Today, a Hacker News thread asked a simple question : “Has anyone replaced Claude/GPT with a local model for daily coding?”1 500+ comments later, a clear picture emerged of the local coding stack.
Qwen 3.6 35B-A3B dominates model mentions at 33%, followed by the 27B variant at 20%. DeepSeek Pro & Gemma4 31B round out the top four. The common thread : mixture-of-experts architectures that run fast on consumer hardware.2
We’re entering the golden age of AI applications. Three recent developments confirm it.
The Fable retraction shows regulatory risk. Nadella’s thesis shows strategic consensus. Salesforce’s acquisition shows market validation.
First, the US government shut down Fable access1 & the software ecosystem roared with many responses : Bring it back! Open-source & local models have become essential! Don’t rely on a single model!
Satya Nadella published an AI ecosystem thesis.2 He argued that for a healthy ecosystem, the moat can’t be the model. Instead, human expertise & the system around the model (the harness3) must be the moat.
SpaceX IPOs today. One hallmark of the largest IPO in history : Elon Musk’s astoundingly low cost of capital. Despite raising 25x more than the typical founder, Musk retained ownership in the top decile.
Some founders raise $2m for an idea. Others raise $15m. Yet others raise hundreds of millions.
We’ve reached the upper bound of AI.
Not in the sense that performance won’t improve. On the contrary, AI will improve AI.
But Anthropic’s Fable release has imposed a glass ceiling. How do you release the most powerful model in the world to everyone without destroying kingdoms?
Strong guardrails. It’s easy to trigger a gentle reminder of verboten topics : ask for a description of a plant cell or a detailed description of a modern large language model or question about software security.
Three forces are reshaping the AI cost structure :
The natural response from AI buyers is substitution.
Coinbase6 :
At Coinbase we’re working hot on routing prompts to cheaper models where appropriate, & in some cases have been able to keep costs roughly flat, while token usage continues to grow exponentially.
A laptop on my desk now handles 78% of my AI work, with the rest sent to the cloud. The shift came out of my skill distillation work.
Here’s how it works.
I create tasks in Asana. An agent sees the task : scheduling, email triage, research, a CRM update ; & classifies it as easy or hard. If it’s straightforward, a local model on my Mac handles it in seconds. If it’s complex, the same model routes it to a cloud model.
Yesterday Microsoft added a new metric to a model release card, one that will likely become a standard.1
Average token usage.
In the first row, the Microsoft model hits 71.6 on SWE-Bench Verified using about a third of the tokens Claude Haiku 4.5 burns.
Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.
Competition is a discovery procedure. — Friedrich Hayek
And developers are discovering the value of open models.
OpenRouter offers a useful view into the model market.1 It is not the whole AI economy. But it is close to the API frontier, where developers can switch models quickly, compare price-performance daily, & route each request to the best available option.
Since 2025, open models have grown sharply on OpenRouter. In the latest model-level snapshot, open-weight models generated 69.1% of named open-versus-closed token volume. Closed models produced 30.9%.
With Michael Burry 1 & Leopold Aschenbrenner 2 placing heavy short trades on AI, questions about GPU depreciation, & the Saaspocalypse, how negative is the financial market on AI?
We can look at the percentage of shares sold short, a bet the stock will decline.
Across all software, semiconductor, neocloud, data center, & hyperscalers, the median short interest (short shares / total shares) has increased by about 24% in the last quarter.
