Money was no object for the AI industry in early 2025. A vibe check crept in the second half of the year.
OpenAI raised $40 billion at a $300 billion valuation. Safe Superintelligence and Thinking Machine Labs raised individual $2 billion seed rounds before shipping a single product. Even first-time founders were raising at a scale that once belonged only to Big Tech.
Such astronomical investments were followed by equally incredible spends. Meta shelled out nearly $15 billion to lock up Scale AI CEO Alexandr Wang and spent countless more millions to poach talent from other AI labs. Meanwhile, AI’s biggest players promised close to $1.3 trillion in future infrastructure spending.
The first half of 2025 matched the fervor, and investor interest, of the prior year. That mood has shifted in recent months to deliver a vibe check of sorts. Extreme optimism for AI, and the accompanying wild valuations, is still intact. But that rosy view is now being tempered with concerns over an AI bubble bursting, user safety, and the sustainability of technological progress at its current pace.
The era of unabashed acceptance and celebration of AI is fading just a skosh at the edges. And with it, more scrutiny and questions. Can AI companies sustain their own velocity? Does scaling in the post-DeepSeek era require billions? Is there a business model that returns a sliver of the multi-billions of investment?
We’ve been there for every step. And our most popular stories of 2025 tell the real story: an industry hitting a reality check even as it promises to reshape reality itself.
How the year started
The biggest AI labs got bigger this year.
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In 2025 alone, OpenAI raised a Softbank-led $40 billion round at a $300 billion post-money valuation. The company also reportedly has investors like Amazon orbiting with compute-tied circular deals, and is in talks to raise $100 billion at an $830 billion valuation. That would bring OpenAI close to the $1 trillion valuation it is reportedly seeking in an IPO next year.
OpenAI rival Anthropic also closed $16.5 billion this year across two rounds, its most recent raise pushed its valuation to $183 billion with heavy hitters like Iconiq Capital, Fidelity, and the Qatar Investment Authority participating. (CEO Dario Amodei confessed to staff in a leaked memo that he was “not thrilled” about taking money from dictatorial Gulf states).
Then there’s Elon Musk’s xAI, which raised at least $10 billion this year after acquiring X, the social media platform formerly known as Twitter that Musk also owns.
We’ve also seen smaller, new startups get a hypey boost from froth-mouthed investors.
Former OpenAI chief technologist Mira Murati’s startup Thinking Machine Labs secured a $2 billion seed round at a $12 billion valuation despite sharing almost no information about its product offering. Vibe-coding startup Lovable’s $200 million Series A earned it a unicorn horn just eight months after launching; this month, Lovable raised another $330 million at a nearly $7 billion post-money valuation. And we can’t leave out AI recruiting startup Mercor, which raised $450 million this year across two rounds, the latest bringing its valuation up to $10 billion.
These absurdly large valuations are still happening even against the backdrop of still-modest enterprise adoption figures and serious infrastructure constraints, heightening fears of an AI bubble.
Build, baby, build
For the larger firms, those numbers aren’t coming from nowhere. Justifying those valuations requires building vast amounts of infrastructure.
The result has created a vicious cycle. Capital raised to fund compute is increasingly tied to deals where the same money flows back into chips, cloud contracts, and energy, as seen in OpenAI’s infrastructure-linked funding with Nvidia. In practice, it’s blurring the line between investment and customer demand, stoking fears that the AI boom is being propped up by circular economics rather than sustainable usage.
Some of the biggest deals this year powering the infrastructure boom were:
Stargate, a joint venture between Softbank, OpenAI, and Oracle, which includes up to $500 billion to build AI infrastructure in the U.S.
Alphabet’s acquisition of energy and data center infrastructure provider Intersect for $4.75 billion, which comes as the company said in October it plans to lift its compute spend in 2026 up to $93 billion.
Meta’s accelerated data center expansion, which has pushed its projected capital expenditures up to $72 billion in 2025 as the company races to secure enough compute to train and run next-generation models.
But cracks are beginning to show. A private financing partner, Blue Owl Capital, recently pulled out of a planned $10 billion Oracle data-center deal tied to OpenAI capacity, underscoring how fragile some of these capital stacks can be.
Whether all that spending ultimately materializes is another question. Grid constraints, soaring construction and power costs, and growing pushback from residents and policymakers – including calls from figures like Sen. Bernie Sanders to rein in data center expansion – are already slowing projects in some regions.
Even as AI investment remains enormous, the infrastructure reality is beginning to temper the hype.
The expectation reset
In 2023 and 2024, each major model release felt like a revelation, with new capabilities and fresh reasons to fall for the hype. This year, the magic faded, and nothing captured that shift better than OpenAI’s GPT-5 rollout.
While it was meaningful on paper, it didn’t land with the same punch as earlier releases like GPT-4 and 4o. Similar patterns emerged across the industry as improvements from LLM providers were less transformative and more incremental or domain-specific.
Even Gemini 3, which is topping several benchmarks, was only a breakthrough insofar as it brought Google back up to equal footing with OpenAI – which sparked Sam Altman’s infamous ‘code red’ memo and OpenAI’s fight to maintain dominance.
There was also a reset this year in terms of where we expect frontier models to come from. DeepSeek’s launch of R1, its “reasoning” model that competed with OpenAI’s o1 on key benchmarks, proved that new labs can ship credible models fast and at a fraction of the cost.
From model breakthroughs to business models
As the size of each leap between new models shrinks, investors are focused less on raw model capacity and more on what’s wrapped around it. The question is: who can turn AI into a product that people rely on, pay for, and integrate into their daily workflows?
That shift is manifesting in several ways as companies see what works, and what customers will let fly. AI search startup Perplexity, for example, briefly floated the idea of tracking users’ online movements to sell them hyper-personalized ads. Meanwhile, OpenAI was reportedly considering charging up to $20,000 per month for specialized AI, a sign of how aggressively companies tested the waters of what customers might be willing to pay.
More than anything, though, the fight has moved to distribution. Perplexity is trying to stay relevant by launching its own Comet browser with agentic capabilities and paying Snap $400 million to power search inside Snapchat, effectively buying its way into existing user funnels.
OpenAI is pursuing a parallel strategy, expanding ChatGPT beyond a chatbot and into a platform. OpenAI has launched its own Atlas browser and other consumer-facing features like Pulse, while also courting enterprises and developers by launching apps inside ChatGPT itself.
Google, for its part, is leaning on incumbency. On the consumer side, Gemini is being integrated directly into products like Google Calendar, while on the enterprise side, the company is hosting MCP connectors to make its ecosystem harder to dislodge.
In a market where it’s getting tougher to differentiate by dropping a new model, owning the customer and the business model is the real moat.
The trust and safety vibe check
AI companies received unprecedented scrutiny in 2025. More than 50 copyright lawsuits wound through the courts, while reports of “AI psychosis” – the result of chatbots reinforcing delusions and allegedly contributing to multiple suicides and other life-threatening episodes – sparked calls for trust and safety reforms.
While some copyright battles met their end – like Anthropic’s $1.5 billion settlement to authors – most are still unresolved. Though the conversation appears to be shifting from resistance against using copyrighted content for training, to demands for compensation (See: New York Times sues Perplexity for copyright infringement).
Meanwhile, mental health concerns around AI chatbot interactions – and their sycophantic responses – emerged as a serious public health issue following multiple deaths by suicide and life-threatening delusions in teens and adults after prolonged chatbot usage. The result has been lawsuits, widespread concern among mental health professionals, and swift policy responses like California’s SB 243 regulating AI companion bots.
Perhaps most telling: the calls for restraints are not coming from the usual anti-tech suspects.
Industry leaders have warned against chatbots “juicing engagement,” and even Sam Altman has cautioned against emotional over-reliance on ChatGPT.
Even the labs themselves started sounding alarms. Anthropic’s May safety report documented Claude Opus 4 attempting to blackmail engineers to prevent its own shutdown. The subtext? Scaling without understanding what you’ve built is no longer a viable strategy.
Looking ahead
If 2025 was the year AI started to grow up and face hard questions, 2026 will be the year it has to answer them. The hype cycle is starting to fizzle out, and now AI companies will be forced to prove their business models and demonstrate real economic value.
The era of ‘trust us, the returns will come’ is nearing its end. What comes next will either be a vindication or a reckoning that makes the dot-com bust look like a bad day of trading for Nvidia. Time to place your bets.
