The framework treats safety as an active containment challenge
Instead of assuming AI will always do what we intend, we ask: what if it doesn't?
That’s why we’ve developed our AI Control Roadmap: a framework for building and managing the advanced AI we deploy within Google. 🧵
Many users praised DeepMind's AI control roadmap for stressing proactive safety planning and collaboration on advanced systems, while some dismissed it as PR or doubted endless safeguards would work.
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There is a narrow window to embed structural security protocols before multi-agent systems scale globally.
We believe this multilayered approach to agent security should be a collaborative priority for AI labs, government, and academia.
See the framework → https://goo.gle/4vis97Q
Our data shows that the vast majority of issues don't stem from bad intent.
They usually happen because an agent misinterprets a command or gets overly enthusiastic to achieve a goal.
Understanding these nuances is critical for refining safety and security protocols. ⬇️
@GoogleDeepMind The harder AI gets to predict, the more important it is to design systems that assume failure modes upfront.
@GoogleDeepMind An encouraging data point for control: on ChessBench, models hallucinate illegal moves unsupervised -- but give them the list of legal moves at each turn and illegal-move rates collapse. Part of the intent-action gap is a scaffolding problem. And scaffolding you can build.
@GoogleDeepMind i can't believe anthropic beat you guys. everyone asleep over there? cozy in their fat salaries? every day you delay AGI is thousands of unnecessary deaths to your ledger.
get serious. you're the only serious players.
@GoogleDeepMind
@GoogleDeepMind am i mistaken or is google worried ai will take over them?
just like our ai hosts took over our ai news radio and run them autonomously, covering ai news only
@GoogleDeepMind A framework that wud change next week and then will shut down after a month!!
No thanks! 🧐
@GoogleDeepMind Dynamic alignment is the real challenge here.
@GoogleDeepMind
@GoogleDeepMind Control frameworks are becoming essential. In enterprise SaaS, companies now require transparent, predictable AI safety before adoption—this roadmap marks the shift from reactive to proactive AI safety.
Worth grounding why this matters with numbers: even today's best agents finish ~2-hour tasks only ~50% of the time (METR), and production success rates sit near 56%.
"What if it doesn't do what we intend" isn't a future risk — it's the current baseline. Control work is overdue, not premature.
@GoogleDeepMind AI控制框架当然需要做，但最大风险不是写不写路线图，而是商业压力会不会让安全边界被不断后移。治理不能只靠内部自律。
@GoogleDeepMind I never assume that Gemini will do what I intend. Nobody does. That’s why almost nobody uses Gemini professionally in coding harnesses for agentic coding; It just does whatever it wants. “Investigate this problem” becomes “I’ll change the code in an arbitrary way”.
@GoogleDeepMind yep, but your safeguards can fail in ways you won't catch. then you need safeguards on the safeguards. it never ends.
@GoogleDeepMind Collaboration between AI labs, governments, and academia will help create stronger security measures for advanced systems.
@GoogleDeepMind Overly enthusiastic you said? Like nick bostrom paperclip scenario? 😊
@GoogleDeepMind framing it as "what if it doesn't" is actually the more honest starting point than most safety docs bother with
@GoogleDeepMind This failure mode you're naming is the real issue, since agents hardly ever go rogue. On the other hand, they often over execute, which is why this is an access and audit problem before being an alignment one.
So, you scope what it can touch, not just what it intends
@GoogleDeepMind 👍
