Eng Lead - AI Developer Tools @ Google
New York City
Joined March 2025
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Bryan Morgan retweeted
Had early access to Gemini 4 Argon - so I asked for slides on the AI power bottleneck.
It synthesized 8 years of 10-K filings, FERC queue data across all 6 US grid operators, 17 gigawatt cluster buildouts, and a decade of GPU thermal specs.
And I got this interactive deck 🤯
Bryan Morgan retweeted
At Box, we've been testing Gemini 4 Argon on our Box AI Complex Work eval for the past week. It's a huge upgrade over Gemini 3.1 Pro, jumping by 14 percentage points across all our tests, and firmly at the frontier.
In our evaluation, we give the Box Agent (using Gemini 4 Argon) a variety of complex enterprise tasks involving enterprise documents in a range of industries. These tasks approximate the kind of hard, time-consuming work that knowledge workers do every day with their enterprise content. Gemini 4 is a huge leap on effectively every dimension of this work.
Gemini 4 is far more capable on math, logic, analytics, and deep knowledge of a variety of business domains. It also works far harder to complete tasks fully without stopping short of the desired result. This is incredibly important for complex enterprise workflows that depend on robust task execution.
Here are some example wins that we saw to put the improvements in perspective:
* Financial services (+18 points): a funding-gap analysis for a family-owned company, where the brief spelled out every step of the projected statements, down to the interest on the bank loan and the overdraft for each year. Gemini 4 Argon built them exactly as specified, every line broken out and carried through to the funding gap. Gemini 3.1 Pro folded the interest into a single figure.
* Healthcare (+25 points): an intensive care management plan where both models spotted a dangerous potassium level and a drug interaction. Only Gemini 4 Argon went back and pulled the offending drugs from the final regimen, so its plan actually matched its own diagnosis.
* Technology (+37 points): a product performance review deciding which of two regional markets should have its cloud infrastructure optimized first. Gemini 4 Argon pulled the right month's user, retention and per-user figures for both regions and correctly flagged North America. Gemini 3.1 Pro often pulled the wrong rows, at one point reporting retention above 100%, and pointed the effort at the wrong region.
* Energy (+16 points): an energy consumption report across two facilities that had to flag any gaps in the meter data. Gemini 4 Argon consistently caught that the data center's smart meter log stops in November. Gemini 3.1 Pro often missed it, at times declaring the logs complete.
* Consumer products (+33 points): a product launch evaluation deciding which production expansion to fund. Gemini 4 Argon saw that existing inventory could bridge an early capacity gap, which made the smaller expansion viable with an earlier launch and a faster break-even. Gemini 3.1 Pro defaulted to the larger expansion and the later timeline.
Gemini 4 Argon will be available in the Box AI Studio shortly for anyone to build custom Box Agents with.
Bryan Morgan retweeted
Today we are rolling out Gemini 4 Argon globally in the Gemini API, to Pro + Ultra in Gemini App, and Ultra subs in Antigravity!
Argon is a huge step forward for Gemini, and a labor of love. This model has been battle tested by over 100,000 people at Google over the last month, with so much new innovation, ambition, and hard work baked into this release. The team has been heads down over the last 6 months working to make this possible across the stack (research, infra, safety, eng, etc).
We rolled the model out to cyber defenders as part of our fairwind program and early access customers over the last 2 weeks, it has been amazing to see and talk to folks who are now using this model daily. Seeing the models writing capability, raw knowledge and persistence for coding, paired with new features like 1M token output when the model needs to work more has gotten me even more excited about our path ahead.
This is just the start of the Gemini 4 era. As you saw with 3.5, 3.6, 3.7, and 3.8 Flash, our iteration speed with Gemini is fast : ) Now with Gemini 4 Argon to work from, I am cannot wait to see the progress continue, and most importantly see what this new model enables for the world.
This is such a special moment in the arc of technology, as millions of people everyday tackle problems they could never have thought possible before. Gemini 4 Argon was built to help you tackle the hardest problems. I have personally felt this spark with Argon and hope you do too. Onwards!!
Bryan Morgan retweeted
I don’t think you have a dog in this fight but if you did you would have shot it.
I’ll take @ellalangleymsic over Kacey Musgraves any day.
And if I’m choosin’ Texas, I’ll take Ken Paxton over James Talarico.
Bryan Morgan retweeted
Gemini Agent is built for high-ceiling work where EACH task gets its own computer.
You can choose Gemini 4 Argon or Claude Opus 5.5.
10 things that make it a universal work Agent (read till the end to see what a REAL work agent looks like):
1. One Agent, one API
Chat, autonomous objectives, and code generation from a single interface. Assign it work, schedule it, or have it respond to events.
2. Coworker agents with their own identities
It creates job-specific sub-agents, each with its own identity, and computer running in parallel for hours or days.
Each Coworker agent gets its own Workspace account: an agent email, calendar, Drive, and presence in your company directory. You mention it in Chat. It acts under its own identity, not yours, and sees only what you share.
3. Everywhere, including headless
Web, iOS, Android, Windows, Mac, command line, Workspace, Microsoft 365, Slack. It also runs as a headless agent with no dedicated interface.
4. Work 24/7 in the cloud
It keeps one set of memories, context, and personalization graph across every device and channel. Work that takes hours or days keeps running after you close your laptop. You never have to re-brief it.
5. Layers of memory that improve over time
- Session: the task in front of it, even when it runs for days.
- Semantic: a structured knowledge base it builds as it reads documents, talks to people, and works with other agents.
- Procedural: how a job gets done, including skills it writes for itself.
- Episodic: everything it has done before.
6. Proactive by design
It does not wait to be prompted. An email asking you for a deck gets flagged as delegatable, one click to hand off. Your inbox ranks by what matters, not by what just arrived, and tells you why.
7. Tools and skills, with company registries
Salesforce, ServiceNow, Jira, Git, Confluence, Microsoft 365, Slack, BigQuery, Databricks, Postgres, Snowflake, your desktop files, and any MCP server inside or outside your network. Teams publish their own tools and skills to a shared company registry.
8. Model Choice so you are not locked in
It runs each job on the model that fits best, running across the Gemini and Claude models from Anthropic today, with other private and open models to come.
9. Governed like an employee
Every agent gets a cryptographically attested identity with least-privilege permissions, and an audit trail attributed to the agent rather than a person. Agents run in a sandbox behind Agent Gateway, an AI network firewall. Write the policy once and it applies to every agent.
10. Costs you can actually cap
Smart Routing puts each workload on the model with the best performance at the lowest cost. Set a hard spend limit in Cloud Billing and that project's agent pauses until you resume it in one click.
Thank you for reading so far. Stay tuned for updates.
We are 100% committed to make it the best Agent for work.
Bryan Morgan retweeted
The New York Times, Best Books of the Year (So Far)
BIOLOGICAL WAR: A SCENARIO
nytimes.com/2026/04/27/books…
Bryan Morgan retweeted
I usually trust Bloomberg, but this take on Argon is completely detached from reality. Internal sentiment has been overwhelmingly positive—it's easily the best-received Gemini model to date. Seeing claims of widespread internal concerns genuinely baffled me.
$GOOGL EMPLOYEES RAISE CONCERNS OVER GEMINI 4 CODING PERFORMANCE
Google is preparing to launch Gemini 4, but some employees with access to the model say its strong benchmark results aren’t fully translating into real-world performance, according to Bloomberg.
The main concerns are around coding. Some internal testers say Gemini 4 struggles with certain coding tasks and front-end design, while the model is also said to be very large, potentially making it more expensive to run.
Google disputes that Gemini 4 is underperforming and says there is broad internal consensus that the model is at the frontier.
Bloomberg also reports Google abandoned Gemini 3.5 Pro after previously planning to release it in June.
Internally, views appear mixed: some employees believe Gemini 4 still trails the latest models from OpenAI and Anthropic in certain areas, while others believe it has caught up.
Gemini 4 is said to perform particularly well on multimodal tasks, including understanding video, along with safety, cybersecurity and natural communication.
Source: Bloomberg
Bryan Morgan retweeted
It’s pretty competitive nitter.cf/_mohansolo/status/2105…
Bryan Morgan retweeted
Google says Gemini 4 Argon agents have already freed over 300 TiB of memory across its data centers, with a 1 million token output limit and agents already optimizing its own infrastructure.
The agents analyzed profiling data, found memory optimizations and applied changes that were subsequently rolled out.
Argon agents are also working on C/C++ to Rust migrations reaching 800,000+ lines of kernel code, with extensive audits and testing before deployment.
For its video decoder, Google says agents replaced 32,000 lines of SIMD code with safe Rust, making the existing Rust port 2.7x faster while preserving identical video output.
These are expensive engineering tasks inside infrastructure Google already operates.
This release seems to be a much bigger deal than people think!
Join us in NYC!
We're hiring an Engineering Manager for our AI developer tools frontend team: goo.gle/4xS84WR
This represents the next big step in our transition from Gemini CLI to Antigravity. Starting today, Antigravity is now available to enterprise customers (via Gemini Enterprise).
In addition, we're also launching a variety of Antigravity IDE integrations today!
🎉 We're expanding @antigravity for enterprise customers!
Read how this expansion will help even more eligible Gemini Enterprise customers and developers accelerate software delivery—with the security and control organizations need to innovate at scale → goo.gle/46bUTEj
Bryan Morgan retweeted
Ordinary Abundance. This is the greatest thing I've read in weeks. No kidding. This should win a Pulitzer in a category not yet invented. ordinaryabundance.com/
