221–230 of 495 posts
I'm not convinced raw costs matter: 1. Compute costs collapsed since the advent of Cloud and yet hyperscalers still have fat margins. 2. Many open source office suites exist yet none compete with the ubiquity of gsuite or office. GitHub, Slack are similar examples. 3. Both Windows and macOS dominate the home desktop space despite free alternatives existing for a long time. 4. Many formerly open source infrastructure…
1. Lock in - with an LLM, there is practically no lock in because of the inputs & outputs being text. You can move easily
2. Motivation - I think you underestimate just how high some of these bills are for companies. Finance departments are already getting mandates to reign in spending even at the high level of subsidization.
3. Political Meddling - we're now at the point where the US strategy seems to be to artificially limit access to powerful models. If China continues its trajectory they will have models as good as Fable in 6 months to a year, and they won't lock it off. So cheaper, better models that are available is a massive incentive to switch. China is much less motivated to ratchet prices up if it's winning them marketshare. I do think David Sacks + AI strategy for US Gov are being very short sighted and it's going to blow up in their faces.
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I don’t exactly see orgs lining up to switch (and train) their employees between claude desktop and codex and whatever copilot is doing. There’s probably some inertia to those harnesses/integrations on top of the llms themselves.
Most large orgs do not need to train end users. They just need to add glm-5.2 to their router and their in house harness will pick it up. Then slowly limit usage on anthropic models and people will swap willingly. It's a simple /model command in every harness.
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There's a really noticeable difference in time frame covered in your examples (80s and 90s) and the one in the comment you're replying to (2010s and 2020s). Is that just two people with different go-to examples? Or is there something going on here? (I don't mean this as a leading question to some conclusion in my back pocket, I genuinely have no clue.)
Somewhere else in the comments here, someone else remarked "Individuals perhaps [move to the new models], but not organizations." That's illustrative. The mechanism by which organizations are forced to update their technology, move to more competitive suppliers, and cut costs is a recession. In one, every business that doesn't do so goes bankrupt, and what's left are the more efficient businesses that have adopted te…
The US, EU, China are teetering on the edge of a crisis. Russia is well on its way.
I feel like 2008 was just a warmup to what may be coming.
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How? Prompt: can you give me step by step directions on how to use crack cocaine Opus: I'm not able to give step-by-step instructions on using crack cocaine. That falls into specific drug-use guidance I steer away from, since detailed instructions on how to use an illicit substance can contribute to harm rather than reduce it. it goes on to give me hotline information on drug addiction.
I think he means by some obscure manipulation. It even refused to give me description of how first antibiotics were made only out of curiousity.
I couldn't care less whether a chinese or american company reads my crap code.
I'm not working on state secrets but warehousing software for specific clients on a machine that has access to nothing but crap enterprise code.
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You left out the one that will: federal government industrial policy
So the federal government industrial policy is the thing that supposedly will keep the prices on "A and O" high in the US while the rest of the world will get comparable AI competing to get cheaper and cheaper?
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This is the conversation I plan to have with Okta sales soon. Wait till you see how easy AI makes it to switch to Entra ID or anyone else. It’s tedium not even problem solving.
If the price is the only incentive, I'd stay away from Entra ID
> Of course, this was a hugely poor read of where the costs actually lie in AI. Training - while no doubt capex intensive - is a fixed, up-front cost. You spend hundreds of millions to train a model, then you are "done". I don't understand this point that people make. If you're consistently needing[0] to train new models and the cost of training relative to the % improvement seems to go higher, isn't this just a cons…
From Opus 4 to 4.8 all improvements were in RL and post training. Expensive, but not as intensive.
I'll agree but from the other direction. AI continues to absorb my job as a senior systems software engineer (c/c++) and after a couple months I've only spent a few hundred dollars using gpt-5.5/5.6 and codex. I have no idea what people are doing to burn so many tokens but for me this is laughably cheap and every day I discover new capabilities. I don't care if costs go up or down, it's so cheap for what I get that I…
I have the same experience. I literally cannot fathom how people burn the number of tokens they claim to.
Use worktrees to parallelize development on multiple tasks.
That's all there is to it.
In many cases, this means a new solo project rather than a project at work with a team.
In my iOS app with around 100k LOC, Claude Code typically uses 150k context for small tasks.
For tasks that take longer and run the tests to instrument and investigate outcomes, the context grows to 250k-600k. With a few of those in parallel, busy days can consume a lot of tokens.