Engineering has always been about more than writing code.
That's true, but it's interesting how FizzBuzz as said to be the bete noir of the average dimwitted software developer, and how much cutting-edge engineering organizations used to emphasize code in their recruitment processes.
If writing code is being replaced by "engineering judgement" it's going to need a much smaller cohort of developers. Too many opinions spoil the broth, after all.
I suspect that the goalposts for AI-assisted coding will be moved the same way they've been moved for the Turing Test.
The Turing Test used to matter until it didn't (does anyone even talk about it? was there a big news conference when it was solved?). Likewise every time it becomes easier to ship software, the bar will be pushed higher by sceptics. Ultimately the gatekeeping is going to become meaningless as software becomes "too cheap to meter".
For something that was supposedly always unimportant, huge amounts of energy were spent recruiting developers based on how they produced and interacted with code.
FizzBuzz was a litmus test that showed how hopeless the average developer was. Coding interviews were the real test of programming ability. Now we're being told none of that ever mattered for real?
We should just admit that the game has changed (possibly, I'm not 100% convinced). Code WAS the bottleneck and coding ability was the bottleneck, but it may not be going forward.
This is clearly a well-timed loss-leading strategic market share grab! Anthropic have blown a lot of user trust in the last couple of months..
But, overall, the current AI pricing is completely unsustainable, across all AI companies, except via the exponential growth they are relying on. Dylan Patel did the most insightful analysis of this I've come across.. https://youtu.be/mDG_Hx3BSUE?si=nyJu4adwYCH1igbJ
Really feel like the current versions are for sure "good enough". Thats not how market capture is gonna function though and they are gonna keep pushing because the only moat is to stay ahead, so the problems gonna stay strange. at some point more compute isn't a reasonable answer, and optimization is, and my feeling is we are well past that point from a product perspective, but ipos etc etc
The only moat is the us trying to buy all the compute hardware in the world for the next two years. Then China, amd, etc are just making their own chips.
So I think the current generation of models are arguably all about the same in terms of capability. However, the requirement for exponential growth I mentioned is all about the economics.
AI companies are trying to ride a growth wave where the income curve lags the expense curve by 1-2 years, and at the same time investing 10x their historical income on next year's projected demand.
Everyone is selling their API calls at a loss, because to capture the investment required to scale the business up and the costs down, you need to grow your market now (in relative and absolute terms). And history shows, that in big tech you often have winner-takes-all situations, or, at least a couple of big firms will dominate, and the others will die. That's where market share becomes a key strategic goal.
But to secure that, they also need to be building next year's compute now. And if their anticipated compute needs are 10x this year, they've got a serious funding problem, one that can only be filled by capital with an appropriate risk appetite. You can only get this high-risk capital when the potential payoff is even more enormous, or, when it's a smaller bite of a much bigger pie. Hence, MS putting into OpenAI and so on. But the investment needs are getting so big we are starting to see some pullback from more conservative sources, but also record deals from others.
Now say an AI company does get the capital they need to grow. Well, they've still got a very serious supply problem. RAM, GPUs, water, electricity etc. Hence why there's a lot of deals and cross-investment going on - everyone is trying to secure resources and lower their overall risk exposure while keeping a foot in every possible door, so they can switch alliances whenever it's expedient, and because collaboration also helps the overall market to grow.
This all explains to me why the industry _needs_ the hype. These companies can't exist without it, because the money they need to sink in, in order to even be around in 18 months, far outstrips all reasonable financial practices. So it's capitalism on steroids or nothing. If you believe the AI story, then to that extent, it's rational.
But note that nowhere in this scenario does it suggest the actual consumers will be getting a consistent product at a consistent price!!!
Qwen 3.6 supports reasonable agentic programming. People are vibe coding with it. It's really not that far off. If you truly cannot make a model that was SOTA 6-12 months ago work for you today for free I don't want to know what your needs are.
Free lunch? More like "free data". The fools who give their life data and most intimate Intellectual property over to the AI companies for free, yes that's a free lunch that won't be subsidized for much longer when the cost on them which has been unsustainable (their data being harvested for non-training purposes) come stop catch up with them.
Sincerely,
- I see you AI companies harvesting our data giving us discounted subscriptions so we can not realize we are paying you to take our own data!
They need to build data centers and lots of them everywhere, preferably powered with renewable energy. Let the tokens flow like water. The models are finally getting to the point where the LLM just knows what you’re asking for and gives it to you.
there will be free lunch till they admit to themselves that there is no moat. Acquring customers at huge costs is a fools errand when models are mostly indisguishable.
Anthropic is learning that lesson now. Doesnt help that their ceo goes around antognozing everyone by claiming jobs are over and annoying boris does like 500 podcasts per week repeating "coding is solved"
I’m not happy with their privacy policy [1]. I’m unfamiliar with the phrase “Parties with Other Legal Rights”. Given the well-documented struggles of Anthropic and others to provide enough compute, I wonder if “Parties with Other Legal Rights” constitutes part of the advantage here.
Just run a local model or run deepseek from another provider with a policy you like. The models are open weight and widely available. Still cheaper than chatgpt and anything else through 3rd parties
this is the pitch - it's open source, run it yourself. But >99% of people will not have the hardware needed to run these models at a high enough quality to be close to SOTA. So they will run the open-source models on CCP systems for a good price.
a lack of existential threat in the form of pay-seeking and remediation from the people you stole training materials from that allows for an intrinsically different pace of operation than the Western competition
I can't figure out how there's both too little supply (so a dramatic need for more data centers) but also too little demand (so labs subsidize inference).
There isn't too little demand. There is massive demand and many competing companies trying to capture that demand, so they are attempting to make better offers than their competition. Hence subsidy.
- Every competitor is planning for the demand to be much higher in a few years than it is now, and aiming to capture as much of that as they can, which starts by getting companies hooked on their models now
- The data center capacity will get used no matter who captures the most demand
I can somewhat understand companies getting users depentant on their harnesses or workflow, but model vendors as in this deepseek case, I have absolutely 0 model loyalty when it's a simple config change away, and will always optimize for either capability or price (or whatever !/$ metric you can determine).
Depends what you’re doing. For example, Gemini is somehow still your only option if you need a model that can natively understand video and reference timestamps in its response.
In my experience, managers don't have to be hands-on, but they need to be able to recognize people with talent and unblock them do their jobs, to be able to spot process improvements, including channelling the AI hype to productive outcomes, and to be a steadying influence in a crisis (without adding noise). If a manager doesn't have technical ability, its impossible for them to do those things.
Everything but the AI bit are on my list of manager qualities too, but the best managers I've had weren't active programmers, and one had zero coding background.
Knowing what you don't know and knowing how to get qualified information from people around you makes up for a lot of not having a programming background.
If anything, the managers with technical backgrounds who weren't active programmers tended to significantly underestimate the difficulty of doing something because back in their day, things were different or some such nonsense.
I think they simply just haven't figured out that the barrier to entry is so low, that no one really cares what their app can do, even if does something genuinely useful.
I've done a ton of low-effort vibe-coded projects that suit my exact use cases. In many cases, I might do a quick Google search, not find an exact match, or find some bloated adware or subscription-ware and not bother going any further.
Claude Code can produce exactly what I want, quickly.
The difference is that I don't really share my projects. People who share them probably haven't realized that code has become cheap, and no one really needs/wants to see them since they can just roll their own.
The kind of code, with the kind of quality, that LLMs can output has become cheap. Learning has not, and neither has genuinely well designed, human designed, code. This might be surprising to the majority of users on HN, but once a really good programmer joins your team, who is both really good, and also uses LLMs to speed up the parts that he or she isn't good at, you really learn how far away vibe coders are from producing something worth using.
I caught myself structuring a comment like an LLM on another site. It's expected that people who chat heavily to LLMs will start to mirror their styles.
I'm surprised how many of my technical team use free ChatGPT in their personal lives. The rest have Claude subscriptions. I'm the only one with ChatGPT and Claude subs and I'll be switching from Claude Pro to Ckaude Max and cancelling ChatGPT, since I only use it when I hit my Claude quota.
I have been out of the loop for a couple of months (vacation). I tried Claude Opus 4.5 at the end of November 2025 with the corporate Github Copilot subscription in Agent mode and it was awful: basically ignoring code and hallucinating.
My team is using it with Claude Code and say it works brilliantly, so I'll be giving it another go.
How much of the value comes from Opus 4.5, how much comes from Claude Code, and how much comes from the combination?
I strongly concur with your second statement. Anything other than agent mode in GH copilot feels useless to me. If I want to engage Opus through GH copilot for planning work, I still use agent mode and just indicate the desired output is whatever.md. I obviously only do this in environments lacking a better tool (Claude Code).
I suspect that's the other thing at play here; many people have only tried Copilot because it's cheap with all the other Microsoft subscriptions many companies have. Copilot frankly is garbage compared to Cursor/Claude, even with the same exact models.
That's true, but it's interesting how FizzBuzz as said to be the bete noir of the average dimwitted software developer, and how much cutting-edge engineering organizations used to emphasize code in their recruitment processes.
If writing code is being replaced by "engineering judgement" it's going to need a much smaller cohort of developers. Too many opinions spoil the broth, after all.
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