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It doesn't reveal that they have no clue of what they're talking about. They know that a good number of idiot CEOs will hear this and dictate their engineers spend more claude/codex money, which benefits them.

Jensen isn't an idiot, he's capitalizing on the market.


> They know that a good number of idiot CEOs will hear this

This is true regardless of the specific jargon used, though. Or are you saying that idiot CEOs will not be impressed by correct claims only stupid claims?


It doesn't reveal they don't have no clue either - the problem is that both "Doesn't actually understand" and "Does understand but saying it because they think it'll benefit their products" look exactly the same from the outside.

All we know is he's saying things that are technically incorrect. And I'm not sure where that falls on the line of "Just Acceptable Marketing BS" to "Lying to the market about a Publicly Traded company" that would fall if he does understand it.

And while true he very much had deep technical education and roles, he's been C-level for over 30 years now. And a lot can change - both in the underlying technology and an individual's technical understanding - in 30 years.


He might be, but this sounds suspiciously like "Trump is playing 4D chess and trolling the media - he would never actually do the dumb thing he just suggested he was about to do."

The joke is on everyone else, our favorite leader is just pretending to be an idiot!

I mean you're kind of attributing this to malice when its actually incompetence. Nvidia doesn't need any shilling by its CEO to increase its sales or capitalize on the market. It's purely banter from his pov, not some super intelligence spectre ploy to sell more GPUs.

Minimax 2.7 is fine for most web stuff. It's slightly worse than Claude at backend, but works great for frontend.

They're all slop when the complexity is higher than a mid-tech intermediate engineer though.


> They're all slop when the complexity is higher than a mid-tech intermediate engineer though.

This right here. Value prop quickly goes out the window when you're building anything novel or hard. I feel that I'm still spending the same amount of time working on stuff, except that now I'm also spending money on models.


10x more code output is 10x more review.

We've gone from doing the first 90% and then the second 90% to the first 90% and the second 990%, its exausting.


Kimi is surprisingly good at Rust.

Must be nice working on simple stuff.

Several fintechs like Block and Stripe are boasting thousands of AI-generated PRs with little to no human reviews.

Of course it's in the areas where it doesn't matter as much, like experiments, internal tooling, etc, but the CTOs will get greedy.


I don't think anybody is doubting its ability to generate thousands of PR's though. And yes, it's usually in the stuff that should have been automated already regardless of AI or not.

Depends on your circle. On HN I would argue that there are still a fair number of people that would be surprised to see what heavy organizational usage of AI actually looks like. On a non programming online group, of which I am a member of several, people still think that AI agents are the same as they were in mid 2025 and they can't answer "how many R's are in the following word:". Same thing even when chatting with my business owner friends. The majority of the public has no clue of the scale of recent advancement.

Not arguing, but I just prompted Opus with a made up word and it responded with this:

“There are 4 Rs in the word “burberrorrly.” Here they are highlighted: burberrorrly (positions 3, 6, 7, 9)”

Obviously not a real word, but perhaps the fundamental concept remains


these companies contribute to swathes of the west's financial infrastructure, not quite safety critical but critical enough, insane to involve automation here to this degree

Law enforcement ICs do not have public records.


IC?


AI coding is gambling on slot machines, managing developers is betting on race horses.


Only if your AI coding approach is the slot machine approach.

I've ended up with a process that produces very, very high quality outputs. Often needing little to no correct from me.

I think of it like an Age of Empires map. If you go into battle surrounded by undiscovered parts of the map, you're in for a rude surprise. Winning a battle means having clarity on both the battle itself and risks next to the battle.


Good analogy! Would be interesting to read more details about how you’re getting very high quality outputs


It's basically (1) don't make decisions for the LLM - force it to think (2) make it explore extensively (3) make it pressure test it's own thinking.


Would you mind sharing some of your findings?


Until it produces predictable output, it's gambling. But it can't produce predictable output because it's a non-deterministic tool.

What you're describing is increasing your odds while gambling, not that it's not gambling. Card counting also increases your odds while gambling, but it doesn't make it not gambling.


This is a pretty wild comparison in my opinion, it counts almost everything as gambling which means it has almost no use as a definition.

The most obvious issue is it’d class working with humans as gambling. Fine if you want to make that as your definition but it seems unhelpful to the discussion.


You seem to have a fundamental issue understanding what the term deterministic even means.

If you give the same trivial task to the same human five times in a row, let's say wash the dishes, your dishes are either gonna be equally clean or equally not clean enough every time. Hell, it might even get better over time by giving them feedback at the end of the task that it can learn from.

If you run the same script five times in a row while changing some input variables, you're gonna get the same, predictable output that you can understand, look at the code, and fix.

If you ask the same question to the same LLM model five times in a row, are you getting the same result every time? Is it kind of random? Can the quality be vastly different if you reject all of its changes, start a new conversation, and tell it to do the same thing again using the exact same prompt? Congrats, that's gambling. It's no different than spinning a slot machine in a sense that you pass it an input and hope for the best as the output. It is different than a slot machine in a sense that you can influence those odds by asking "better", but that does not make it not gambling.


Deterministic doesn’t mean “generally pretty predictable, in broad strokes”.

> If you give the same trivial task to the same human five times in a row, let's say wash the dishes, your dishes are either gonna be equally clean or equally not clean enough every time.

Probably pretty similar but not quite the same. Sometimes they might drop a plate.

> If you ask the same question to the same LLM model five times in a row, are you getting the same result every time?

Probably pretty similar results. Sometimes they might mess up.

> It is different than a slot machine in a sense that you can influence those odds by asking "better", but that does not make it not gambling.

It rather can, we don’t call literally anything with a random element to the outcome gambling.

I’m probably gambling with my life if I pick a random stranger to operate on me. Am I gambling with my life if I take a considered look at the risk and reward and select a highly qualified surgeon?

Is it gambling to run a compiler given that bitflips can happen?

At what point does the word lose all meaning?


How does it 'count almost everything as gambling'? They just said 'non-deterministic' output is gambling-like, that is not 'almost everything'. Most computation that you use on a day-to-day basis (depending on how much you use AI now I suppose) is in all ways deterministic. Using probabilistic algorithms is not new, but it your point is not clicking...


Working with humans is decidedly not deterministic, though. And the discussion here is comparing AI coding agents and humans.


That starts to get into a very philosophical space talking about human action as deterministic or not. I think keeping to the fact that the artifacts (ie code) we are working off will have deterministic effects (unless we want it not to) is exactly the point. That is what lets chaotic human brains communicate with machines at all. Adding more chaos to the system doesn't strike me as obviously an improvement.


Almost everything is non deterministic to some degree. Huge amounts of machine learning, most things that have some timing element to them in distributed systems, anything that might fail, anything involving humans, actual running computation given that bitflips can happen. At what point does labelling everything that has some random element “gambling” become pointless? At best it’ll be entirely different to how others use the term.


Similar to quantum computing, a probabilistic model when condensed to sufficiently narrow ranges can be treated as discrete.


Every engineer I've ever worked with is non-deterministic, too.


Dam this is so accurate. As a project manager turned product manager this is so true. You need to estimate a project based on the “pedigree” of your engineers


Would it make us uncomfortable to reword the above example to

> AI coding is gambling on slot machines, managing developers is gambling on the stock market.

Because I feel like that is a much more apt analogy.


What is it with you guys and stallions?


There is a long history of managers just wanting to work their developers like horses.


Great analogy, I’m saving it!


It's not only hard and painful, but potentially damaging to the woman's body and could leave them with permanent hormonal issues.

So, no. It's not a good policy.


Gemini is a generalist model and works better than all existing models at generalist problems.

Coding has been vastly improved in 3.0 and 3.1, but Google won't give us the full juice as Google usually does.


My guess is that Google has teams working on catching up with Claude Code, and I wouldn't be surprised if they manage to close the gap significantly or even surpass it.

Google has the datasets, the expertise, and the motivation.


It really depends on the human and the code it outputs.

I can get my 2y old child to output 100k LoC, but it won't be very good.


Your 2yr old can't build a C compiler in Rust that builds Linux.

Sorry mate, I think you're tripping.


I never said this. I think you're the one tripping mate.


The thing is that there is only a tiny portion of houses ever selling at any time. The only ones that would benefit from selling are people who are downsizing or moving away.

If you buy 60% of the properties on market, the rest will see this and adjust their own prices. Usually this only works when the macro is favourable (low interest rates, easy mortgage applications, etc.), but it is definitely a large factor. It sometimes creates a even hotter market, with people thinking that real estate goes up forever, then they sell.

You're right that it's not always large investment groups. Vancouver in Canada had the same thing happen, but mostly from foreign investors and criminals washing money. The latter was facilitated by politicians who cashed in big on this.


I don’t understand the last sentence in your second paragraph. If “people [think] that real estate goes up forever” why does that trigger them to sell?


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