Tears streaming! 21 top VC firms collectively missed the mark, missing out on 3000x returns. This wave of "cognitive tax" hurts more than missing out on $BTC!

Imagine a team made up of core OpenAI members, showing up at the doors of 22 top venture capital firms with their business plan. The result: 21 of them turned them away. Five years later, the company’s valuation reached $350 billion. Those rejection letters from back then, in hindsight, turned out to be worth a fortune.

In 2021, as Anthropic’s team was running around seeking funding, they had a setup that was almost dreamlike: core executives from OpenAI, and the creator of GPT-3. Yet, in the mainstream capital of the time, large models were seen as a money-sucking monster. Anthropic’s steadfast focus on AI safety and its nonprofit background were viewed as nothing short of an incomprehensible “high-risk outlier.”

It wasn’t until Spark Capital led the Series C round that the market snapped out of its daze. One investor later admitted that projects that everyone could understand early on often lacked truly disruptive potential. The cost of this “cognitive sluggishness” was staggering: in May 2021, Anthropic completed a $124 million Series A round led by Jaan Tallinn. Looking back from today’s $350 billion valuation, the 21 institutions that rejected them missed out on nearly 3000x potential returns.

In this drama, Sequoia Capital’s performance was a textbook example. Its global head, Roelof Botha, had repeatedly turned down early lead positions citing “concentration risk.” This asset allocation logic drawn from traditional finance looks pale and powerless in the face of the AI wave that grows at exponential speed.

By early 2026, the actual contribution rate of AI investment to the U.S. GDP had surged to 40%. At that point, investing in AI was no longer a matter of choice—it was a matter of survival necessity. After Sequoia underwent leadership changes internally, it eventually squeezed into Anthropic’s latest round of financing, but the valuation had already jumped from the billions at the A round to $350 billion. To avoid so-called risk, they paid over 300x in “cognitive premium.”

When mainstream VCs were hesitant, who supported Anthropic? It was a group of technical believers whom traditional capital considered “crazy.” Jaan Tallinn, who led the A round, is the co-founder of Skype. His investment logic wasn’t about chasing financial returns, but rather rooted in deep concern about the risk of AI getting out of control.

Those who co-invested also included former Google CEO Eric Schmidt and Facebook co-founder Dustin Moskovitz, among others. Their common traits were: strong capital, deep technical expertise, and freedom from the short-term pressure typically imposed by traditional limited partners (LPs). This pot of money, which institutions at the time treated as “charity,” gave Anthropic a precious R&D window and ultimately incubated its core R1 series models.

By 2026, the capital chasing Anthropic had nothing to do with vision—it was pure survival. Macroeconomic data shows that if you strip out AI-related investments, the real U.S. GDP growth rate would plummet to below 0.7%. One analyst likened it: AI has become an indispensable “respirator” for the U.S. economy.

VC logic also fully flipped—from chasing general-purpose large models to deeply developing intelligent agents in vertical domains. This created a bitterly ironic cycle: in 2021, VCs missed Anthropic because they couldn’t understand “safety” and “large models.” Then in 2026, they may be淘汰’d by a new generation of funds because they can’t understand “vertical-domain knowledge.”

This five-year history reveals a cold reality: capital rarely creates the future; it only ends up being forced to pay huge fees to buy a ticket when the future becomes unavoidable. From 21 rejection letters to a $350 billion valuation, Anthropic ripped off the venture capital industry’s halo of “predicting the future” with data. Of that $350 billion, a substantial portion was simply the expensive tuition paid by latecomers for their own limitations in cognition.

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