On the heels of releasing its most capable AI model yet, Google is making some changes to the Gemini team. A new report from Semafor reveals that longtime Googler Sissie Hsiao will step down from her role leading the Gemini team effective immediately. In her place, Google is appointing Josh Woodward, who currently leads Google Labs.
According to a memo from DeepMind CEO Demis Hassabis, this change is designed to “sharpen our focus on the next evolution of the Gemini app.” This new responsibility won’t take Woodward away from his role at Google Labs—he will remain in charge of that division while leading the Gemini team.
Meanwhile, Hsiao says in a message to employees that she is happy with “Chapter 1” of the Bard story and is optimistic for Woodward’s “Chapter 2.” Hsiao won’t be involved in Google’s AI efforts for now—she’s opted to take some time off before returning to Google in a new role.
Hsiao has been at Google for 19 years and was tasked with building Google’s chatbot in 2022. At the time, Google was reeling after ChatGPT took the world by storm using the very transformer architecture that Google originally invented. Initially, the team’s chatbot efforts were known as Bard before being unified under the Gemini brand at the end of 2023.
This process has been a bit of a slog, with Google’s models improving slowly while simultaneously worming their way into many beloved products. However, the sense inside the company is that Gemini has turned a corner with 2.5 Pro. While this model is still in the experimental stage, it has bested other models in academic benchmarks and has blown right past them in all-important vibemarks like LM Arena.
Sergey Brin co-founded Google in the 1990s along with Larry Page, but both stepped away from the day to day at Google in 2019. However, the AI boom tempted Brin to return to the office, and he thinks everyone should follow his example. In a new internal memo, Brin has advised employees to be in the office every weekday so Google can win the AI race.
Just returning to the office isn’t enough for the Google co-founder. According to the memo seen by The New York Times, Brin says Googlers should try to work 60 hours per week to support the company’s AI efforts. That works out to 12 hours per day, Monday through Friday, which Brin calls the “sweet spot of productivity.” This is not a new opinion for Brin.
Brin, like many in Silicon Valley, is seemingly committed to the dogma that the current trajectory of generative AI will lead to the development of artificial general intelligence (AGI). Such a thinking machine would be head and shoulders above current AI models, which can only do a good impression of thinking. An AGI would understand concepts and think more like a human being, which some would argue makes it a conscious entity.
To hear Brin tell it, Google is in the best position to make this AI computing breakthrough. He cites the company’s strong workforce of programmers and data scientists as the key, but he also believes the team must strive for greater efficiency by using Google’s own Gemini AI tools as much as possible. Oh, and don’t work from home.
Brin and Page handed the reins to current CEO Sundar Pichai in 2015, so his pronouncement doesn’t necessarily signal a change to the company’s current in-office policy. Google still operates on a hybrid model, with workers expected to be in the office three days per week. But as a founder, Brin’s voice carries weight. We reached out to Google to ask if the company intends to reassess its policies, but a Google rep says there are no planned changes to the return-to-office mandate.
Over the past few years, Google has embarked on a quest to jam generative AI into every product and initiative possible. Google has robots summarizing search results, interacting with your apps, and analyzing the data on your phone. And sometimes, the output of generative AI systems can be surprisingly good despite lacking any real knowledge. But can they do science?
Google Research is now angling to turn AI into a scientist—well, a “co-scientist.” The company has a new multi-agent AI system based on Gemini 2.0 aimed at biomedical researchers that can supposedly point the way toward new hypotheses and areas of biomedical research. However, Google’s AI co-scientist boils down to a fancy chatbot.
A flesh-and-blood scientist using Google’s co-scientist would input their research goals, ideas, and references to past research, allowing the robot to generate possible avenues of research. The AI co-scientist contains multiple interconnected models that churn through the input data and access Internet resources to refine the output. Inside the tool, the different agents challenge each other to create a “self-improving loop,” which is similar to the new raft of reasoning AI models like Gemini Flash Thinking and OpenAI o3.
This is still a generative AI system like Gemini, so it doesn’t truly have any new ideas or knowledge. However, it can extrapolate from existing data to potentially make decent suggestions. At the end of the process, Google’s AI co-scientist spits out research proposals and hypotheses. The human scientist can even talk with the robot about the proposals in a chatbot interface.
The structure of Google’s AI co-scientist.
You can think of the AI co-scientist as a highly technical form of brainstorming. The same way you can bounce party-planning ideas off a consumer AI model, scientists will be able to conceptualize new scientific research with an AI tuned specifically for that purpose.
Testing AI science
Today’s popular AI systems have a well-known problem with accuracy. Generative AI always has something to say, even if the model doesn’t have the right training data or model weights to be helpful, and fact-checking with more AI models can’t work miracles. Leveraging its reasoning roots, the AI co-scientist conducts an internal evaluation to improve outputs, and Google says the self-evaluation ratings correlate to greater scientific accuracy.
The internal metrics are one thing, but what do real scientists think? Google had human biomedical researchers evaluate the robot’s proposals, and they reportedly rated the AI co-scientist higher than other, less specialized agentic AI systems. The experts also agreed the AI co-scientist’s outputs showed greater potential for impact and novelty compared to standard AI models.
This doesn’t mean the AI’s suggestions are all good. However, Google partnered with several universities to test some of the AI research proposals in the laboratory. For example, the AI suggested repurposing certain drugs for treating acute myeloid leukemia, and laboratory testing suggested it was a viable idea. Research at Stanford University also showed that the AI co-scientist’s ideas about treatment for liver fibrosis were worthy of further study.
This is compelling work, certainly, but calling this system a “co-scientist” is perhaps a bit grandiose. Despite the insistence from AI leaders that we’re on the verge of creating living, thinking machines, AI isn’t anywhere close to being able to do science on its own. That doesn’t mean the AI-co-scientist won’t be useful, though. Google’s new AI could help humans interpret and contextualize expansive data sets and bodies of research, even if it can’t understand or offer true insights.
Google says it wants more researchers working with this AI system in the hope it can assist with real research. Interested researchers and organizations can apply to be part of the Trusted Tester program, which provides access to the co-scientist UI as well as an API that can be integrated with existing tools.
