Engineer @GoogleDeepMind. Prev. Product @ GDM, Founder/CEO @ Scaled Inference, Engineer @Google (Search, Research, X, Brain).
Palo Alto, CA
Joined June 2008
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Olcan retweeted
i've reproduced the results from the MIT blog post today on weight-space programming. but one additional result i have to add: this is a new, step-change emerging capability that's appeared in just the latest set of models.
Olcan retweeted
AI may well be able to write massive software projects “from scratch”, but that almost certainly isn’t the fastest / highest performance / highest security / most token efficient way to do things. Good building blocks matter.
In addition to RL’ing the agents on final task loss, we should try to get them to regularize the building blocks as well. What would the standard library look like that makes the agents most effective?
It is likely at the limit that it turns out to be some alien organization of code, but given the existing human-bias in training, I bet that in the near term it would be lovely code that old programmers would appreciate.
I suspect the same could be said for mathematics, but that is out of my lane.
Olcan retweeted
Sharing early evals on Gemini 4 Carbon. Strongest model that we've seen so far for long-form research and answer quality, with a new SoTA on the Wide-And-Deep-Research (WANDR) benchmark. Coming soon to all Perplexity Pro and Max users inside Computer, once we have sufficient token capacity. Congrats @GoogleDeepMind!
Olcan retweeted
A ‘hello world’ milestone for Project Suncatcher 🚀
After launching our prototype satellite a few weeks ago, we’ve now successfully sent a command up to orbit, booted up the TPU, ran the prompt, and received our first complete response back on earth - a haiku!
Olcan retweeted
Less than a year ago, launching Gemini 3 Pro was a big-bang event where the whole team celebrated #1 benchmark numbers.
Today Gemini 4 Carbon hits #1 on the AA Index, yet the feeling inside is distinctly different. We've been dogfooding continuously for weeks, and we've almost adapted to the new intelligence -- taking ambitious code rewrites for granted, leaving loops overnight that launch dozens of experiments to repro, root-cause, and fix mysterious bugs, and using swarms to inspect and fix RL data.
Launches just feel like periodic checkpoints now.
Just two weeks after rolling out Gemini 4 Argon, excited to share a preview of Gemini 4 Carbon - our next frontier model. It has state-of-the-art long horizon coding capabilities driving SOTA performance across the cybersecurity defense lifecycle including detection, validation, and remediation. It also ranks #1 overall on the recently updated v5.0 of Artificial Analysis Intelligence Index at 59.
We're starting by rolling out to a subset of cyber defenders through our Fairwind Program this week and soon expanding access further. Read more at: blog.google/innovation-and-a…
Olcan retweeted
Google’s Gemini 4 Carbon is the new #1 on the Artificial Analysis Intelligence Index v5, and second only to Claude Opus 5.5 on the Artificial Analysis Coding Agent Index
Gemini 4 Carbon scores 59 on Intelligence Index v5, driven by leading performance in complex reasoning, knowledge, and scientific tasks. It also has large gains in coding and knowledge work, particularly when compared to other Gemini models, but it is not leading in all these areas.
As with launch of Gemini 4 Argon, Gemini 4 Carbon is being rolled out to selected users and is not yet publicly available. Google is offering a 50% launch discount, with no confirmed end date. All our charts and figures use standard pricing for both Gemini 4 Carbon and Argon.
Key results for Gemini 4 Carbon with high reasoning:
➤ Gemini 4 Carbon leads the Intelligence Index v5, with the #1 score in 4 of the 9 Index evaluations: HLE-Diamond, Terminal-Bench-Science, AA-Omniscience, AutomationBench-AA. Carbon makes substantial gains elsewhere but is not leading in coding and knowledge work
➤ Behind only Claude Opus 5.5 on coding agents: With Antigravity CLI, Gemini 4 Carbon (high) scores 59 only 3 points behind Claude Code with Claude Opus 5.5 (max, 62) on the Artificial Analysis Coding Agent Index. Carbon ranks second on both Terminal-Bench 4.0 (64%) and FrontierCode v1.1 (51%), behind Claude Opus 5.5 (max) at 56%. Google has historically underperformed in this area and so this improvement stands out
➤ Improved agentic knowledge work, but not yet leading: On AA-Briefcase, Gemini 4 Carbon (high) scores 1609 Elo, a 126-point improvement over Gemini 4 Argon (high, 1483), but still behind models including Grok 4.7 (xhigh, 1638), Muse Wonder (max, 1659) and Kimi K3.1 Pro (1678). The largest gain comes from Presentation, up 293 Elo to 1598, alongside a 120-point improvement in Analytical Quality to 1671
➤ Lower Cost per Task than Claude Opus 5.5 for comparable overall intelligence: Gemini 4 Carbon costs $7.76 per Intelligence Index task, ~23% less than Claude Opus 5.5 (max, $10.04) but ~1.2x GPT-6 Astra (max, $6.25). It uses 123k output tokens per task, 29% fewer than Claude Opus 5.5 (max, 173k). At the same $4/$20 standard pricing, its Cost per Task is in line with Gemini 4 Argon ($7.68), while scoring 9 points higher on the Intelligence Index
➤ Leading AA-Omniscience v1.1 score, with low hallucination: Gemini 4 Carbon scores 54 on our recently updated AA-Omniscience v1.1, 6 points ahead of Claude Opus 5.5 (max, 48) and 8 points ahead of Gemini 4 Argon (46). Accuracy (% correct) rises 10 points over Argon to 58%, while its hallucination rate rises from 4% to 11%. This is still the second lowest of any model, behind only Argon, compared with 50% for both Claude Opus 5.5 (max) and GPT-6 Astra (max)
Key model details:
➤ Context Window: 1M tokens
➤ Multimodality: Text, image, video, and speech input, with text output
➤ Pricing: $4/$20 per 1M input/output tokens, the same as Gemini 4 Argon’s standard pricing, with a 50% discount. Cached input tokens receive a 95% discount ($0.20 per 1M)
Congratulations to the @GoogleDeepMind team!
Olcan retweeted
People have expected that RSI would magnify small leads and mean one lab runs away with it all. That may happen, but the frontier AI race also has a built in self-stabilizing dynamic that pushes the other way.
If you build a spectacular model, you sell out and may have to canibalization all your training and research compute to stay afloat and keep your customers from rioting, as well as needing to direct a lot of your efforts to inference efficiency on the existing model. So your next model’s quality may suffer a lot.
If you release a really bad model, no one uses it, and you have a ton of unneeded inference compute, and can scale faster and experiment more for your next model runs, which can mean a much better model.
Or, in meme form: Big compute builds strong models. Strong models lead to little compute. Little compute leads to weak models. Weak models lead to big compute.
I'm giving the "Agentic Linux Desktop" experience a try, and I can say now that it is extremely jagged: think a mix of having the custom UX of your dreams, mixed with a few terrible annoyances (e.g. chrome randomly exiting on some wakes) that can take weeks for SOTA agents to debug with your help.
Another thing: I have not run into an issue that could not be fixed or worked around, eventually. So "we can fix everything" feels like an honest slogan for the movement.
Olcan retweeted
I’m so happy to be able to share some of our work with you. Our model Ampersand, we trained to help people come closer to their dreams.
I love the humans& team and am so glad I can share this moment with them and you.
No one changes the world alone
Introducing Ampersand - the first model built for us. Ampersand is a collaborator trained to help people achieve their individual and collective goals. In multi-month simulations, users collaborating with Ampersand accomplished more than with Fable and other frontier models
Available to everyone through our API
Olcan retweeted
Astra dethroned
Gemini 4 Argon is the best "vision" model
#1 for detection:
- tight, precise boxes
- dense scenes
#1 for text extraction:
- small text
- handwritten text
- complex tables
- rotated text
4× cheaper than Astra at high
Olcan retweeted
Argon is so good at robotics that our whole team is having an existential crisis right now, enjoy!
developers.googleblog.com/ge…
Olcan retweeted
Gemini 4 Argon (gemini-4-argon) is now live across Google AI Studio, the Gemini API, Google Antigravity, Gemini Enterprise, and the Gemini app!
Gemini 4 Argon is awesome:
1. Unified 1M Context & 1M Output (Long Decode): Input, thinking, and output now share a single 1M-token window—lifting the old 64K output cap by 16x. With background=True on the Interactions API, our servers handle multi-hop continuation and checkpoint recovery automatically so the model can think for tens of minutes and emit complete multi-file repo migrations or formal proofs in a single pass.
2. Tunable Reasoning Depth: Control compute per turn via thinking_level ("low" | "medium" default | "high") plus native agentic video processing (media_processing="agentic").
3. Real-World Engineering: 77.9% on DeepSWE v1.1, 91.9% on Vibe Code Bench, and 68.9% on the Vals Index. Internally, we used Argon to migrate 800K+ lines of Fuchsia Zircon C/C++ kernel code to memory-safe Rust and replace 32K lines of hand-written SIMD in libgav1 for a 2.7x speedup.
4. Token Economics: Launching at an introductory rate of $2 / 1M input and $10 / 1M output tokens through Nov 30, with 95% off cached inputs ($0.10 / 1M) via automatic implicit caching.
