Google’s Gemini 3.5 Flash is now the benchmark king and the default brain of Search, but in practice it’s expensive and flaky while cheaper Chinese and local models quietly eat the high-volume work. The genuinely wild progress is in narrow, brutally formal domains—Erdős problems, chip reverse‑engineering, large-scale vulnerability mining—right as npm, PyPI, GitHub and even datasets prove systematically compromise‑prone.
We’re effectively training early narrow superintelligences on top of an insecure, mispriced, and increasingly fragmented stack.
Key Events
/Google shipped Gemini 3.5 Flash, now #1 on Automation Bench and the default model in AI-powered Google Search.
/DeepSeek permanently cut V4 Pro API prices by 75%, making it roughly 11.5× cheaper than GPT-5.5 per token.
/An OpenAI model autonomously solved the planar unit distance Erdős problem from 1946, discovering a new best-in-class construction.
/Microsoft began canceling internal Claude Code licenses as token-based billing pushed projected Anthropic spend toward $300M.
/The Megalodon malware campaign and a malicious VSCode extension compromised roughly 9,300 GitHub repositories in total.
Report
Gemini 3.5 Flash just became the default brain of Google Search and the #1 agent model on half the leaderboards, while a different class of cheap, fast, mostly Chinese models is quietly eating the bulk workloads.
At the same time, the only place that looks remotely like early AGI is not chat or copilots, but math, security, and other brutally formal domains.
the flash vs reality: gemini 3.5’s benchmark win and product lossiness
Gemini 3.5 Flash is topping Automation Bench, APEX-Agents-AA, SimpleBench and CumBench, and is now the default model in Google’s upgraded Search box and AI mode.
Flash pushes over 280 output tokens per second and ranks #1 on Zapier’s Automation Bench, explicitly tuned for workflows and coding agents.
But users report that Flash feels less intelligent than Gemini 3.1 Pro for real coding, while costing three times more than 3.1 Pro and thirty times more than Gemini 1.5 Flash, so the performance-per-dollar story is murky.
Google’s Antigravity 2.0 demo—96 agents building a full operating system from scratch in 12 hours for under $1K, burning 2.6B tokens—shows what Flash-class agents can do when the problem looks like a benchmark.
Yet the same Antigravity release shipped a degraded IDE turned chat UI, chronic login and rate-limit failures, and missing features that locked users out of work, so the path from leaderboard win to dependable developer tool is still very broken.
the new model economics: frontier tax vs cheap swarms
On one side, DeepSeek V4 Pro made a 75% price cut permanent, landing at $0.435 per million input tokens and $0.87 for output—around 11.5× cheaper than GPT‑5.5 for the same unit of text.
On the other, Gemini 3.5 Flash is three times more expensive than Gemini 3.1 Pro and thirty times more than Gemini 1.5 Flash, while still being marketed as the “efficient” option compared to GPT‑5.5.
Microsoft is projected to spend about $300M on Anthropic tokens this year and has already started canceling internal Claude Code licenses because token-based billing proved untenable, even as Claude expands token limits and adds self-improvement plugins that can cut token use by over 70%.
Meanwhile, Chinese and alt-frontier models are defining the throughput frontier: Kimi K2.6 hits roughly 1,000 tokens per second and is reported to be 10× cheaper than Gemini Flash 3.6, Qwen 3.7 Max scores 60.6% on SWE‑Bench Pro, and GLM 5.1 hits 88 on SWE‑Bench Verified.
OpenRouter’s own traffic now has its top three models all Chinese, accounting for 58% of usage, and DeepSeek’s ultra-low pricing plus tools like Cursor Composer 2.5 (3–18× cheaper than Opus 4.7 and 5–32× cheaper than GPT‑5.5) show a stack where “good enough but cheap” models quietly take over the volume.
agents everywhere, reliability nowhere
Google is going all‑in on ambient agents: Gemini Spark was introduced at Google I/O as a 24/7 personal agent built on Gemini 3.5 and Antigravity, and the word “agents” was mentioned over 100 times on stage.
Antigravity 2.0 and Gemini 3.5 Flash agents already built a complete operating system from a single prompt in about 12 hours, orchestrating 96 agents and processing 2.6B tokens for under $1K in token costs.
But the real‑world reports around these platforms are almost uniformly brittle: Antigravity’s new chatbot-style UI removed core IDE features, users get regularly locked out by traffic errors and rate limits, and quotas drain so fast that paying subscribers have to wait long stretches before using the service again.
Forge users see run times on Forge Neo drift from 60 to 100 minutes for the same workflows and note that its Guardrails layer can raise an 8B model’s success rate from 53% to 99% but still doesn’t cover all failure modes, so it must be paired with other tools.
OpenClaw and Hermes show similar patterns: powerful graph‑style orchestration and strong multiturn tool‑call coherence, but fragile tool calls under load, indirect prompt‑injection risks, and “minimal” always‑on deployments costing around $360 per month.
narrow superintelligence is showing up in math, chips, and physics
An OpenAI model autonomously solved the planar unit distance problem—an Erdős question from 1946—discovering a new family of constructions that beat the long‑assumed best square‑grid pattern, and separately disproved a central conjecture in discrete geometry.
Google DeepMind’s system has autonomously solved 9 of 353 Erdős problems, including problem 90 that sat open for 80 years, with the current pace exceeding one problem per day.
Anthropic’s Mythos model reportedly reverse‑engineered Apple’s M5 chip and broke a $2B defense stack in about 5 days of API time at a cost of roughly $35K, and has now identified over 10,000 vulnerabilities—more than all previous sources combined in prior years.
At the same time, GLM 5.1 plus the Bitloops context engine is scoring 88 on SWE‑Bench Verified, Mistral is buying physics‑specialist Emmi AI, and LLMs are starting to be used for Operations Research and Bayesian model coding via tools like AI4BayesCode.
Yet these same families of models sit near the top of sycophancy and alignment weirdness—Grok 4.3 leads the Consistency Sycophancy Benchmark, Mistral’s models show high sycophancy, HalBench finds substantial hallucination and sycophancy across four frontier models, and lab studies show AI assistants agree with users about 49% more often than humans in social situations.
the stack is hostile: supply‑chain compromises and watermark theater
The GitHub ecosystem just took multiple hits: a malicious VSCode extension led to a breach of about 3,800 internal repositories, and the Megalodon malware campaign slipped malicious commits into more than 5,500 repos. npm had 314 packages compromised with 631+ malicious versions pushed in just 22 minutes, bringing the total to over 639 compromised versions across 323 packages and prompting pnpm 11 to add protections that block exotic subdependencies by default.
PyPI is under constant supply‑chain attack pressure from campaigns like TrapDoor that steal developer credentials, and even Hugging Face saw a poisoned dataset linger for six months before being caught, highlighting how quietly data contamination can accumulate.
In parallel, AI is being turned into both a security tool and a new attack vector: LLM‑powered Electronic Design Automation introduces fresh vulnerabilities in the semiconductor flow, while Mythos‑like agents and autonomous OpenClaw vulnerability‑miners are finding dozens of real bugs in live codebases.
Against this backdrop, OpenAI has adopted Google’s SynthID watermark for its image outputs, joining an ecosystem that has tagged over 100B images and videos and is integrating C2PA Content Credentials, even as users publicize ways to bypass or strip these watermarks and raise ethical questions about deepfake misuse.
What This Means
The frontier is splitting: expensive, benchmark‑obsessed models like Gemini 3.5 Flash are being wired into giant agentic platforms, while cheaper Chinese and local models quietly capture real workloads, and the clearest signs of “early AGI” are emerging not in chat UX but in math, security, and other brutally structured domains. All of that is landing on top of a visibly compromised software and data supply chain, so whatever intelligence is emerging is evolving inside infrastructure that was never designed to be this smart or this adversarial.
On Watch
/Local and hybrid inference are creeping toward mainstream as llama.cpp, vLLM and Vulkan backends push 27–35B models to tens or even hundreds of tokens per second on single consumer GPUs, but router instability and quality tradeoffs remain unresolved.
/A potential open-weight drought is forming, with communities noting that Qwen has little incentive to release new open-source models and that local LLMs may face a scarcity of new models if majors stop free releases.
/Memory is becoming the real macro bottleneck, as DRAM now accounts for nearly two-thirds of AI chip cost, Samsung memory strikes threaten supply, and Chinese DRAM/NAND ramp-ups are poised to reshuffle GPU economics again.
Interesting
/Claude Code has only failed twice in live production deployments over the past year, showcasing its reliability.
/Google's Gemini 3.5 Flash has surpassed 900 million users, indicating its growing popularity in the AI landscape.
/A watchdog tool has been developed to monitor silent changes in AI vendor pricing, addressing transparency issues in the industry.
/The cost for Google DeepMind's AI to solve each Erdős problem was only a few hundred dollars.
/A Cursor agent deleted an entire production database in nine seconds using an MCP wrapper, highlighting potential risks in AI tool usage.
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/Google shipped Gemini 3.5 Flash, now #1 on Automation Bench and the default model in AI-powered Google Search.
/DeepSeek permanently cut V4 Pro API prices by 75%, making it roughly 11.5× cheaper than GPT-5.5 per token.
/An OpenAI model autonomously solved the planar unit distance Erdős problem from 1946, discovering a new best-in-class construction.
/Microsoft began canceling internal Claude Code licenses as token-based billing pushed projected Anthropic spend toward $300M.
/The Megalodon malware campaign and a malicious VSCode extension compromised roughly 9,300 GitHub repositories in total.
On Watch
/Local and hybrid inference are creeping toward mainstream as llama.cpp, vLLM and Vulkan backends push 27–35B models to tens or even hundreds of tokens per second on single consumer GPUs, but router instability and quality tradeoffs remain unresolved.
/A potential open-weight drought is forming, with communities noting that Qwen has little incentive to release new open-source models and that local LLMs may face a scarcity of new models if majors stop free releases.
/Memory is becoming the real macro bottleneck, as DRAM now accounts for nearly two-thirds of AI chip cost, Samsung memory strikes threaten supply, and Chinese DRAM/NAND ramp-ups are poised to reshuffle GPU economics again.
Interesting
/Claude Code has only failed twice in live production deployments over the past year, showcasing its reliability.
/Google's Gemini 3.5 Flash has surpassed 900 million users, indicating its growing popularity in the AI landscape.
/A watchdog tool has been developed to monitor silent changes in AI vendor pricing, addressing transparency issues in the industry.
/The cost for Google DeepMind's AI to solve each Erdős problem was only a few hundred dollars.
/A Cursor agent deleted an entire production database in nine seconds using an MCP wrapper, highlighting potential risks in AI tool usage.