James Titcomb
Diogo Almeida was one of the fathers of ChatGPT, but he believes his creation has failed to deliver.
βItβs not leading to an AI-based economic revolution, which is what the early people really wanted,β he told an audience earlier this year.
Rather than simply lament his failure, Almeida has done something about it. In the last week, Jev β his low-cost AI system β has become a viral sensation, prompting rumours of a $US10 billion ($14.4 billion) valuation, as well as a string of copycats.
Jevβs developer, TypeSafe, says the system is both hundreds of times cheaper and faster than the βfrontierβ models developed by OpenAI and Anthropic, and designed for use by machines, rather than humans.
Almeida β recognisable by a trademark pink fluffy coat that he says he purchased on Temu for $US20 β believes Jev could displace ChatGPT and Claude, and upend the two trillion-dollar companies.
He is now at the vanguard of a wave of low-cost AI systems that, if successful, could rewrite the rules of the booming AI industry.
Almeida, a maths genius raised in Manila in the Philippines, worked at the Google Brain AI lab before joining OpenAI, interrupted by an 18-month sabbatical β much of which he spent playing fantasy video games. At OpenAI, he pioneered the training technique known as reinforcement learning from human feedback, in which bots improve based on how humans rate their answers.
The procedure was used to create ChatGPT, which launched in 2022 as the first AI system able to hold a conversation and answer questions.
But chatbots like ChatGPT, which produce results in text, place extraordinary demands on computing resources. βLanguage modelsβ such as ChatGPT or Claude produce complex strings of text to reason through questions, and queries can take several seconds or even minutes for a response.
Humans might be willing to wait a few seconds for a chatbot answer. But increasingly, AIβs users are other machines: a customer service system deciding where to route email complaints; an automated recruitment system screening CVs; or a bankβs compliance software assessing transactions for fraud. For these cases, tolerance for delay is lower.
Jev, released this month, produces answers in nanoseconds. Instead of answering with words, it responds with numbers, for example assigning a probability that a bank payment is fraudulent. This makes it orders of magnitude faster and cheaper than rival systems.
At a time when corporate customers are seeking to rein in staff spending on AI systems, TypeSafe says Jev delivers results 194 times faster and 445 times cheaper than the best OpenAI and Claude systems. One demo shows the model working fast enough to control the video game Doom. In the real world, the concept could be applied to controlling robots fast enough for them to become useful.
Since the system was released, incessant demand has forced it to suspend sign-ups, and reports have suggested the companyβs $US200 million valuation could rise to $US10 billion. Almeida has said investors are βbattering down our doorβ.
Vercel, a company that provides software development tools, says Jev became the fastest-adopted AI model in its history after launch.
Jevβs own staying power is up for debate. Within days, a cluster of copycat systems has emerged built on the same idea (one, unimaginatively, is called βKevβ). But if a swath of AI usage moves from expensive frontier systems to fast, budget models, it could puncture the investment case for OpenAI and Anthropic, both of which are planning to go public in the coming months.
HSBCβs analysts said that the system had the potential to deliver a market sell-off similar to the DeepSeek market crash at the start of last year, which sent shares in Nvidia, AMD and Google tumbling on fears that it would lead demand for computing resources to crater.
βThe prospect of a new class of AI models that are much cheaper and faster could lead to a resurgence of market fears about AI monetisation,β they said, although they added that those fears would be misplaced.
Almeida has predicted that his system will lead to a massive expansion in AI usage. Jev comes from the Jevons paradox, the phenomenon named after the British economist William Stanley Jevons, who observed that when coal use became more efficient, demand for it went up.
If the same is true when it comes to AI, then Jev could keep the boom alive. The expensive models it replaces might be collateral damage β Almeidaβs earlier creation among them.
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