could transform office leasing into a high-margin software business. The company’s unchecked spending, reckless expansion, and unsustainable model precipitated its dramatic implosion.

Among other high-profile recent examples, Quibi burned $1.75 billion on a product nobody wanted (TV shows you couldn’t watch on your TV), while Fast raised $120 million and promptly fast-forwarded to its own demise.

We’ve seen this pattern repeat across instant delivery startups (GoPuff, Jokr, Gorillas), crypto unicorns, and, most recently, AI companies chasing the next big breakthrough. In June 2023, Inflection AI announced that it had raised $1.3 billion in a round led by Microsoft; less than a year later, the same lead investor absorbed it whole, “feasting on Inflection’s body and sucking the marrow from the bones,” per TechCrunch’s colorful description.

On the other end of the spectrum, we have DeepSeek, the now-notorious Chinese AI company that built a model competitive with OpenAI’s ChatGPT and Google’s Gemini, supposedly on a mere $5.5 million of funding. DeepSeek’s success raised an uncomfortable question: Why do American AI companies require billions while others achieve similar results on a fraction of the budget?

A big part of the answer relates to capital discipline — or the lack thereof. When companies raise more money than they can productively use, they aren't just given runway; they're handed a ticking time bomb. The pressure to justify massive valuations forces them into reckless spending, prioritizing speed over strategy. Instead of refining their product and proving market fit, they inflate headcount with redundant teams, commit to expensive cloud and compute contracts without clear cost efficiencies, and pursue speculative R&D moonshots long before nailing core functionality.

The irony in AI companies falling victim to this bloat in particular is that, thanks to advances in generative AI, capital efficiency is easier to achieve than ever. Software development, customer support, content creation, and even sales processes are all becoming dramatically less resource-intensive. In many cases, the massive funding rounds we see today aren’t driven by genuine business needs, but by investors foisting capital onto startups in a bid to secure a stake in the next AI giant.

Yet the most effective AI companies won’t simply be the ones with the biggest war chests. Rather, they will be the ones that learn how to thrive as businesses: deploying capital with precision, investing in targeted compute instead of wasteful scale, hiring essential talent rather than bloated teams, and ensuring that each dollar of R&D is tied to real customer value, not vanity projects.

Of course, not all companies raising massive rounds are doomed to fail. Some of these will no doubt become the next Amazon or Uber, converting into true behemoths of the AI era. But for most founders (and investors), the foie gras approach is not a risk worth taking. The surer bet is leaner, capital-efficient startups that scale sustainably, treat every dollar like it matters, and focus on becoming indispensable to their customers. History shows that scarcity breeds efficiency, while abundance breeds waste. As horizontal AI and other hype cycles continue, expect more overfunded startups to implode. The real winners will be those who scale on discipline, not excess.