The models are finally ready. Costs of inference are getting optimized with open models, and even on-device models.

We now appear to be entering the era of token rationing.

The nuances of tuning the carburetors & the timing belts of these complex beasts are tasks better assigned to a few vendors to deliver maximum intelligence per dollar & amortize the costs across a broader population.

Even though per-token prices have fallen, the push for more AI adoption and increasingly autonomous agents have driven token consumption higher and higher.

At a time when many companies are blowing through their AI budgets, those token cost savings have become a major selling point for the company.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Uber capped employee AI spending after blowing through its budget in four months.

Benchmarks are now measured on two different dimensions, the overall performance & the cost to achieve that intelligence.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Even the most valuable companies in the world cannot afford state-of-the-art intelligence for every conceivable use case.

Running every query through Opus 4.7 is the fastest path to negative gross margins. The best Rest of Oz companies route across tiers of models — frontier models for the hardest tasks, mid-tier for the bulk, smaller custom or fine-tuned models where they've earned the right to use them.

The Scientist AI is going to be trained using essentially the same machine learning techniques: stochastic gradient descent on large neural nets, transformers, whatever works best. It doesn't care about what is the architecture of the neural net. So all of the effort that is currently being done to improve, for example, memory and other properties and continual learning, can just be applied directly to the Scientist AI.

The benchmark tasks were meticulously constructed to be realistic, involving the hard work of hundreds of experts and likely millions of dollars — placing it among the most expensive economics papers of all time.

When our engineers no longer spend time supervising Codex sessions, the economics of code changes completely. The perceived cost of each change drops because we're no longer investing human effort in driving the implementation itself.

Resolution increases make them more expensive, then efficiency gains reduce costs - a sawtooth pattern.

This is the part people miss about AI-native companies - the $113k is not a cost, it is your headcount budget allocated differently.

The extra tokens bought something measurable. +5pp on strict instruction-following. Small. Real. So: is that worth 1.3–1.45x more tokens per prompt?

Eight out of eight models detected Mythos's flagship FreeBSD exploit, including one with only 3.6 billion active parameters costing $0.11 per million tokens.

As the cost of software development falls, trusted partners with broad adoption can expand faster than anyone else.

未来的评估体系，必须同时考虑：成功率、成本、延迟。这有点类似于对于云计算的考核标准，而不是传统软件。

Performance: dev-browser: 3m53s, $0.88, 100% success rate — beats MCP configs, Chrome extensions, 'browser skill' stacks.

that's what anthropic says it cost Mythos to find those zero days. per repo.

It defines a frontier model as any AI model trained using more than $100 million in computational costs, which likely could apply to America's largest AI labs, like OpenAI, Google, xAI, Anthropic, and Meta.

Total cost: ~$29 ($20 in CPU VMs, $9 in API calls) over ~3 hours with 4 VMs.

Artificial Analysis has also positioned Gemini 3.1 Flash TTS within its 'most attractive quadrant' for its ideal blend of high-quality speech generation and low cost.

since reasoning models and agentic AI can rack up quite a bill

The cost of understanding what happens in a video has dropped by a factor of roughly 40, while the quality of that understanding has improved dramatically.

By using SAM, the Alta team has been able to process more than 20 million images without incurring exorbitant costs, allowing them to focus on building the best possible product for their users.

For a socially and economically sustainable growth path, the labor displacement in the sectors ofapplication must be counterbalanced by job creation within the same and other sector