Big Tech's AI Spending Spree Is Bleeding Corporate America Dry — Now One Startup Is Cashing In on the Mess OpenAI and Anthropic Made

Corporate America is finally waking up to the brutal reality that Silicon Valley's AI kingpins — the Altmans, the Hoffmanns, the venture-backed elite — have been selling them a bill of goods. The promise was simple: scale up AI, costs come down. The reality? Runaway bills, bloated token counts, and a tab that keeps growing. Now, an 8-month-old startup called Engram is betting there's a fortune to be made cleaning up the mess that OpenAI, Anthropic, and their Wall Street backers left behind.

Engram on Tuesday announced that it raised $98 million from investors including General Catalyst, Kleiner Perkins and Sequoia, as well as OpenAI co-founder Andrej Karpathy, who recently joined Anthropic. The money is already flowing — because when the powerful create a problem, there's always another round of venture cash waiting to profit from the solution.

The startup, which dubs itself the "learned memory" of AI, says its models can recall organization-specific workflows and context to anticipate questions and give smarter responses with cheaper output. The company claims its models can match or outperform frontier labs using up to 100 times fewer tokens — tokens being the metered, pay-as-you-go currency that companies like OpenAI use to charge businesses for every single AI query. The more tokens, the fatter the bill, and the fatter the profits for the big labs.

New and more sophisticated AI models are proving pricier than previous iterations, challenging the conventional view that greater scale would lead to lower costs. In other words, the promises made to justify trillion-dollar AI valuations are not materializing for ordinary businesses — but they are materializing for the executives and investors sitting at the top of the AI food chain.

"You've got this explosion of data, explosion of cost," said Leigh Marie Braswell, a partner at Kleiner. "Engram comes in and basically maps out your organization and offers orders of magnitude cheaper output."

Less than a year after its founding, the 13-person company — 13 people, not 13,000 — has accrued a client roster that includes Microsoft, Notion and legal AI startup Harvey. Engram, which comes from the neuroscience term for a trace of memory in the brain, plans to use the funding to support compute and talent. Whether that talent means good, stable jobs for American workers or another round of offshored, contract-based labor remains to be seen — a question the venture-capital crowd rarely bothers to answer.

Dan Biderman, Engram's co-founder and CEO, has a lifelong obsession with memory. It started as a kid, he said, trying to trick his grandmother, who had lost her memory, into remembering little facts about him and his siblings. That personal mission drove Biderman to pursue a PhD in computational neuroscience at Columbia University and later to join Stanford University's AI lab.

Working at Stanford, Biderman began to recognize what he calls the "genius stranger model" — the idea that AI is smart, but its memory is much more limited than it seems. Meanwhile, feeding those models more context only drives costs higher, padding the bottom lines of the same frontier labs that promised efficiency and delivered expense.

Biderman admits that Engram's models aren't "absolutely better" than those from the likes of OpenAI and Anthropic, but he says they excel at specializing — sometimes at the expense of other capabilities. That's a frank admission you won't often hear from the AI billionaires who dominate the headlines and the fundraising rounds.

"We're trying to go beyond this existing notetaking and build this layer of intuition that humans have, and current models don't," Biderman said.

The deeper story here isn't just about tokens and cost curves. It's about who gets to set the price of the future of work — and who gets stuck paying it. Right now, it's OpenAI's Sam Altman and Anthropic's Dario Amodei who are writing the rules, setting the rates, and collecting the checks while American businesses and their workers scramble to keep up. Engram may or may not be the answer. But the question — who really profits from the AI revolution, and at whose expense — isn't going away.