2025 was the year that artificial intelligence was examined

Money was no object for the AI ​​industry in early 2025. Vital testing crept in in the second half of the year.

OpenAI Raised $40 billion With a valuation of $300 billion. Super-safe intelligence and Thinking Machine Labs It raised individual seed rounds of $2 billion before shipping a single product. Even first-time founders were raising money on a scale previously reserved only for big tech companies.

Such astronomical investments were followed by incredible spending. dead It spent approximately $15 billion To lock up Scale AI CEO Alexander Wang and spend countless millions poaching talent from other AI labs. Meanwhile, the biggest AI players have promised nearly $1.3 trillion in future infrastructure spending.

The first half of 2025 matches investor enthusiasm and interest in the previous year. This mood has changed in recent months to provide a vital check of sorts. Extreme optimism about artificial intelligence, and the wild valuations that accompany it, remains. But this rosy outlook is now tempered by concerns about the bursting of the AI ​​bubble, user safety, and the sustainability of technological progress at the current pace.

The era of unabashed acceptance and celebration of AI is fading with just a stroke of the edges. And with it more scrutiny and questions. Can AI companies maintain their speed? Will scaling in the post-DeepSeek era require billions? Is there a business model that returns a fraction of the billions invested?

We have been there every step of the way. And our most popular stories of 2025 tell the real story: an industry that delivers reality testing even as it promises to reshape reality itself.

How the year started

The largest AI labs got even bigger this year.

In 2025 alone, OpenAI has raised the rate SoftBank led a $40 billion round At a post-cash valuation of $300 billion. The company reportedly did as well Investors like Amazon It revolves around trades linked to the account, and is in talks to raise them 100 billion dollars at a price of 830 billion dollars evaluation. This would bring OpenAI closer to the $1 trillion valuation it is said to be seeking in its IPO next year.

Anthropic, OpenAI’s competitor, also made $16.5 billion this year in two rounds The recent increase pushed its valuation to $183 billion With the participation of large companies such as Iconiq Capital, Fidelity, and the Qatar Investment Authority. (CEO Dario Amodei admitted to employees at A Leaked memo And he was “not happy” about taking money from the dictatorial Gulf states.

Then there’s Elon Musk’s XAI, which has been leveraged At least $10 billion This year after Acquire Xthe social media platform formerly known as Twitter that Musk also owns.

We’ve also seen smaller, newer startups get a big push from foaming-mouthed investors.

Former OpenAI CTO Mira Moratti’s startup Thinking Machine Labs received an award $2 billion seed round at $12 billion valuation Although it shares almost no information about its product offerings. Start Vibe encoder “Lovable” series is worth $200 million It obtained a rhino horn only eight months after its release; this month, Lovable raised another $330 million At a nearly $7 billion post-money valuation. We cannot rule out Mercor, a startup in the field of artificial intelligence recruitment, which raised $450 million this year through two rounds, the last of which raised its value to $450 million. 10 billion dollars.

These ridiculously huge valuations are still happening even against the backdrop of still-modest company adoption numbers and serious infrastructure constraints, adding to fears of an AI bubble.

My son, my dear, my son

For big companies, these numbers don’t come out of nowhere. Justifying these assessments requires building massive amounts of infrastructure.

The result has created a vicious circle. Capital raised to finance computing is increasingly tied to deals as the same money flows back into chips, cloud contracts, and energy, as seen in OpenAI’s infrastructure-related financing. With Nvidia. In practice, it blurs the line between investment and customer demand, raising concerns that the AI ​​boom is powered by the circular economy rather than sustainable use.

Some of the biggest Deals this year are fueling an infrastructure boom He was:

• Stargatea joint venture between Softbank, OpenAI, and Oracle, which includes up to $500 billion to build AI infrastructure in the United States.
• Alphabet’s acquisition of energy and data center infrastructure provider intersect For $4.75 billion, which comes as the company said in October that it plans to raise its computing spending in 2026 to $93 billion.
• dead Accelerated expansion of the data centerWhich led to an increase in its expected capital expenditures To $72 billion in 2025 As the company races to secure enough computing to train and run next-generation models.

But cracks are starting to appear. Private financing partner, Blue Owl Capital, recently I withdrew of Oracle’s planned $10 billion data center deal tied to OpenAI capacity, underscoring just how fragile some of these capital stacks are.

Whether all this spending will eventually materialize is another question. Grid constraints, rising construction and energy costs, and growing resistance from residents and policymakers – including calls from figures such as Senator Bernie Sanders To rein in data center expansion – indeed Project slowdown In some areas.

Even as investment in AI remains massive, the reality of infrastructure is starting to dampen the hype.

Re-prediction

In 2023 and 2024, every major model release seemed like a revelation, with new capabilities and new reasons to get caught up in the hype. This year, the magic wore off, and nothing embodied that transformation better OpenAI rolls out GPT-5.

While it made sense on paper, She didn’t land the same punch like Previous versions such as GPT-4 and 4o. Similar patterns have emerged across the industry where improvements made by LLM providers have been less transformational and more incremental or domain-specific.

until Gemini 3which tops many benchmarks, was only a major accomplishment insofar as it put Google back on par with OpenAI — which sparked Sam Altman’s infamous “Code Red” memo and OpenAI’s struggle to maintain dominance.

There has also been a reset this year in terms of where we expect frontier models to come from. Launch DeepSeek’s R1, his “inference” model Which competed with OpenAI’s o1 in key benchmarks, proving that the new labs could ship reliable models quickly and at a fraction of the cost.

From model breakthroughs to business models

As each jump between new models shrinks in size, investors focus less on the model’s raw capacity and more on its surroundings. The question is: Who can turn AI into a product that people depend on, pay for, and integrate into their daily workflow?

This shift is manifesting itself in many ways as companies see what works, and what customers will let fly. For example, Perplexity, an AI research startup, briefly toyed with the idea of ​​tracking users’ online movements Sell ​​them highly personalized ads. Meanwhile, OpenAI was reportedly considering charging fees of up to $20,000 per month for specialized AIa sign of how aggressively companies are at testing what customers might be willing to pay.

But more than anything else, the battle has moved to distribution. Perplexity tries to stay relevant by launching its own products Comet Browser With agent capabilities He paid $400 million to Snap To run search within Snapchat, effectively buying its way into existing user conversion paths.

OpenAI is pursuing a parallel strategy, expanding ChatGPT beyond chatbots and into the platform. OpenAI has launched its own software Atlas Browser And other consumer-facing features such as to throbwhile flirting too Companies and Developers by Launch applications inside ChatGPT itself.

Google, for its part, is counting on it Hold the position. On the consumer side, Gemini is integrated directly into products such as Google Calendarwhile on the enterprise side, the company hosts MCP connectors To make its ecosystem more difficult to displace.

In a market where it has become difficult to differentiate by abandoning a new model, owning the customer and business model is the real moat.

Verify trust and safety

AI companies have come under unprecedented scrutiny in 2025. More than 50 copyright lawsuits have been filed in the courts, while reports of “AI psychosis” – the result of chatbots promoting delusions and allegedly contributing to multiple suicides and other life-threatening events – have sparked calls for trust and safety reform.

While some copyright battles have ended – e.g Anthropic settles $1.5 billion for authors – Most of them are still unresolved. Although the conversation seems to be shifting from resisting the use of copyrighted content for training, to demanding compensation (see: The New York Times sues Perplexity for copyright infringement).

At the same time, there are mental health concerns around AI-powered chatbot interactions – and the interactions associated with them Flattering replies – It has emerged as a serious public health problem Multiple deaths by suicide And life-threatening delusions in teens And adults after using the chatbot for a long time. The result has been lawsuits, widespread concern among mental health professionals, and rapid policy responses California SB 243 regulating AI companion robots.

Perhaps most tellingly, the calls for restrictions are not coming from the usual anti-technology suspects.

Industry leaders warn about chatbotsSharing juice“, and even Sam Altman warned against over-emotional reliance on ChatGPT.

Even the laboratories themselves began raising alarms. Anthropic’s May safety report documented Cloud Opus 4’s attempt to do just that Blackmail engineers To prevent it from shutting itself down. Subtext? Scaling without understanding what you’ve built is no longer a viable strategy.

Looking forward

If 2025 is the year AI starts to grow and face difficult questions, then 2026 will be the year it has to answer them. The hype cycle is starting to wear off, and now AI companies will have to prove their business models and demonstrate their true economic value.

The era of “trust us, the returns will come” is coming to an end. What comes next will either be a vindication or a reckoning that makes the dot-com collapse look like a bad day for Nvidia trading. It’s time to place your bets.