This Week in AI：Gemini 4 Carbon、Mistral Large 4 等新模型发布，投资者质疑 AI 支出

New frontier models arrived this week as investors showed signs of growing cautious about AI spending. On the latest episode of This Week in AI, we examined those developments, along with research into how models learn and the less visible costs of AI adoption. From unequal pricing across languages to the environmental impact of data centers, the discussion raised questions about who benefits from AI progress and who pays for it.

Frontier models advance as investors question the spending

We started with three model announcements that show how quickly AI capabilities are expanding. Google introduced Gemini 4 Carbon, designed for cybersecurity and complex, multistep tasks. France’s Mistral published the weights for its trillion-parameter Large 4 model, also known as “Le Chonk,” giving developers more control over deployment and customization, provided they have the hardware to run it. Meanwhile, Google DeepMind previewed Genie 4, a world model that simulates environments, physical interactions, and sequences of events. Unlike language models that predict text, world models attempt to predict what happens when objects and people interact. With Genie 4, researchers could train AI systems in simulated worlds, an approach that would be especially useful in robotics, where teams could test AI designed to control robots before trying it on physical machines.

Investor enthusiasm for AI appeared to weaken even as model development accelerated. Anthropic moved closer to a public offering by filing its IPO paperwork publicly, although it hasn’t announced a date or share price. OpenAI has reportedly secured about $11 billion in funding for its latest round, well below its initial $30 billion target. Those developments coincided with sharp declines in several major technology stocks, and the tech-heavy Nasdaq ended October with its worst monthly decline since March 2025.

The scale of AI spending is attracting greater scrutiny, particularly as investors wait for returns. Companies investing in AI should pay attention to more than model performance. The economics of training and running increasingly expensive models could affect pricing, availability, and which providers remain competitive.

Researchers are rethinking how AI models learn

Researchers are investigating how AI models process information, retain knowledge, and generate responses. AI interpretability company Goodfire proposed a project modeled on the Human Genome Project, committing $10 million to begin mapping how large language models work internally. By studying which patterns activate in response to prompts and how changes to those patterns affect behavior, researchers hope to better understand why models produce particular outputs.

Google Research proposed a framework inspired by human cognition that introduces a “sleep” phase. During this offline period, models would consolidate new information and generate practice examples through a process researchers call “dreaming.” The goal is to address catastrophic forgetting, when learning new information degrades previously acquired capabilities, while helping models retain knowledge beyond individual conversations. The approach remains experimental, but it offers a possible way for models to continue learning without sacrificing existing skills.

We also examined Clover, a 35-billion-parameter diffusion language model released by China’s DiffuSpace. Most familiar language models use what’s called an autoregressive approach: They generate text sequentially, one token at a time. Diffusion language models instead work on multiple parts of an answer simultaneously, refining them over successive passes. DiffuSpace reports competitive performance and faster generation on some tasks when compared to autoregressive models of similar size. Alongside Goodfire’s and Google’s research, this work suggests that improvements in AI may increasingly come from rethinking how models learn and generate outputs rather than simply making them larger.

AI’s hidden costs extend beyond infrastructure spending

Research into the “tokenizer tax” revealed how AI pricing can disadvantage people who speak less widely represented languages. Researchers found that some languages require substantially more tokens than English to express the same message. In one example, the Malayalam translation contained slightly fewer characters than the original sentence in English, yet it required twice as many tokens: 26 compared with 13. Because AI providers commonly charge by token, these differences can translate into higher costs for users, particularly in communities where AI services may already be less affordable. Developers building multilingual applications need to consider tokenization efficiency alongside model quality.

In Finland, authorities are investigating Google’s clearing of more than 400,000 trees for planned data centers. The investigation centers on whether the company completed required environmental assessments before clearing the sites. The case shows why environmental reviews need to happen before land is cleared or construction begins. Once that happens, regulatory action can’t immediately restore the affected ecosystem.

Finally, several AI companies said they plan to honor a shared website opt-out signal that would let publishers indicate that their content shouldn’t be used for AI model training. The proposal could give website owners more control over future data collection, although participation remains voluntary. It can’t reverse the use of material already collected for training, and the standard will only help if AI companies follow the instructions publishers provide.

What’s next

This week’s developments show why evaluating AI progress requires more than tracking model releases and benchmark scores. We need to understand how systems learn, whether their economics are sustainable, and how their costs affect people and communities. As more businesses and individuals adopt AI, those considerations will become harder to ignore.

We’ll be back on Monday, November 9, for another episode covering the latest developments in AI research and applications. You can catch previous episodes on YouTube, Spotify, Apple, or wherever you get your podcasts.