As someone who's always loved synthesizing ideas and having lightbulb moments, I find the headline flattering. I wonder, though, if there's any more rigorous and general analysis (hah!) of the complexity of these two modes of thought.
EDIT: Fable 5 turned up some relevant references:
[1] Richardson, D. (1968). "Some Undecidable Problems Involving Elementary Functions of a Real Variable." Journal of Symbolic Logic, 33(4). — Differentiation is a simple recursive algorithm; deciding integrability in elementary terms is undecidable in general.
[2] Risch, R. H. (1969). "The Problem of Integration in Finite Terms." Transactions of the American Mathematical Society, 139. — The partial recovery: a (semi-)decision procedure for a restricted class.
[3] Guilford, J. P. (1967). The Nature of Human Intelligence. McGraw-Hill. — The divergent vs. convergent thinking distinction, the classic psychometric cousin of synthesis vs. analysis.
[4] Anderson, L. W., & Krathwohl, D. R. (Eds.) (2001). A Taxonomy for Learning, Teaching, and Assessing (revision of Bloom's taxonomy). Longman. — Moved "Create" (synthesis) to the top of the cognitive hierarchy, above "Analyze."
[5] Aaronson, S. (2011). "Why Philosophers Should Care About Computational Complexity." arXiv:1108.1791. — Argues complexity asymmetries (verification vs. generation, P vs. NP) bear directly on questions about cognition.
To my knowledge, Pangram has a very low false positive rate approaching zero. (False negative is another matter.) I’m not sure that’s what you want to use as the analogy here? (I don’t know much about this Kramnik situation.)
Quite frustrating to see all these cynical, borderline-irrational comments on HN. Maybe I should do what pg and other ex-HN contributors have done--avoid taking part in the discussions here.
The level of discourse here has dropped so much.
At least I should stop replying to people hiding under throwaway accounts.
Demis' bar is high and he stated clearly multiple times: AGI should be capable of inventing truly novel things. Examples he gave included the Theory of General Relativity and the game of Go.
Anthropic's latest model also solved this 80-year-old problem that eluded many expert mathematicians, in a different way, according to one of its employees.
No one rational ever said software engineers were going to be replaced in 6 months. Some people said AI will automate 90% of coding in 6 months and they were not far off (and accurate in some contexts, e.g. startups).
"Ten years from now, I think we will realize that we were standing in the foothills of the singularity now... I believe that we're only a few years away from [AGI], maybe 2030 plus or minus a year...
I think [AGI] will be an enormous transformative technology, it's going to effectively be a new human era...
We can feel this year, I would say, even though I've been working towards this for 30 years, I think this year with the way the agents are working and tool use, it started to become really useful, still early days of it, but genuinely useful in people's workflows...
And it's not any one thing, it's several different technologies, several use cases, several things that I thought were maybe a bit further out, turned out to be now, that are coming together that make me feel that in aggregate.
I think society needs to hear that because we don't have long to prepare for what that means." -- Demi Hassabis, CEO of Google DeepMind & Nobel laureate
"Ten years from now, I think we will realize that we were standing in the foothills of the singularity now... I believe that we're only a few years away from [AGI], maybe 2030 plus or minus a year...
I think [AGI] will be an enormous transformative technology, it's going to effectively be a new human era...
We can feel this year, I would say, even though I've been working towards this for 30 years, I think this year with the way the agents are working and tool use, it started to become really useful, still early days of it, but genuinely useful in people's workflows...
And it's not any one thing, it's several different technologies, several use cases, several things that I thought were maybe a bit further out, turned out to be now, that are coming together that make me feel that in aggregate.
I think society needs to hear that because we don't have long to prepare for what that means." -- Demi Hassabis, CEO of Google DeepMind & Nobel laureate
Things in the real world often take longer than expected. Still, in cities where Waymo operates, many people routinely ride autonomous vehicles and prefer them.
For software, however, a rapid turn is often a possibility. See: AI for coding over the last 3-4 years.
AI autocomplete --> AI coding assistants --> vibe coding --> agent orchestration
Coders can now accomplish work that used to take a week or longer in a couple of hours, with the right tools and skills.
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A key issue the article implies is that the real world increasingly runs on software.
I have watched Dario’s interview at WEF referred to in the article and I am quite certain Dario didn’t say that. He talked about AI automating most coding already or soon, not software engineering as a whole.
He did say a few months later in an interview in India that AI will eventually take over most of SWE tasks.
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My statement on startups is largely about automating coding by SWEs. My startup also uses AI to automate part of technical specifications and code review but I am not sure how widespread that is.
This is already the case for many startups. In fact, the figure might be closer to 100%. The work shifts to requirements analysis, high-level specifications, and final review instead (after AI code review).
I assume you're using the "regular" Pro version of Gemini 3.1 for the above, rather than the Deep Think mode, which is more comparable to GPT-5.5 Pro. To my knowledge, regular 3.1 Pro is a tier below and often makes mistakes.
Moreover, there's no reason to believe the progress of LLMs, which couldn't reliably solve high-school math problems just 3–4 years ago, will stop anytime soon.
You might want to track the progress of these models on the CritPt benchmark, which is built on *unpublished, research-level* physics problems:
Given the capabilities of upcoming LLMs, I suspect that by mid-2027, most competent companies, outside specific niches, will not hire and might fire any non-senior “generative AI vegetarian” software developer.
Note: I agree with others that another term should be used instead of ‘vegetarian’. “LLM vegetarians” do not hold the same moral values as vegetarians.
“A good way to describe myself is as a generative AI vegetarian. You can find a fuller explanation—and many, many links—at the above essay by Sean Boots, which I agree with almost 100%.”
—-
Given the capabilities of upcoming LLMs, I suspect that by mid-2027, most competent companies, outside specific niches, will not hire and might fire any non-senior “generative AI vegetarian” software developer.
The combinatorial nature of trying things randomly means that it would take millennia or longer for light-speed monkeys typing at a keyboard, or GPUs, to solve such a problem without direction.
By now, people should stop dismissing RL-trained reasoning LLMs as stupid, aimless text predictors or combiners. They wouldn’t say the same thing about high-achieving, but non-creative, college students who can only solve hard conventional problems.
Yes, current LLMs likely still lack some major aspects of intelligence. They probably wouldn’t be able to come up with general relativity on their own with only training data up to 1905.
Neither did the vast majority of physicists back then.
They probably want to select for high-quality ads without having to be responsible for filtering issues, whether false positive or false negative, which will adversely affect their reputation with consumers and advertisers. They probably wait until they have enough data/experience to do that properly.
EDIT: Fable 5 turned up some relevant references:
[1] Richardson, D. (1968). "Some Undecidable Problems Involving Elementary Functions of a Real Variable." Journal of Symbolic Logic, 33(4). — Differentiation is a simple recursive algorithm; deciding integrability in elementary terms is undecidable in general.
[2] Risch, R. H. (1969). "The Problem of Integration in Finite Terms." Transactions of the American Mathematical Society, 139. — The partial recovery: a (semi-)decision procedure for a restricted class.
[3] Guilford, J. P. (1967). The Nature of Human Intelligence. McGraw-Hill. — The divergent vs. convergent thinking distinction, the classic psychometric cousin of synthesis vs. analysis.
[4] Anderson, L. W., & Krathwohl, D. R. (Eds.) (2001). A Taxonomy for Learning, Teaching, and Assessing (revision of Bloom's taxonomy). Longman. — Moved "Create" (synthesis) to the top of the cognitive hierarchy, above "Analyze."
[5] Aaronson, S. (2011). "Why Philosophers Should Care About Computational Complexity." arXiv:1108.1791. — Argues complexity asymmetries (verification vs. generation, P vs. NP) bear directly on questions about cognition.