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AI Leaves the Chat: Wet Labs, Megawatts, and Default Models

AI Leaves the Chat: Wet Labs, Megawatts, and Default Models

GPT-5 boosts cloning 79×, Tesla scales to 100–200 MW, and Gemini Flash + GPT Image 1.5 push speed and volume into the mainstream.

Martin Musiol
December 17, 2025 • Estimated Reading Time: 6 minutes

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Friends,

Quick pulse-check on the week: AI is escaping the chat box—into wet labs, into datacenters, and into your default apps. Here’s what matters.

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GPT-5 Ran a Wet Lab (Seriously)

OpenAI + Red Queen Bio put GPT-5 in charge of a real experimental loop and it boosted molecular cloning efficiency by 79×.

GPT-5 iterated on a cloning workflow across multiple rounds; humans/robots & machines executed, GPT-5 steered.
Result: 79× more sequence-verified clones from the same input DNA vs baseline.
Key finding AI had: a new enzyme mechanism (RecA + gp32), packaged as a new approach.
Why it matters: this is what “AI for science” looks like when it touches reality—tight loops, real data, real gains.

GPT-5 wet lab experiment image Alt text: Diagram illustrating GPT-5 running an automated molecular cloning loop with human and robotic execution, resulting in a 79× increase in sequence-verified clones.

(Source)

100–200 MW for Robot Training: Tesla’s Cortex 2

The physical footprint keeps growing: Tesla has a new permit trail around Cortex 2, a datacenter-scale cluster tied to training workloads (FSD + increasingly, Optimus).

Filings point to a 100–200 MW-class facility (fire detection/alarm work scoped for that scale).
That’s “tens of thousands of GPUs + cooling + storage” territory.
Big picture: progress isn’t just better models—it’s brute-force infrastructure, built fast.

Tesla Cortex 2 datacenter image Alt text: Aerial view of Tesla’s Cortex 2 datacenter construction site, representing a 100–200 megawatt compute facility for AI and robotics training.

(Source)

Gemini 3 Flash: Pro-ish Intelligence at Flash Speed

Google launched Gemini 3 Flash—the speed/cost monster that’s meant to be the default workhorse. It is already in Cursor.

Compared to Gemini 3 Pro, Flash is positioned as:

~3× faster latency
~¼ the cost (and higher rate limits)
Stronger agentic coding: 78% SWE-bench Verified
Very strong multimodal (text/image/video/audio), close to Pro

Trade-off:

Slightly lower peak intelligence vs Pro (e.g., 33.7% vs 37.5% on Humanity’s Last Exam, no tools).
Pro still wins on hardest math, deepest reasoning, and nastiest code.

Flash is now the default/free model in the Gemini app.

— (@)

GPT Image 1.5: OpenAI’s New Flagship Image Model

OpenAI released GPT-Image-1.5 (a.k.a. the new ChatGPT Images).

Up to 4× faster generation
More reliable, surgical edits (change what you ask—keep the rest)
Better instruction following + denser text rendering
Also ~20% cheaper than the prior image model in the API

You’ll see the comparison in the graph below (incl. vs Nano Banana Pro).

Generated with GPT-Image-1.5 (Infographic of LM Arena Board):

Top AI image generation models infographic Alt text: Infographic ranking top AI image generation models, led by OpenAI’s GPT-Image-1.5, followed by Google Gemini 3 Pro and Flux models, with scores and future trends highlighted. GPT-Image-1.5 comparison infographic (LM Arena Board) Alt text: Comparison chart generated with GPT-Image-1.5 showing performance scores of leading AI image generation models, including GPT-Image-1.5, Gemini 3 Pro, and Flux.

OpenAI Goes Geopolitical + Everyone Doubles Down on Compute

OpenAI hired former UK Chancellor George Osborne to lead “OpenAI for Countries”—basically “Stargate for Countries,” exporting an “AI stack” via government partnerships.

And the meta-trend is still the same: compute wins. Greg Brockman’s blunt take: it’s what keeps working for intelligence.