“The historical impact of technological progress suggests that most of the metrics we care about (health outcomes, economic prosperity, etc.) get better on average and over the long-term, but increasing equality does not seem technologically determined and getting this right may require new ideas,” Altman wrote. “In particular, it does seem like the balance of power between capital and labor could easily get messed up, and this may require early intervention.”
Solutions to this problem, like Altman’s “compute budget” concept, may be easier to propose than execute. Already, AI is impacting the labor market, resulting in job cuts and departmental downsizing. Experts have warned that mass unemployment is a possible outcome of the rise of AI tech if not accompanied by the right government policies and reskilling and upskilling programs.
Not for the first time, Altman claims that artificial general intelligence (AGI) — which he defines as “[an AI] system that can tackle increasingly complex problems, at human level, in many fields” — is near. Whatever form it takes, this AGI won’t be perfect, Altman warns, in the sense that it may “require lots of human supervision and direction.”“[AGI systems] will not have the biggest new ideas,” Altman wrote, “and it will be great at some things but surprisingly bad at others.”
But the real value from AGI will come from running these systems on a massive scale, Altman asserted. Similar to OpenAI rival Anthropic’s CEO, Dario Amodei, Altman envisions thousands or even millions of hyper-capable AI systems tackling tasks “in every field of knowledge work.”
One might assume that’ll be an expensive vision to realize. Indeed, Altman observed that “you can spend arbitrary amounts of money and get continuous and predictable gains” in AI performance. That’s perhaps why OpenAI is reportedly in talks to raise up to $40 billion in a funding round, and has pledged to spend up to $500 billion with partners on an enormous data network.
Yet Altman also makes the case that the cost to use “a given level of AI” falls about 10x every 12 months. In other words, pushing the boundary of AI technology won’t get cheaper, but users will gain access to increasingly capable systems along the way.
Capable, inexpensive AI models from Chinese AI startup DeepSeek and others seem to support that notion. There’s evidence to suggest that training and development costs are coming down, as well, but both Altman and Amodei have argued that massive investments will be required to achieve AGI-level AI — and beyond.