AI labs and money: a survival test or an endless cash burn?

It's time to admit the obvious: today's AI industry looks like a giant party where the guests drink the most expensive champagne, but nobody remembers who actually paid the bill. We've grown used to headlines about new multibillion-dollar funding rounds, but how often do you hear about actual profit? It seems the moment has come to stop judging AI labs by the parameter counts of their models and start looking closely at their bank statements. We built a rating system that answers one simple but deeply uncomfortable question: do you actually plan to make money, or are you just waiting for the singularity?

For a long time, Silicon Valley ran on a "growth first, monetization later" playbook. That strategy worked brilliantly for Google and Facebook, but AI is a fundamentally different beast. Here, every user request costs real, and quite substantial, money in compute. A Google search costs the company a tiny fraction of a cent, while generating a single thoughtful GPT-4 response can cost dozens of times more. That creates a unique and dangerous situation: without a clear, efficient business model, the more popular your product gets, the faster you go broke.

Look at OpenAI. The company has turned from a modest nonprofit into a complex "capped-profit" structure, raised tens of billions from Microsoft, and is now scrambling to justify that investment. Paid subscriptions and the API are just the tip of the iceberg. The main battle is in enterprise, where security and reliability requirements are far higher than those of a regular user who wants to write a poem about their cat. Meanwhile, the cost of training each new model version is growing exponentially, pushing Sam Altman to look for trillions of dollars to build his own chip fabs.

The problem is that many labs still behave like academic institutes disguised as startups. They chase SOTA (state-of-the-art) benchmark results and completely forget that the end customer doesn't care about a 0.5% accuracy gain on MMLU. Businesses need a solution to a specific pain point that costs less than hiring a human or using good old Excel. This is where many "unicorns" start to stumble, offering incredibly powerful but economically pointless tools.

Our rating weighs several critical factors: inference cost, depth of integration into existing business processes and, most importantly, how unique the offering is. If your model is just another wrapper on top of Llama with a slightly tweaked system prompt, you don't have a business. You have an expensive hobby that survives exactly as long as investors believe in the magic of the word "AI." We can see the market starting to split into two camps. The first is made up of those who build infrastructure and actually sell the "shovels" in this gold rush. The second is the visionaries, who promise artificial general intelligence within five years and ask for another couple of billion for GPUs.

History teaches that the survivors are ultimately the ones who know how to count money while building the future, not after it arrives. Investors are already starting to ask uncomfortable questions. Why is Nvidia posting record profits while the companies using its chips are, so far, posting only record losses? That doesn't mean the technology is hollow. It means it's time to grow up. The era of pure hype is ending, and the era of hard operational efficiency is beginning. Labs will have to prove their value not through the number of GPUs in their clusters, but through the margin on every generated token.

The bottom line: the AI industry's romantic period is over. Can OpenAI become the next Microsoft, or will it remain just a very expensive research department kept afloat by the giants?

*Meta has been designated an extremist organization and is banned in the Russian Federation.