Opus was pretty decent at that before the whole Fable drama.
In fact, for our work, which is absolutely not rocket science most of the time, we see little upside in using Fable. The output is still "good enough," but the token burnout rate is through the roof. For many tasks, we stick to Opus, or even Sonnet, and the output is just fine.
Fun fact, we recently prepared an AI recruitment task. The basic idea was that there are conflicting goals in the context. We assumed that AI would lead candidates to a dead end, and they'd have to figure out what was happening. We abandoned the idea as even Sonnet was handling it fine enough. And it burned way fewer tokens than Fable would.
We also find that the agents tend to find false positives. Especially when it comes to security. In theory, you can get an automated pen test every week. In practice, it drowns you in triaging reported vulnerabilities, as you find a significant part of them aren't something that you'd like to address, like ever.
Gemini: The article focuses on the environmental and human labor costs of scaling Artificial Intelligence, specifically focusing on water usage, electricity, and "ghost work."
Which is hilarious, since the article doesn't even mention the words "water" or "electricity." Gemini remains unfazed, reporting the links that are not in the article (some don't exist at all) to make the final ruling: "The Tech Trenches document is highly accurate in its citations."
Now, I know. Had I used Claude Code with relevant skills, it would have done better. But would it be good?
That thought crossed my mind. However, for such a product to work, there would have to be a human in the loop. With data-starved edge cases, which are many in fact-checking landscape, it would be relatively easy for an LLM to make stuff up or mislabel the context (which it inherently does not understand).
Also, thorough validation would cost a ton in tokens. So it would be both expensive from the tech perspective (AI bills) and labor. Now, whose interest would be to fund such a product? I don't see too many takers...
Um, is Snopes wrong about city cleaning crows, though? As that was the context of the original post. Which, by the way, doesn't say "Go, trust Snopes with everything; they can't be wrong!"
Fundamentally, yes, it is a different "search engine."
BTW, as critical as I can be to AI, using an argument that something didn't work 3 years ago, so it must be crap, doesn't work in this context. 3 years ago, AI could barely generate several lines of consistent code. Now, it generates working apps with a prompt (it's another discussion how good the code is, but still).
I guess 3 years ago, Gemini couldn't tell how many r's are in the word refrigerator.
Same for research. At some point, I switched from ChatGPT and Gemini to Perplexity as it promised AI-powered search. It worked visibly better. Until it didn't, as GPT and Gemini models made a leap.
Back to the point, as long as we understand that, for now, it's all just a probabilistic machine generating the most likely output, no one should expect bulletproof answers. Search was/is way more deterministic than LLMs.
Ultimate credibility? Sure, they never did. Yet the whole thing Google was built upon was using links as tokens of credibility.
You'd assume an outgoing link from a CNN website has more credibility than one from an anonymous blog. That is, I reckon, still true. Although the credibility either link conveys is degrading. Again, it has been so since we started playing the game of SEO, yet AI-generated content in this context is basically a weapon of mass destruction. The deterioration has sped up dramatically.
There's nuance to that. An LLM is quite capable of suggesting relevant reading, given the context. Especially when the context is broad enough that there's enough training data.
"Find me research on code reviews, their size, and quality" would give you more than enough reading. Yet, if you start with a claim, like "Longer PRs mean worse defect detection," the relevant data points fall to few enough for AI to start hallucinating.
You get "something, something, PR length, defect detection, IDK, I don't read research papers." Such output is fine as long as the author cares to validate it.
Skip the second step, and you might be good if you ask about something generic, like "What's the Slack story?" or "How did Blockbuster go bust?" Ask about some specific details, though, and you're bound to end up with made-up stuff that sounds just about right, while it's actually wrong.
Everything else being the same, more resources are better than less. Yet, VC money comes with strings attached.
VC doesn't want a startup to become just a healthy business. It needs to grow at a breakneck speed. In fact, for a VC, it's better to put pressure on somewhat successful startups to take a moonshot at becoming unicorns, even at the grave risk of going bust instead.
The expectation to spend the funding round in 12-18 months is a well-established pattern. So you get millions, but you have to spend it fast.
Running a product development consultancy, I routinely see products/businesses that could have been built for a fraction of what they cost. You don't need to hire hundreds of developers (pre-2026) and instantly have huge misalignment and coordination issues. You don't need to tokenmaxx the crap of everything (2026), ballooning your AI spend and generating a ton of bloat. That is, unless someone pressures you to spend fast because it's their shot at you becoming a unicorn.
"Even without AI-generated code, [code review] is already a major failing."
By all means, yes. Yet, it feels like we were playing a catch-up game (to a degree), and no one intentionally shipped unreviewed code. Now, reviewed without comprehension becomes standard, unreviewed & unread increasingly happens. That's a different kind of reckless.
"Open source adds thousands of (unpaid) eyes to code review."
True. And the open source community sees a massive inflow of AI-generated pull requests, which floods their capabilities to review. Leaving the ecosystem as it is means it will be dead. Thus, I assume resistance or evolution. And we definitely see some of the former, with some open source codebases being closed for AI contributions.
"Hiring is now 'My AI versus Your AI; and the former real need of 'A qualified person for a suitable job' is lost in the fallout."
Yes, that's where hiring has headed. Which, coincidentally, has made everyone worse off (save for AI-for-hiring apps providers). Candidates have it harder to land a decent job. Companies talk to people who play the AI hiring game better, not the most suitable candidates. All while having the same number of candidates and the same number of jobs, but 100x as many resumes exchanged: https://brodzinski.com/2025/08/broken-ai-hiring.html
Which basically means that a resume has lost its value as a token of information exchange. And since we base the whole process on this very assumption (resume as a token of information), the system is due to be rewired eventually. And sooner rather than later. One random idea: how about creating limited traffic where people actually care at least enough to pay some token money: https://brodzinski.com/2025/12/pay-for-resume-read.html
"My humble suggestion is that our ultimate question be phrased as 'How much is enough?'"
Perfect question if we start from the grand scheme of things. I am afraid, though, that there is never enough. At some point, another billion means increased status. You could buy everything with the billions you had previously, so right now it's a virtual leaderboard between you and other billionaires. And the status game is, indeed, infinite. If you aren't winning now, you can chase the leader. If you are the leader, you try to escape the chase.
The "enough" question doesn't work just as well in a finer-grained context. If we want to figure out things like the evolution of a specific profession. Or consider how digital products will be built in the future. Or how well outsourcing your content generation to an AI agent would work in the long run.
Yes. And the more autonomously we create code, the more of these (and not only these) vulnerabilities we'll be adding. Combine that with the AI-automation in attacks, and you have an all-out security mess.
It's like a Petri dish for inventing new angles of security attacks.
Oh, and let's not forget that coding agents are non-deterministic. The same prompt will yield a different result each time. Especially for more complex tasks. So it's probably enough to wait till the vibe-coded product "slips." Ultimately, as a black hat hacker, I don't need all products to be vulnerable. I can work with those few that are.
Security is even a bigger issue than it looks at first glance. While security risk by omission was always a thing (AI or not), now we face a whole new level of risks, from prompt injection to creating malicious libraries to be used by coding agents: https://garymarcus.substack.com/p/llms-coding-agents-securit...
The most shallow security, however, seems easier. Now, you can get through an automated AI security audit every day for (basically) free. You don't have to hire specialists to run pen tests.
Which makes the whole thing even more challenging. Safe on the surface while vulnerable in the details creates the false sense of safety.
Yet, all these would be a concern only once a product is any successful. Once it is, hypothetically, the company behind should have money to fix the vulnerabilities (I know, "hypothetically"). The maintenance cost hits way earlier than that. It will kick in even for a pet personal project, which is isolated from the broader internet. So I treat it as an early filter, which will reduce the enthusiasm of wannabe founders.
The question is not whether we like or want subscriptions, but rather whether we're used to them. And the answer is yes.
Given the choice, we'd be using Spotifys and Netflixes for free, and have ad-free Google. I don't expect that choice to be given to us.
AI tools won't change anything on that account. At best, we'll switch one subscription for another one, except that the latter will add a bill for the tokens we use.
There's a huge difference between nurses or teachers and Ivy League students. Namely, the former are not remotely as prestigious roles. I highly doubt there are 20 candidates for each nurse or teacher job.
Affirmative action happens when we discuss privileged positions. Spots at Ivy League colleges definitely are positions of privilege.
So if the situation under consideration were nursing, there wouldn't be such a discussion because there wouldn't be affirmative action in place.
> do Altman and Andreesen really believe that, or is it just a marketing and investment pitch?
As for Andreessen, I don't think he even cares. As the author writes:
"for the venture capitalists that have driven so much of field, scaling, even if it fails, has been a great run: it’s been a way to take their 2% management fee investing someone else’s money on plausible-ish sounding bets that were truly massive, which makes them rich no matter how things turn out"
VCs win every time. Even if it's a bubble and it bursts, they still win. In fact, they are the only party that wins.
Heck, the bigger the bubble, the more money is poured into it, and the bigger the commissions. So VCs have an interest in pumping it up.
Can they, fundamentally, do that? That is, given the current technology.
Architecturally, they don't have a concept of "not knowing." They can say "I don't know," but it simply means that it was the most likely answer based on the training data.
> We've got something that seems to be general and seems to be more intelligent than an average human.
We've got something that occasionally sounds as if it were more intelligent than an average human. However, if we stick to areas of interest of that average human, they'll beat the machine in reasoning, critical assessment, etc.
And in just about any area, an average human will beat the machine wherever a world model is required, i.e., a generalized understanding of how the world works.
It's not to criticize the usefulness of LLMs. Yet broad statements that an LLM is more intelligent than an average Joe are necessarily misleading.
I like how Simon Wardley assesses how good the most recent models are. He asks them to summarize an article or a book which he's deeply familiar with (his own or someone else's). It's like a test of trust. If he can't trust the summary of the stuff he knows, he can't trust the summary that's foreign to him either.
What's the lifecycle length of GPUs? 2-4 years? By the time OpenAIs and Anthropics pivot, many GPUs will be beyond their half-life. I doubt there would be many takers for that infrastructure.
Especially given the humungous scale of infrastructure that the current approach requires. Is there another line of technology that would require remotely as much?
Note, I'm not saying there can't be. It's just that I don't think there are obvious shots at that target.
One more interesting aspect: the infrastructure doesn't age that well. We basically need to renew all that infrastructure every, like, 2-4 years or so? (And I think I'm being optimistic here.)