# GenAI Brief

> GenAI Brief covers the launches, research and tools that actually matter in generative AI: daily news, model comparisons, surveys and editorials, written for people who build with AI.

Full text of every article: https://genaibrief.com/llms-full.txt

## News

Launches, funding, partnerships and policy: the generative AI stories that matter, reported fast and put in context. Index: https://genaibrief.com/news/

- [Inference prices keep falling. Here is who actually benefits](https://genaibrief.com/news/inference-prices-keep-falling/): Per-token prices for frontier-class models have dropped by roughly an order of magnitude every 18 months. The savings are real, but they land unevenly across the stack.
- [The EU AI Act's model rules, one year in: what changed for builders](https://genaibrief.com/news/eu-ai-act-gpai-obligations-one-year-in/): General-purpose AI model obligations under the EU AI Act have applied since August 2025. A year later, here is what providers have actually had to do, and what still applies to you if you only fine-tune or deploy.

## Models

Frontier and open-weight model releases, benchmarks, pricing and what each one changes for the people who build on them. Index: https://genaibrief.com/models/

- [How to read a model card without getting fooled](https://genaibrief.com/models/how-to-read-a-model-card/): Model cards are part specification, part marketing. Here is a field-by-field guide to the numbers that matter, the ones that are routinely gamed, and the questions a card should answer before you ship on it.
- [Open-weight models are closing the gap. The economics say it will not fully close](https://genaibrief.com/models/open-weight-models-closing-the-gap/): The lag between the best closed model and the best open-weight model has shrunk to months on most benchmarks. Whether it reaches zero depends on who pays for frontier training runs and why.

## Research

Papers, techniques and results from labs and universities, translated into what they mean in practice. Index: https://genaibrief.com/research/

- [What "reasoning" models actually do differently](https://genaibrief.com/research/what-reasoning-models-actually-do/): Reasoning models are trained to spend tokens thinking before they answer. Here is what that training involves, why it works on some problems and not others, and how to decide when to pay for it.
- [Test-time compute changed the scaling roadmap. Here is what it costs](https://genaibrief.com/research/test-time-compute-changed-the-roadmap/): For a decade, progress meant bigger training runs. Now labs can trade inference compute for capability instead. That shifts the economics from capex at the lab to opex at the user, and it changes what "a better model" means.

## Tools

Developer tooling, agents, evaluation, gateways and the software stack around generative AI, tested and compared. Index: https://genaibrief.com/tools/

- [Five LLM gateways compared: routing, failover, governance and where each fits](https://genaibrief.com/tools/llm-gateways-compared/): LLM gateways sit between your applications and model providers to handle routing, keys, failover, budgets and logging. We compare Bifrost, LiteLLM, Portkey, Kong AI Gateway and Cloudflare AI Gateway on the decisions that actually differ.
- [The state of AI agent frameworks in 2026: a survey](https://genaibrief.com/tools/agent-frameworks-survey-2026/): Agent frameworks have split into graph-based orchestrators, lab-native SDKs, multi-agent role systems and typed minimalists. This survey maps the landscape, the design bets behind each camp and the questions to ask before committing.

## Industry

The economics, strategy and regulation of generative AI: who is spending, who is winning and why. Index: https://genaibrief.com/industry/

- [Benchmarks became a marketing channel. Treat them like one](https://genaibrief.com/industry/benchmarks-are-marketing-now/): Benchmark tables were meant to be measurements. They are now launch collateral, optimised for by every lab and reported under whatever settings look best. That does not make them useless, but it changes how they should be read.
- [The real cost of running an AI product, line by line](https://genaibrief.com/industry/real-cost-of-an-ai-product/): Token spend is the line everyone watches and rarely the largest. A working breakdown of where the money goes in a production generative AI product, from inference and evaluation to the humans in the loop.

## Site

- [Comparisons](https://genaibrief.com/comparisons/)
- [Surveys](https://genaibrief.com/surveys/)
- [Editorials](https://genaibrief.com/editorials/)
- [Explainers](https://genaibrief.com/explainers/)
- [Topics](https://genaibrief.com/tags/)
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