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Google's AI science tools could fuel breakthroughs

Welcome back. Hollywood may be reaching a turning point where AI video is no longer judged by its technology, but by the quality of the storytelling. Meanwhile, federal agencies are monitoring rising anti-AI extremism tied to fears over job loss, data centers, and resentment around forced adoption. And one of the most overlooked announcements from Google I/O could end up being one of the most important: Gemini for Science. This set of tools is designed to accelerate research and give scientists an AI agent lab partner. In some cases, it's helping take shrink research timelines from months or years down to days. Jason Hiner

IN TODAY’S NEWSLETTER

1. Google's AI science tools could fuel breakthroughs

2. AI video hits its story-over-tech moment

3. Anti-AI extremism is taking a darker turn

RESEARCH

Google bets AI agents can accelerate science

Google is rolling out Gemini for Science, a set of experimental tools designed to compress scientific work that would typically take months or years into a matter of days.

This was one of the announcements at Google I/O that went under the radar, overshadowed by the rollout of Gemini 3.5 and Google's re-entry into the smartglasses market. But Gemini for Science could quietly have an even bigger impact than either of those flashy announcements. 

The program is built across Google Research, DeepMind, Google Cloud, and Google Labs. It puts three prototypes into the hands of working researchers through Google Labs. It also extends to enterprise customers through Google Cloud, with early partners including BASF, Klarna, Bayer Crop Science, and the US National Labs as part of the Department of Energy's Genesis Mission.

In an interview with The Deep View, the Head of Google Research, Yossi Matias said, "I think about AI as an amplifier of human ingenuity." He emphasized that junior scientists, postdocs, and even graduate students can now run "their own virtual lab." He also shared the example of an early partnership with Imperial College, where researchers had spent years arriving at a bacterial hypothesis that Google's new Co-Scientist agent reached in days.

Co-Scientist, which Google detailed in a new paper in the journal Nature, is a team of AI agents built on Gemini that work together like a research group. Each agent has a job: one comes up with ideas, another critiques them, another ranks the best ones, another improves them, and another reviews the whole process. A lead agent acts as the manager, keeping them all on track.

Early results look promising. At Stanford, researchers used Co-Scientist to help find an existing drug that reduced signs of liver scarring in lab-grown tissue. The system is also being used by researchers at Calico Life Sciences to study aging, at the University of Edinburgh to look for new liver disease treatments, and at the University of Cambridge to study how viruses like the flu and COVID-19 can spread from animals to humans.

The pitch from Google Research is that general-purpose agents, not narrow specialized models, are what will move the needle for working scientists across disciplines. The key components include:

  • Hypothesis Generation: This is built on Co-Scientist, runs a multi-agent "idea tournament" that generates, debates, and evolves research hypotheses, with claims grounded in citations.

  • Computational Discovery: This is built on AlphaEvolve and ERA (Empirical Research Assistant), generates and scores thousands of code variations in parallel to test modeling approaches in fields like solar forecasting and epidemiology.

  • Literature Insights: This is powered by NotebookLM and structures findings across papers into searchable tables and then produces reports, slide decks, and audio overviews from the materials.

  • Science Skills: This bundle plugs in more than 30 life science databases including UniProt, AlphaFold Database, and AlphaGenome API into agentic tools like Antigravity.

Lizzie Dorfman, who is the science product lead at Google Research, told The Deep View that in one epidemiological forecasting project, her team generated 200,000 candidate models. Most were discarded quickly, but the ability to explore that volume at all is what changes the workflow.

"[Researchers] would say things like, 'I input some ideas, and then I went to sleep, and then I woke up and I had all these cool results to take a look at.'" said Dorfman. "That’s an example of changing the amount of productivity that a single person can have."

All of the frontier labs love to extol the benefits that AI is going to have on science. For example, just last month, OpenAI released GPT-Rosalind, the first life sciences model purpose-built for lab workflows. And we have to give Google credit for its long investment in science and research. Leaders like Dorfman and Matias have been working on basic research inside Google for over a decade. The fact that they are now applying the breakthroughs in AI agents to accelerate their experiments and sharing it broadly with the wider science community has the potential to become one of AI's most positive impacts.

Jason Hiner, Editor-in-Chief

TOGETHER WITH GRANOLA

Real Conversations = Rich Context

By now, you almost certainly know how much all of us at The Deep View love Granola, the notetaking app that saves us around 10 hours per week per person. But their latest update, Spaces, is taking that seamless collaboration and documentation to the next level… and we’re experiencing it firsthand. 

Essentially a team workspace with folders and chat built in, Spaces uses your conversations to give context to any question your team asks. From sales asking “Why are we losing this deal?” to researchers wondering “What are users consistently asking us for?”, you can ask anything and Granola will read all of the Spaces content to immediately give you an answer. 

PRODUCT

AI video hits its story-over-tech moment

The chasm between authentic and fake, known as the "uncanny valley," may be starting to close. 

On Tuesday, AI video firm Runway announced Project Luxo, a research initiative that aims to spark conversations about where AI-generated media stands with audiences in terms of realism and emotional resonance. As part of the project, the company debuted three short films ranging from 46 seconds to 10 minutes. 

Each film had only one staff member and cost roughly $4,000 to produce. The shortest film, an animated segment about pigeons, took four hours to create, while the longest one, a realistic, ten-minute film about a boy lost at sea, took three weeks. 

When showing these films to audiences of entertainment executives, producers, directors and others in the industry, Runway found that many of the comments and critiques weren’t about the use of AI but about the stories themselves.

Additionally, in mid-April, Runway published an AI-generated ad for a fake watch company that garnered more than 100 million views on Instagram and was reposted without mention of AI. “A good story told with AI is a good story,” Runway said in its announcement. “We are moving through the valley.”

Jamie Umpherson, chief creative officer at Runway, told The Deep View that Project Luxo got its name from a 1986 animated short film called Luxo Jr., which debuted at the SIGGRAPH conference in Dallas. The short film was an inflection point for CGI, in which the audience was captivated not just by the computer graphics themselves, but by the character and emotion that the animation portrayed. 

Umpherson said that the goal of Project Luxo is to move the needle for AI-generated filmmaking in the same way. Get beyond the judgment of the technology itself and focus instead on the stories being told. 

“We are at this inflection point where the technology is good enough to no longer have to be the story,” said Umpherson. “The story can be the story.” 

For the creator, Umpherson said, this means that someone could make the next blockbuster film while sitting in their bedroom. For brands, this tech could open the door to creating ads they wouldn’t otherwise be able to produce due to budget or time constraints. 

And for the viewer, "Depending [on] where you're consuming it and what context you have. It doesn't really matter how it was made," Umpherson said. "You don't need to know what camera a film was shot on, or what is CGI versus what isn't. I think at the end of the day, it's about: Does the content resonate?"

AI is still getting mixed reactions in Hollywood, with many creators rallying against it, studios embracing it, and awards institutions reckoning with what it means for the industry’s most prestigious nominations. But Runway’s so-called push to cross the uncanny valley isn't solely about whether creators themselves are comfortable with AI, but about whether the public will notice. The evidence is clear that people are only getting worse at detecting when a piece of media is AI-generated. For The Deep View’s daily AI or Not challenge at the bottom of the newsletter, only around 50% of readers on average can distinguish between AI-generated and authentic images. And as models get better, our ability to tell the difference will only diminish. Though many consumers currently balk when they find out that content is AI-generated, as the lines blur even further, what will be the long-term impacts on this $200 billion industry? The path forward is likely to look very different from what it does today as technology drastically lowers the barriers to entry.

Nat Rubio-Licht

TOGETHER WITH SUPERMETRICS

45% of marketers struggle with marketing data measurement

That's nearly half, and it's not entirely their fault.

According to Supermetrics' latest research, outdated systems, inconsistent strategy, and weak attribution are setting marketing teams up to fail before AI even enters the picture.

The fix is more straightforward than you'd think: own your data strategy, give AI the right inputs, and your team's efficiency will follow.

GOVERNANCE

Anti-AI extremism is taking a darker turn

As much as Silicon Valley is all-in on AI, the rest of the world isn’t nearly as enthusiastic. 

On Tuesday, a WIRED report found that the Department of Homeland Security, the FBI and other agencies are sounding the alarm about anti-technology extremism as concerns mount over AI-powered job displacement and protests rise against the construction of AI data centers. As a result, these agencies are closely surveilling news related to these sentiments. 

In one of thousands of documents viewed by WIRED, the New York Intelligence and Counterterrorism Bureau claimed that AI could cause "large-scale protests that devolve into civil unrest and anti-tech violent extremist activity."

Another document, from an agency in Western Pennsylvania, claimed that adversarial actors and extremist groups may target US data centers and generally "exploit the strategic importance of data centers to the US economy."

It’s the latest signal that AI sentiment isn’t matching the heightened expectations of tech elites. Two recent Gallup polls find that Americans' opinions towards AI are largely negative: 

  • In May, a poll related to data centers found that an average of 7 in 10 Americans opposed the construction of AI infrastructure in their region, largely due to environmental impacts and quality-of-life concerns. 

  • And in April, a poll of people ages 14 to 29 found that excitement about AI dropped by 14 percentage points since 2025, with many reporting that they don’t want to use AI but feel they must to keep their jobs. 

And it makes sense why people have such negative associations with the tech. Almost every week, a new study or forecast is published claiming that AI could fundamentally disrupt the global economy, eliminate jobs, and hinder our ability to think for ourselves. Data centers, similarly, have a bad reputation due to their potential environmental impact, energy demand and impact on water supply.

The grand AI utopian vision tech leaders paint about the future will not be possible without large-scale adoption by the broader public. But that adoption will not happen if sentiment towards the tech doesn’t increase. For the narrative to improve, people need to feel they’re not being forced to use a technology that threatens to replace them. It’s why enterprises should think carefully before blaming AI for layoffs or forcing the tech on their employees. Almost universally, people resent being coerced into change. And when they feel they have no agency, it breeds the kind of extremism that US agencies are now tracking more closely.

Nat Rubio-Licht

LINKS

  • Manus: Projects are now available on mobile

  • Google AI Studio: Users can build native Android apps for free

  • Grok Build: now available in Beta for all SuperGrok and X Premium+ users

  • ElevenLabs Music v2: The voice AI firm launched a new and improved music model

GAMES

Which image is real?

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A QUICK POLL BEFORE YOU GO

Would you see an AI-generated film in a theater?

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The Deep View is written by Nat Rubio-Licht, Sabrina Ortiz, Jason Hiner, Faris Kojok and The Deep View crew. Please reply with any feedback.

Thanks for reading today’s edition of The Deep View! We’ll see you in the next one.

“AI would not have come up with traffic cones and wouldn't have dared to put in so much beautiful contrast. ”


“The street cones in [this image] are not symmetrical, and the house coloring is less universal.”


“[This image] has traffic cones, one is broken and one has been moved out of the road which felt more realistic.”

“[This image] looked just too perfect, such as the uniform buildings.”

“The tree shadows gave [this image] away.”

“The light at the end of the road in [this image] is unrealistic.”

“[This image] looks to have a car on the pavement in the distance, and some weird blurring on the road in the foreground.””

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