MARKETS

Why Google is chasing Jev into decision models

As AI model cost and efficiency comes sharply into focus for enterprises, Google is taking the hint. 

On Wednesday, the tech giant released the Gemini Decisions API, an endpoint for users to access Gemini-decision-1, a model built for rapid decision making, which creates another competitor to TypeSafe's popular Jev. The new Google model costs 10 cents per million input tokens, and output tokens are free, matching OpenAI's price for its own recently-released decision model. 

Gemini Decisions API is built to handle text, images, audio, video, and PDFs, as well as up to 1,000 independent questions with "bounded choices, such as things like tool selection, policy checks, model routing, and context grading. Then, instead of coming up with answers using tokens to generate text, the model grades different options and returns probabilities and confidence scores. 

• Like other decision models, Gemini Decisions API isn't built for open-ended questions. The model offers three decision choices: Yes or no, multiple choice with up to 255 options, and rubric-based scoring. 

• Google says that its system can return outputs in as little as 150 milliseconds, compared to Jev's response time of between 70 and 500 milliseconds and OpenAI's of between 100 milliseconds and three seconds. These decision models beat the response time of a traditional LLM by a mile, which can take up to 329 seconds to respond. 

• Google also claims its model beats out the TypeSafe and OpenAI offerings in decision-specific benchmarks, including for tasks like agent routing, reasoning, ranking, and multimodal understanding. 

Inside Google, the company said it's using Decisions API for things like dynamic model routing in Gemini Notebook, iterating on designs through rapid generation and editing, and adding a "safety layer" for MCP in search that detects adversarial behavior. 

Google's latest release adds to a growing movement towards more efficient and affordable AI as enterprises grow weary of the expensive token bills brought on by frontier models. Though many are looking into open-source, local models, and small models to fix this, decision models provide a way to drastically drive down the cost curve, especially as agents, which don't need outputs to be in lengthy text, make up an ever-increasing share of token spend.

While the decision model trend isn't necessarily favorable to the financials of frontier labs like Google and OpenAI, these companies' validation of decision models could signal that they are eager to keep pace with the changing tide. The fact that two of the biggest labs in the industry have jumped on this trend following Jev's explosion onto the market could indicate that decision models may be one of AI's next big trends. Though it's unlikely that these models will replace language models entirely, they certainly have the potential to upend the economics of AI, making it far more affordable to implement agents at larger scales for tasks that don't require language outputs. However, one thing to be wary of is the fact that these models aren't built for humans to understand their outputs. While that's exactly the beauty in them, as cutting out the friction of human understanding makes them far more efficient, that may also limit our ability to observe and control them. 

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PRODUCTS

Why OpenAI's Dots leads the proactive agent race

Proactive agents that anticipate your needs are the best new trend in AI during the second half of 2026. 

The year started with more AI moving from chatbots that simply answer your questions to agents that handle your tasks. That's where OpenClaw, Anthropic's Claude Code, OpenAI's Codex, and a slew of other agents stole the headlines and drove AI token bills through the roof. 

The next stage is persistent agents that can watch your messages, follow your files, carry out multiple-step workflows across different platforms, and preemptively flag you when you're missing something, making mistakes, or are unaware of gaps that are emerging. 

OpenAI's Dots, Anthropic's Claude Duet, Meta's Muse, and SpaceX's GrokBot are the best examples. All four are persistent agents that run with their own cloud computer and web browser, work within the apps you connect, run in the background without your intervention, and require your approval before taking consequential actions. 

I've been testing the ones from OpenAI, Anthropic, and Meta for the past couple months and I'm especially impressed with OpenAI's Dots. It has saved me from a number of mistakes and errors, flagged impending calendar conflicts, reminded me of things I promised to do, proactively helped me prepare for important meetings, flagged upcoming deadlines, and generally anticipated my needs in ways that are helpful without being creepy. 

Here are a few examples of things that Dots did for me:

• Noticed that a hotel booking confirmation was off by one day on either end of the trip compared to what I had requested in an email thread, which saved me a headache when I would have tried to check in and discovered I didn't have a room

• I remembered that I had promised two deliverables to teammates when I was going to be back at my desk on a Friday, but I couldn't remember what the two things were; Dots reminded me of exactly the two things I had promised and it was over 24 hours ahead of time so I still had time to work on them and meet the deadline

• Caught a discrepancy between facts written in an article in Google Docs and the press release about the topic from my inbox; it was right and it flagged it fast enough that we caught it before it was published

• Proactively flagged two typos in an important work document that I created before it was sent to the stakeholder who needed to review it

• Regularly reminded me of important emails and messages that needed a response, but had scrolled off of my radar

Of course, in order to get this level of usefulness I had to give OpenAI access to my Google Workspace and Slack. And since I work at a media startup, that's not such a big deal. But if you work in a regulated industry or handle deeply proprietary and confidential information, then that's not going to fly. However, more and more software companies are building their own versions of these kinds of agents into their regulatory-compliant versions of platforms for various industries. 

As for Claude Duet and Meta Muse, I found that Duet is particularly good at identifying multi-step tasks it could carry out for me and simply offering a button to press to get started. But it's not nearly as prolific or proactive as Dots. And as for Muse, I didn't trust Meta enough to give it private data other than a connection to my Instagram. But it is a good shopping assistant. When I left one of my favorite shirts in a hotel room recently, Muse was able to go out to eBay and find an inexpensive version of the same shirt, for example.

This week I had a great conversation with an Uber driver about the ways he was using ChatGPT to streamline important parts of his work in his blue-collar service industry job until being laid off just a few weeks ago. And he mentioned that he's been continuing to use ChatGPT for job searches and automating other parts of his life such as personal budgeting. I asked him if he's been using Dots, and he said, "No." Then I explained some of the helpful things it can do (like the ones listed above) if he's comfortable hooking up his email to it. And then he said, "Oh yeah, it's doing all that for me," and he explained how it flagged new job listings he'd received in email that were a perfect fit for his skills and how he quickly applied to those. So he was using Dots without realizing he was using Dots, or remembering the brand name. I took this as a great sign that AI's latest benefits are starting to reach more users a lot faster. If you haven't tried Dots or other persistent agents like it yet, I'd recommend giving them a try. Just make sure you're clear and intentional about what your data privacy and compliance requirements are.

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RESEARCH

Google's DNA model narrows AI's promise gap

AI companies promise that the tech's applications in sciences are practically boundless. New research from Google DeepMind may start to live up to that promise. 

On Wednesday, the company's AI lab unveiled AlphaGenome 2, the second generation of its DNA sequence model, aimed to help scientists research the cellular causes of disease. The model represents a "step-change improvement" in predicting genetic variations compared to its predecessors, allowing researchers to predict genetic mutations in specific cells, including those the model is unfamiliar with.

The model's previous iteration, AlphaGenome, was released last year and was able to predict how variants of DNA could impact molecular activity across broad tissues. AlphaGenome Atlas, released in September, used this to create predictions for every possible single-letter change in the human genome, totalling 9 billion, making it the most comprehensive catalogue yet of how genetic mutations impact molecular biology and allowing biologists to potentially identify harmful mutations. 

AlphaGenome 2 builds on this work, improving in a number of areas: 

• The model can identify nearly twice as many genetic variants that change gene expression in cells compared to the previous model, scoring a 30% in identifying causal variants compared to the original AlphaGenome's 16%. 

• It's also able to make more precise predictions, as it can investigate specific cell types rather than just broad tissue categories, and can make predictions about genetic behaviors based only on a small sample of cells. 

• AlphaGenome 2 is also trained on a massive well of data, learning from 107 million individual cell profiles across 68 different types of tissues. 

Though the model is only available publicly for non-commercial research and in private preview for commercial use cases, it could aid researchers in discovering which genetic mutations are linked to disease at a cellular level, helping speed up research and develop new drugs. It's also one of several efforts from Google to embed AI within science, with its previous track record including disease research on breast cancer and tuberculosis and extreme weather mapping.

Science, particularly life sciences and health, are commonly used as an arguing point by AI industry leaders to justify the investment of so many resources into AI. For instance, Anthropic CEO Dario Amodei said in a recent podcast that AI could make things like cancer, Alzheimer's, heart disease and mental illness "relics of the past," as the tech could generate cures for a broad swathe of diseases. However, when faced with this reasoning over and over again, it could be argued that using health as a bargaining chip to vindicate creating increasingly powerful AI is manipulative, playing into humanity's drive to live longer, healthier lives. While it's undeniable that this tech can, and has, facilitated innovative research in health, as shown by Google DeepMind's latest delivery, it shouldn't be treated as a panacea for all of humanity's ailments, nor should these innovations be used to justify unchecked growth by frontier labs. 

LINKS

• Nvidia board reportedly prepares to oust Jensen Huang

• Anthropic IPO oversubscribed as company targets $1.4 trillion valuation

• Palantir CEO Alex Karp pivots toward Democrats after US midterms

• ByteDance plans to release a Muse-like AI assistant for video

• Grok Build coding agent leaks confidential data from 35,000 apps

• United Airlines will reportedly ban AI bots from booking flights

• Qwen4-Max: The latest model from Alibaba built for complex coding and knowledge work, now in preview.

• Tabletop: new Google Labs experiment built for real-time collaboration

• Claude: Now rolling out Claude Whiteboards, Claude Sheets and Claude Data.

• Scribe v3 and Scribe v3 Live: ElevenLabs' most accurate Speech to Text models yet

• Anthropic: Applied AI Architect, Startups

• Green Key Resources: Artificial Intelligence Specialist

• Distyl: Applied AI Researcher, System Discovery

• StackAI: AI Strategist

GAMES

POLL RESULTS

Do you regularly use productivity from frontier labs, such as Claude Docs, Slides, Design, etc.?

Yes (19%)
Somewhat (7%)
No (73%)
Other (1%)