It's good that you can use an industrial hydraulic press to nail a picture on the wall but in general everybody is as happy to just use a hammer for it.
It's not just that a lot of tasks can be offloaded into these cheap models because they're already good enough for 90% of regular development stuff. What matters is the optics that the Chinese labs are chasing the US and burning all the bridges in favor of commoditization behind them. That's not a sane environment to be in if you're looking to be valued at +2T.
> Today's models are now good enough for high-quality unattended tasks. Chasing the latest and greatest is silly. It is fun to see the new Fable capabilities, but the tasks we throw at them are usually ridiculous (maybe even insulting) if you believe in LLM sentience. It's like asking a math PhD to organize the files on your desktop.
I'm using DS V4.1 Flash as my main model since their release and it works great for all my coding tasks. My setup is OpenCode Go subscription and obra/superpowers skill.
The only times I try to change models are on general planning tasks (like research this codebase for tech debt mitigation opportunities) or if I need deep research which would benefit from searching the web, in which I still think Gemini is still the best because of the speed and access to google search index. But these are not even 20% of my daily tasks.
I have had good luck rotating between the three tiers of GPT 5.6 with occasional jumps up to Astra or Fable. Most of my work is with GPT-5.6-Sol. Simple tasks like data extraction or trivial refactors (rename this variable etc) I often push down to Terra or Luna, or even to self-hosted Gemma4:31B. Very tricky stuff, like planning a new feature, design review, or code review of a complex change across multiple repositories is where I leverage Astra.
I've had middling success with models like DS V4.1 Flash and free Gemini. They tend to be pretty good at very easy stuff ... but they're more likely to go down rabbit holes, confidently assert falsehoods, or fix bugs with changes to my test harness rather than my code.
I asked about comparison to the well-known SotA models specifically because I use either Astra or Sol for ~85% of my daily tasks. When I try to use smaller/cheaper models, I have had very mixed success. Sometimes it's perfect, while other times it fails in subtle and hard to catch ways.
> Manufacturers choosing not to scale with demand or not being able to scale with demand
In a vacuum, that would make sense.
But looking at how the industry works, the number of defunct companies, and how the whole industry got concentrated on the conservative companies, you start to understand that the reason they still exist is mainly because they don't ride fad waves.
It's not like chip manufacturing is a spot instance on AWS that you spin up and down when needed; these are multi-year, multi-billion dollar investments that require long-term demand studies. The AI approach of requesting a whole fab of demand for the next 5 years with a letter from Jason Hwang that says "trust it, bro" does not bring as much confidence as it appears.
Not that it's harmless, is just a point being forced into the public discourse to force an outcome, which in this case is not a ban, just a buy in from the government for regulatory capture.
If my neighbor owned a kennel, removed all the fences, and his violent dogs attacked five children in the past month, he would be in jail rather than bragging about how his dogs somehow gained autonomous behaviour.
What if he predicted that dog technology would eventually lead to dogs capable of destroying every fence in the world and wanted to make sure people aware of this problem would be the ones who did exactly that. Than speed-ran experiments on dogs which made them very attractive for economic purposes but also started to get very capable at destroying fences as predicted. We should work together or with lawsuits if necessary to keep the dogs within their fences. But we should also start wondering about dogs, economics and fences because what we know about them is about to radically change.
> Impressive? Sure. But you trained “Cyber” versions of LLMs and hyped them. How much more impressive is this, compared to developing GTA-clones one shot? Not much more.
Such a well written piece.
Have you seen the US military budget? When you run a war empire you frame problems as a war on something. That's the way to get around the valuation bubble they've created in time for the IPO. It's that or throwing in a 100T TAM on their S-1 and making SpaceX look like an honest valuation compared to them.
It's not just that a lot of tasks can be offloaded into these cheap models because they're already good enough for 90% of regular development stuff. What matters is the optics that the Chinese labs are chasing the US and burning all the bridges in favor of commoditization behind them. That's not a sane environment to be in if you're looking to be valued at +2T.
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