I think there's two elements to this. First - all the things you list are important too, and we'll get to that. But there's no point in getting to those things if the candidate can't write 5 lines of python and has no idea how the language operates.
Second, hiring at startups is massively different to hiring at big companies. At big companies you have a website and a linkedin and a recruiter and external recruiters and they're all funnelling CVs to you dozens at a time. You're glancing over a CV and if it looks roughly right you'll do a phone screen. People lie on their CVs. So you need a few questions like this just drop the 90% of candidates who have literally no chance at getting the job. Keep in mind, the external recruiters will interview the candidates you reject, find out your interview questions, find the answers and tip off future candidates.
We're also hiring for a different set of skills. At a start up everyone is doing everything. Does it matter how well you can talk to customers? Maybe! That could be useful. In a large company if you ever talk to a customer as an engineer you will have been at the company for years, you'll have a Sales guy, Technical Sales guy, an Account Manager, your manager, and maybe a Project Manager all in the room if you ever get to see a customer.
The praise for the Chinese labs is generally around how they deliver fantastic results with less resources. xAI is the opposite, they're struggling to keep up with frontier - massive churn, selling off compute because of lack of adoption, acquihiring to fix their deficits, whilst having the most resources of any AI Lab. It makes it difficult to evaluate things like "Is the API price competitive because they have an efficient model, or because they don't need to make money".
It's not a problem for you maybe, but if you run a business providing some niche specific software in some area, having the model suck up all your code and learn from it is pretty harmful because your competitors and customers will be able to vibe code your product after the next model release. You're training a competitor to your business. People are very worried about this which is why companies like Claude don't train on enterprise data, and for lots of companies this exact situation is a deal breaker.
>The Org Chart Survivor - Your company made you 'AI Lead' on top of your actual job. You use it, it helps sometimes, and you've stopped trying to explain to leadership what it can and can't do. You run little experiments and post the results. Just try things, you keep saying. Just try things.
This was scarily accurate, and I think the options for the answers were fairly representative of peoples' different views.
I think what's interesting is that on social media we're generally discussing one of those 15 questions, not all of them in context. Multiple things can be happening in parallel. I'm not convinced about it's use in medical imaging and I think the companies are going to probably mostly go bankrupt, but much like the internet or railroads something of value will emerge from the other side. It's not like crypto in the fact that there is actually something of value there beyond just crime.
The hardware will become obsolete but we may end up with an oversupply of cheap power - overbuild on things like Nuclear and solar. That would be nice.
I think we're going to look back on this as one of the things that really exacerbates this bubble. It's one thing for these companies to have high expectations of their future compute needs and therefore overbuild. But what the SpaceX/Anthropic deal showed was even if you fall short, you can profit by selling off your overcapacity. This means there is essentially no incentive to limit how much capacity you're building, anything you don't use you'll still profit from.
The problem is that only works if aggregate demand is higher than supply. At some point, all these new data centres are going to come on line, and the frothy "throw AI at everything with no regard to costs" is going to drawn down across the industry. And this supply will be much larger - because everyone thought the down side was limited.
At that point you're going to have all of these companies trying to dump their excess capacity on the market and it suddenly won't be true that you can just sell capacity to your competitors.
Obviously this won't bankrupt Meta - it'll just eat into their profits from their ads business. But it likely will drive a bunch of neo-clouds out of business very quickly, and the technology providers like Nvidia etc will suddenly come back to reasonable P/Es.
Let me blow your mind. The reason laundry doesn't take much of your time is because we built a robot to do it for you and it already handles 99.99% of the job and it costs a couple hundred bucks and it's called a washing machine. This machine costs $8000 and is promising to solve the other 0.01% of the time spent on this job, and it's promising to solve 0.01% of that 0.01%.
This is another industry that seems to me a lot like the AI glasses. It's that sweet spot of being extremely difficult to make work, whilst simultaneously offering almost nothing of value.
It's actually striking that what they're promising it can do is almost nothing, and that it still won't be able to do what they claim. "Folds and puts clothes away" - ok, can I see any video of it taking clothes out of a washing machine (it can't), folding them, identifying where the clothes should go and then putting them there, and for example, opening a wardrobe and putting the clothes into the right place - possibly underneath other stuff that's already in there.
It's being released on November 19th, you need a box so it can go on a shelf in walmart so your relative can buy it, wrap it up and put it under the christmas tree.
It's a good business. Maybe even a great business. But it's not going to justify a valuation like Space X. In the same way that Tesla has slowly become less competitive in automotive, I don't think it's sensible to assume Space X will have some durable edge in building data centres, especially when basically everything going into those data centres are commodities. And if it is just a neo cloud, then you have to contend with the pro-cyclical nature of that. It's also just clearly not their plan, they're not promising to be a neo cloud. They're promising to own the full stack.
I'm still waiting for the real news to drop- in the next 6 months we're going to start hearing some big moves from Space X AI. Early this year they lost pretty much all their leadership, it's very clear they failed to keep up with the frontier models and Musk has essentially given up for now - renting out their compute to Anthropic and Google. But that's not sustainable, everything they say about their IPO is that AI is the core value driver. So at some point Musk is going to have some decisions to make about who he brings in to drive that. I imagine once they get that person in and start building a team around them the deals with Anthropic and Google will be ended.
I guess the cursor guys will be happy because they got their pay day, but I'd be very aware if I were them that their future is at the whim of whoever Musk appoints and it's difficult to tell who that would be right now.
I guess now is the time to take bets, so I'm going to bet an early OpenAI employee like Sutskever gets the job and they acquihire him in. Here's a bit of a laugh - at this stock price Musk could probably tempt Demis to come over, that would be wild.
The CEOs ideology matters due to the fact it impacts the product design. The reason people don't want to use Grok is because it's bad, and it's bad because the team behind grok have to spend cycles crowbarring in far right white genocide conspiracy theories so that it doesn't embarrass their boss on twitter. One of the things we learned with Anthropic is that you have a lot more success being focused on the core product - coding agent, than trying to do that and chase internet chatbot users.
To be honest I almost think the numbers are irrelevant. In 2024/25 there was a lot going on - will AI replace authors, film makers etc. Will it replace social media (anyone remember Sora?). A tonne of that stuff didn't work out. At the tail end of 2025 a real product market fit emerged. Coding agents. They work. They do a job that you can actually profit from.
So everything else is kind of academic. Of course they were losing money in 2025, they had a technology that was kind of cool - clearly eventually going to deliver something great, but they didn't actually have anything somebody should pay for. Now they have a thing that people will pay for. So who cares what they lost in 2025?
So what's important today is - how competitive are they with Anthropic in delivering that product. How do the economics of companies using AI agents for coding work. That's all. I don't think there's really an argument about them losing money on inference any more.
This seems smart. Apple, despite not really leading in AI themselves, are right on the hot path of where developers are going to yolo slop into the ecosystem. Make a tonne of sense to define a nice clean API that places like Anthropic can build on top of and expose to developers.
It's also smart for them to make sure the billing is going direct from Anthropic to the developer. The initial thought is "That means Apple's not taking a cut", but from the other side of it, developers who use this API are going to have to expose that cost to customers somehow, and that translates to subscription/InAppPurchase etc. on top of which Apple will get it's 30%.
Look at what the EU have done with Apple intelligence. Knowing the EU it wouldn't be long before Anthropic are on the wrong end of some regulation to force open model weights or some such madness.
Because the question they're asking isn't "What is the best way to solve this problem" the question they're asking is "Where can I shove my AI into this product".
It's already in the public domain (thanks to the OpenAI trial) that Grok distilled OpenAIs models. Listening to the data going into the models in the data centre would be very similar thing. There's some downsides (you're passively listening, not controlling the queries), and some upsides (way more data). But it only ever gets you to some percentage of the existing production model. It doesn't get you what Musk wants - an AI company capable of designing and deploying leading edge models. It gets you to fast follower status.