Meta has found a new source of training data for its AI models: its own employees. The company plans to use data culled from the mouse movements and keystrokes of its own staff in its pursuit to build more capable and efficient artificial intelligence.

The story, which was first reported by Reuters, shows the lengths to which tech companies are going to find new sources of training data — the lifeblood of AI models that helps the programs learn how to more effectively carry out tasks and respond to user queries.

When reached for comment by TechCrunch, a Meta spokesperson provided the following statement: “If we’re building agents to help people complete everyday tasks using computers, our models need real examples of how people actually use them — things like mouse movements, clicking buttons, and navigating dropdown menus. To help, we’re launching an internal tool that will capture these kinds of inputs on certain applications to help us train our models. There are safeguards in place to protect sensitive content, and the data is not used for any other purpose.”

This trend reveals a troublesome privacy dimension of the AI industry. Last week it was reported that old startups are being scavenged for their corporate communications (like Slack archives and Jira tickets), and converted into AI training data.

Cash App, the banking and payments app run by Block, has added support for parent-managed kids accounts. The new accounts include key benefits from the service’s normal account, with an eye towards teaching financial literacy to younger users ages 6 to 12. Cash App first allowed teenage users on its platform in 2021.

As part of the “expanded Cash App Families experience,” eligible legal guardians and parents can create managed accounts that offer “a dedicated place on the platform to send allowances, set aside savings, and track spending for their child, kickstarting their path to financial independence,” Cash App says. Adults managing these accounts will be able to set up recurring transfers, see how their child is spending and do things like lock their child’s account to prevent transactions. Kids will get a custom debit card and the ability to receive payments from up to five trusted accounts, though notably they won’t be able to access Cash App itself.

Cash App says managed accounts are designed for kids 6 through 12. Once those kids turn 13, Cash App says parents will be able to choose to convert their account to a “sponsored account” to unlock more features, like the ability to send and receive payments, invest in stocks or trade crypto. Those sponsored accounts are technically still monitored and controlled by a parent or legal guardian, but they do give 13-year-olds more control over how they use their money.

A parent-managed account for kids is not a new idea in the fintech space, though Cash App is trying to reach a younger audience than some of its competitors. Venmo rolled out access to its payment platform to teens between the ages of 13 to 17 in 2023. Separately, both Apple and Google also offer their own kids accounts in Google Wallet and Apple Cash Family.

Anthropic’s buzzy announcement about using AI to improve cybersecurity earlier this month was met with plenty of skepticism. However, Mozilla shared some details that support use of the company’s special Claude Mythos Preview model as a way to protect critical services. Using Mythos helped Mozilla’s team find and patch 271 vulnerabilities in the latest release of the Firefox browser. “So far we’ve found no category or complexity of vulnerability that humans can find that this model can’t,” the foundation said.

The blog post from Mozilla feels like a positive sign for Anthropic’s Project Glasswing. Obviously the AI company would want to put itself in the best possible light while presenting its own initiative, but there’s something encouraging about hearing the benefits from a third party. Mozilla also noted that in its time with Claude Mythos, the AI wasn’t able to turn up any bugs that a human wouldn’t have been able to find, given enough time and resources, which indicates that AI isn’t presently able to do more to crack cybersecurity protections than a person can.

An organizaion successfully using AI for good is certainly a refreshing change of pace in tech news. And for those Firefox users who aren’t personally interested in applying any generative AI in their browsing, Mozilla has given the option to turn it all off for the past several months.

Google on Monday unveiled the most significant upgrade to its autonomous research agent capabilities since the product’s debut, launching two new agents — Deep Research and Deep Research Max — that for the first time allow developers to fuse open web data with proprietary enterprise information through a single API call, produce native charts and infographics inside research reports, and connect to arbitrary third-party data sources through the Model Context Protocol (MCP).

The release, built on Google’s Gemini 3.1 Pro model, marks an inflection point in the rapidly intensifying race to build AI systems that can autonomously conduct the kind of exhaustive, multi-source research that has traditionally consumed hours or days of human analyst time. It also represents Google’s clearest bid yet to position its AI infrastructure as the backbone for enterprise research workflows in finance, life sciences, and market intelligence — industries where the stakes of getting information wrong are extraordinarily high.

“We are launching two powerful updates to Deep Research in the Gemini API, now with better quality, MCP support, and native chart/infographics generation,” Google CEO Sundar Pichai wrote on X. “Use Deep Research when you want speed and efficiency, and use Max when you want the highest quality context gathering & synthesis using extended test-time compute — achieving 93.3% on DeepSearchQA and 54.6% on HLE.”

Both agents are available starting today in public preview via paid tiers of the Gemini API, accessible through the Interactions API that Google first introduced in December 2025.

Why Google built two research agents instead of one

The launch introduces a tiered architecture that reflects a fundamental tension in AI agent design: the tradeoff between speed and thoroughness.

Deep Research, the standard tier, replaces the preview agent Google released in December and is optimized for low-latency, interactive use cases. It delivers what Google describes as significantly reduced latency and cost at higher quality levels compared to its predecessor. The company positions it as ideal for applications where a developer wants to embed research capabilities directly into a user-facing interface — think a financial dashboard that can answer complex analytical questions in near-real time.

Deep Research Max occupies the opposite end of the spectrum. It leverages extended test-time compute — a technique where the model spends more computational cycles iteratively reasoning, searching, and refining its output before delivering a final report. Google designed it for asynchronous, background workflows: the kind of task where an analyst team kicks off a batch of due diligence reports before leaving the office and expects exhaustive, fully sourced analyses waiting for them the next morning.

The Google DeepMind team framed the distinction on X: “Deep Research: Optimized for speed and efficiency. Perfect for interactive apps needing quicker responses. Deep Research Max: It uses extra time to search and reason. Ideal for exhaustive context gathering and tasks happening in the background.”

“Deep Research was our first hosted agent in the API and has gained a ton of traction over the last 3 months, very excited for folks to test out the new agents and all the improvements, this is just the start of our agents journey,” Logan Kilpatrick, who leads developer relations for Google’s AI efforts, wrote on X.

MCP support lets the agents tap into private enterprise data for the first time

Perhaps the most consequential feature in today’s release is the addition of Model Context Protocol support, which transforms Deep Research from a sophisticated web research tool into something more closely resembling a universal data analyst.

MCP , an emerging open standard for connecting AI models to external data sources, allows Deep Research to securely query private databases, internal document repositories, and specialized third-party data services — all without requiring sensitive information to leave its source environment. In practical terms, this means a hedge fund could point Deep Research at its internal deal-flow database and a financial data terminal simultaneously, then ask the agent to synthesize insights from both alongside publicly available information from the web.

Google disclosed that it is actively collaborating with FactSet, S&P, and PitchBook on their MCP server designs, a signal that the company is pursuing deep integration with the data providers that Wall Street and the broader financial services industry already rely on daily. The goal, according to the blog post authored by Google DeepMind product managers Lukas Haas and Srinivas Tadepalli, is to “let shared customers integrate financial data offerings into workflows powered by Deep Research, and to enable them to realize a leap in productivity by gathering context using their exhaustive data universes at lightning speed.”

This addresses one of the most persistent pain points in enterprise AI adoption: the gap between what a model can find on the open internet and what an organization actually needs to make decisions. Until now, bridging that gap required significant custom engineering. MCP support, combined with Deep Research’s autonomous browsing and reasoning capabilities, collapses much of that complexity into a configuration step. Developers can now run Deep Research with Google Search, remote MCP servers, URL Context, Code Execution, and File Search simultaneously — or turn off web access entirely to search exclusively over custom data. The system also accepts multimodal inputs including PDFs, CSVs, images, audio, and video as grounding context.

Native charts and infographics turn AI reports into stakeholder-ready deliverables

The second headline feature — native chart and infographic generation — may sound incremental, but it addresses a practical limitation that has constrained the usefulness of AI-generated research outputs in professional settings.

Previous versions of Deep Research produced text-only reports. Users who needed visualizations had to export the data and build charts themselves, a friction point that undermined the promise of end-to-end automation. The new agents generate high-quality charts and infographics inline within their reports, rendered in HTML or Google’s Nano Banana format, dynamically visualizing complex datasets as part of the analytical narrative.

“The agent generates HTML charts and infographics inline with the report. Not screenshots. Not suggestions to ‘visualize this data.’ Actual rendered charts inside the markdown output,” noted AI commentator Shruti Mishra on X, capturing the practical significance of the change.

For enterprise users — particularly those in finance and consulting who need to produce stakeholder-ready deliverables — this transforms Deep Research from a tool that accelerates the research phase into one that can potentially produce near-final analytical products. Combined with a new collaborative planning feature that lets users review, guide, and refine the agent’s research plan before execution, and real-time streaming of intermediate reasoning steps, the system gives developers granular control over the investigation’s scope while maintaining the transparency that regulated industries demand.

How Deep Research evolved from a consumer chatbot feature to enterprise platform infrastructure

Today’s release crystallizes a strategic narrative Google has been building for months: Deep Research is not merely a consumer feature but a piece of infrastructure that powers multiple Google products and is now being offered to external developers as a platform.

The blog post explicitly notes that when developers build with the Deep Research agent, they tap into “the same autonomous research infrastructure that powers research capabilities within some of Google’s most popular products like Gemini App, NotebookLM, Google Search and Google Finance.” This suggests that the agent available through the API is not a stripped-down version of what Google uses internally but the same system, offered at platform scale.

The journey to this point has been remarkably rapid. Google first introduced Deep Research as a consumer feature in the Gemini app in December 2024, initially powered by Gemini 1.5 Pro. At the time, the company described it as a personal AI research assistant that could save users hours by synthesizing web information in minutes. By March 2025, Google upgraded Deep Research with Gemini 2.0 Flash Thinking Experimental and made it available for anyone to try. Then came the upgrade to Gemini 2.5 Pro Experimental, where Google reported that raters preferred its reports over competing deep research providers by more than a 2-to-1 margin. The December 2025 release was the pivot to developer access, when Google launched the Interactions API and made Deep Research available programmatically for the first time, powered by Gemini 3 Pro and accompanied by the open-source DeepSearchQA benchmark.

The underlying model driving today’s improvements is Gemini 3.1 Pro, which Google released on February 19, 2026. That model represented a significant leap in core reasoning: on ARC-AGI-2, a benchmark evaluating a model’s ability to solve novel logic patterns, 3.1 Pro scored 77.1% — more than double the performance of Gemini 3 Pro. Deep Research Max inherits that reasoning foundation and layers autonomous research behaviors on top of it, achieving 93.3% on DeepSearchQA (up from 66.1% in December) and 54.6% on Humanity’s Last Exam (up from 46.4%).

Google faces a crowded field of competitors building autonomous research agents

Google is not operating in a vacuum. The launch arrives amid intensifying competition in the autonomous research agent space. OpenAI has been developing its own agent capabilities within ChatGPT under the codename Hermes, which includes an agent builder, templates, scheduling, and Slack integration, according to reports circulating on social media. Perplexity has built its business around AI-powered research. And a growing ecosystem of startups is attacking various slices of the automated research workflow.

What distinguishes Google’s approach is the combination of its search infrastructure — which gives Deep Research access to the broadest and most current index of web information available — with the MCP-based connectivity to enterprise data sources. No other company currently offers a research agent that can simultaneously query the open web at Google Search’s scale and navigate proprietary data repositories through a standardized protocol. The pricing structure also signals Google’s intent to drive adoption: according to Sim.ai, which tracks model pricing, the Deep Research agent in the December preview was priced at $2 per million input tokens and $2 per million output tokens with a 1 million token context window — positioning it as cost-competitive for the volume of research output it generates.

Not everyone greeted the announcement with unalloyed enthusiasm, however. Several users on X noted that the new agents are available only through the API, not in the Gemini consumer app. “Not on Gemini app,” observed TestingCatalog News, while another user wrote, “Google keeps punishing Gemini App Pro subscribers for some reason.” Others raised concerns about the presentation of benchmark results, with one user arguing that Google’s charts could be “misleading” in how they represent percentage improvements. These complaints point to a broader tension in Google’s AI strategy: the company is increasingly directing its most advanced capabilities toward developers and enterprise customers who access them through APIs, while consumer-facing products sometimes lag behind.

What Deep Research Max means for finance, biotech, and the future of knowledge work

The practical implications of today’s launch are most immediately felt in industries that depend on exhaustive, multi-source research as a core business function. In financial services, where analysts routinely spend hours assembling due diligence reports from scattered sources — SEC filings, earnings transcripts, market data terminals, internal deal memos — Deep Research Max offers the possibility of automating the initial research phase entirely. The FactSet, S&P, and PitchBook partnerships suggest Google is serious about making this work with the data infrastructure that financial professionals already use.

In life sciences, the blog post notes that Google has collaborated with Axiom Bio, which builds AI systems to predict drug toxicity, and found that Deep Research unlocked new levels of initial research depth across biomedical literature. In market research and consulting, the ability to produce stakeholder-ready reports with embedded visualizations and granular citations could compress project timelines from days to hours.

The key question is whether the quality and reliability of these automated outputs will meet the standards that professionals in these fields demand. Google’s benchmark numbers are impressive, but benchmarks measure performance on standardized tasks — real-world research is messier, more ambiguous, and often requires the kind of judgment that remains difficult to automate. Deep Research and Deep Research Max are available now in public preview via paid tiers of the Gemini API, with availability on Google Cloud for startups and enterprises coming soon.

Eighteen months ago, Deep Research was a feature that helped grad students avoid drowning in browser tabs. Today, Google is betting it can replace the first shift at an investment bank. The distance between those two ambitions — and whether the technology can actually close it — will define whether autonomous research agents become a transformative category of enterprise software or just another AI demo that dazzles on benchmarks and disappoints in the conference room.

SpaceX and AI company Cursor have struck a new partnership that could see the owner of X buy the AI company for $60 billion later this year. “SpaceXAI and  @cursor_ai  are now working closely together to create the world’s best coding and knowledge work AI,” SpaceX wrote in a post on X.

According to SpaceX, the deal allows for it to either invest $10 billion into the company known for its AI coding tool, or acquire it entirely “later this year” for $60 billion. If an acquisition were to happen, it’s not clear at what point Cursor could officially join the fold of Elon Musk’s rapidly expanding and increasingly enmeshed web of companies. SpaceX bought xAI, the billionaire’s AI company that also controls X, earlier this year. SpaceX is currently getting ready to go public this summer in what will likely be the biggest initial public offering (IPO) in history.

Cursor, which has reportedly been in talks to raise its own $2 billion round of funding, is known for its AI coding tool of the same name that’s become the vibe coding platform of choice for many developers. It allows people to use either its own models or those from other leading AI companies, including OpenAI, Google, Anthropic and xAI.

In a statement, Cursor said its partnership with SpaceX will “accelerate our model training efforts” while addressing infrastructure-related issues that have slowed it down in the past. “We’ve wanted to push our training efforts much further, but we’ve been bottlenecked by compute,” the company said. “With this partnership, our team will leverage xAI’s Colossus infrastructure to dramatically scale up the intelligence of our models for coding and beyond.”

Strands is the NYT’s latest word game after the likes of Wordle, Spelling Bee and Connections – and it’s great fun. It can be difficult, though, so read on for my Strands hints.

Want more word-based fun? Then check out my NYT Connections today and Quordle today pages for hints and answers for those games, and Marc’s Wordle today page for the original viral word game.