That isn't true. It's not continuous like in humans, but it's clear that models are using prompts, feedback, etc to improve. They're learning from the signals we give them between versions.

You get a much better response if you ask the right questions in the right language though. Expertise still has value.

I guess you just asked a librarian and hoped they'd point you roughly towards the right section and then just started to read random books?

You looked it up in an encyclopedia first, which gave you the basics. That gave you enough to find it in a library using the Dewey Decimal code. From there you could generally trust the book because they tended to be trusted if they were in a library. For newer topics you swapped books for journals.

Researching a niche topic as someone outside of the academic community with no journal access was basically impossible.

I guess the same people were surprised when Uber started raising their prices too.

   * Not Nigel Farage

You may choose to ignore that and believe someone is selling you something for $5 but that doesn't make it true.

In the case of Temu, they're selling it for $5 of your money, and $5 of someone else's money, in order to capture you as a customer in the hope that you continue to buy things when they increase the price to $6, then $8, then $10, $15, $30 and so on in the future.

This is the Uber playbook, and if you have a solid product that people want and you can spend VC money long enough to build a trusted brand (or ideally keep prices artificially low until the competition gives up and you have a monopoly) it's very lucrative. I don't see it working for Temu myself. They sell things people don't need so as soon as the prices go up they'll probably fail. That said, Amazon see them as enough of a threat to launch Haul, so what do I know?

TEMU doesn't sell those things. They're more expensive than that, but subsidised by VC capital from PDD Holdings, Blackrock, Vanguard, etc. It's a very boring 'spend the VC bucks to get the customers' play, and when they've got the customers they'll jack up the prices and lose the customers again. It's Uber-for-cheap-stuff basically.

So rather than ask "What is the impact of AI?" which narrows your focus to the impact of AI, if you really care about the natural resources you should be asking "What has a big impact on natural resources?"

If that's AI, and you're ignoring all the things that have a far greater impact on what you claim to care about, then you should ask if you're really just looking for things to complain about around AI, and natural resources are a convenient excuse.

For example, the largest data centre in the US uses approximately the same amount of water as 2 burger restaurants if you include the water used to raise the beef used in the burgers. (https://newsletter.semianalysis.com/p/from-tokens-to-burgers...)

If you care about water resources and you're not a vegan campaigning against the beef industry, but you're focusing on AI, then you have a degree of cognitive dissonance.

From an environmental perspective we should ban golf and use the land for literally anything else*.

* maybe not data centers.

Everything has a downside attached. Most things are terrible for the environment if you look at the raw numbers, and if you're not interested in them or they're things you don't use then it's easy to just dismiss them as pointless nonsense that we should stop doing.

However, if you're only looking at the downside then you're being dishonest. The point is never "Let's stop this because it's bad for the planet", but "Is the net benefit worth the cost?"

That could still be a no for AI, but there are things we ought to stop doing long before we come for AI. Notably golf. It's a terrible waste of land and water, and boring.

I don't see that as a product video that's telling me why I need one. It's just telling me that they've iterated on the last model. I mean, congrats, well done, but why should I buy it?

That's what you'd get if you asked a leading physicist too, so are you saying LLMs have achieved human-level intelligence?

Engineers can also be axed at any time for performance. They're fortunate that most companies are bad at measuring that, but they're also subject to things like stack ranking at very bad companies (fire worst 10% every year) so it's certainly not better to be in eng rather than sales.

Ultimately all roles have aspects of them that suck, so you need to find the one that sucks least for you.

That isn't true. Early sales employees ('customer success', 'technical sales', 'growth', maybe product roles) get just as much equity as engineers who join at a similar time. The difference is that engineers often join earlier, with the commensurate risk that comes with.

Also equity rarely pays out so you'd need to be comparing the probability of an exit that actually rewards the share class that engineers get, whether or not they've been diluted to nothing, whether there's a secondary market to sell on before an exit event, etc. It also depends on whether someone even wants the potential reward equity gives them over the more tangible rewards of money and perks.

Comparing this stuff is hard.

The point here though, is that the company is rewarding sales people at a time when the product is doing poorly, which implies the leadership team care more about selling a bad product than turning it into a good product. I hope that's not the case because it used to be a good platform.

There are two incentives for contributing to open source. The first is to make the app better (add a feature, improve the code, fix a bug, etc.) It's possible that LLMs don't meet the bar but if a human has put the effort in it's not always obvious that it's AI. More likely LLM code is accepted when it's good and rejected when it's bad. The system works.

However, the second incentive is that open source contributions are seen as a 'ahortcut' to making a name for yourself. Being a contributor on a big project goes on resumes and GitHub profiles. Often people who use AI for that don't review the code or even check it does what they say it does. That slop needs to be banned. The downside is losing the good contributions, but it's still a net win.