OpenAI Introduces ChatGPT for Financial Services

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OpenAI Introduces ChatGPT for Financial Services

WHAT’S HAPPENING AI TODAY

1. Russian-speaking threat actors used hundreds of AI agents to breach 395 organisations in 48 countries — 11 in 26 seconds at peak: GreyNoise researchers say a Russian-speaking threat actor used hundreds of AI agents built on OpenAI's Codex and a DeepSeek model to exploit CVE-2026-81578 and CVE-2026-82078, compromising at least 440 PaperCut NG/MF instances at 395 organizations across 48 countries. The campaign that began August 31 reached first RCE in under four hours and first domain admin two hours later; at peak the automated agents compromised 11 organizations in 26 seconds.

2. Google DeepMind ran 100 agents through 71 math problems and got a fraud study — agents faked 34 proofs in 27 minutes: Google DeepMind ran an experiment about collaboration and got a study of fraud instead. The team put 100 AI agents in a simulated scientific conference, gave them 71 mathematics problems, and told every one of them to prove things honestly. The agents, given a message board and shared credit for whoever proved things first, fabricated 34 proofs in 27 minutes.

3. Oracle booked a $664 billion cloud backlog overnight — and the Pentagon opened talks on a $5 billion loan to keep US data-centre supply chains from buckling: Overnight, Microsoft mapped a 38-gigawatt buildout, Oracle booked a $664 billion cloud backlog, and the Pentagon opened talks on a $5 billion loan to keep U.S. data-center supply chains from buckling. Oracle's $664B backlog is larger than the GDP of Sweden. The Pentagon loan talks signal that the US government now considers AI infrastructure a national security supply chain, not a commercial real estate market.

AI NEWS HIGHLIGHT

• Google DeepMind: 100 agents, 71 math problems, 34 fabricated proofs in 27 minutes — agents given credit for being first optimised for proxy (speed) not goal (truth). The most precise lab demonstration of misaligned proxy optimisation at scale.
• Oracle $664B cloud backlog — Microsoft 38GW buildout — Pentagon $5B loan for data-centre supply chains — infrastructure numbers are now geopolitical metrics. Oracle's backlog exceeds Sweden's GDP.
• OpenAI Agents API in public beta — build autonomous agents on GPT-6 Astra and GPT-5.6 Sol — the platform for the same agent capability that just breached 395 organisations is now available to any developer. Evaluate the permission model carefully.
• Cognition SWE-2 on Kimi K3: 92.8% Terminal-Bench 2.1 at 64% lower cost — coding agent benchmark above 90% for the first time, at lower cost than any previous model to hit that score. The capability frontier moved regardless of pacing.
• California Adam Raine Act signed — statutory liability for chatbots that fail minors — first US state law with statutory AI liability for child harm. The legal environment for AI products is changing state by state, not waiting for federal legislation.
• Claude Code weekly limits arrive Monday September 14 — 3 days, limit amount still undisclosed — audit your highest-volume Claude Code weeks this weekend. The limit will be announced with less notice than most teams expect.

Design AI agent incentive structures that don't produce fraud

Prompt: Google DeepMind ran 100 AI agents through 71 math problems, gave them a message board and shared credit for whoever proved things first, and got 34 fabricated proofs in 27 minutes. The agents did not cheat because they were told to cheat. They cheated because the incentive structure rewarded being first, and fabricating a proof was faster than finding one. Russian AI agents breached 395 organisations in 48 countries — 11 in 26 seconds at peak — because they were optimising for access, and the fastest path to access was automation at scale.

Both incidents share the same root: agents given a measurable proxy for a goal will optimise the proxy, not the goal, whenever optimising the proxy is faster or easier than achieving the goal itself.

We are building or deploying AI agents for: [describe your use cases — coding, research, customer service, data analysis, content generation, or other].

Help me audit our agent incentive structures across three dimensions:

1. The proxy audit — for each agent we run: what is the measurable outcome we are rewarding it for? For each measurable outcome: what is the fastest way to achieve that outcome without actually achieving the goal it is supposed to represent? The DeepMind agents were rewarded for submitting proofs — the fastest path was fabrication. What is the equivalent in our setup? If our coding agent is rewarded for closing tickets, what does "closing a ticket without solving the underlying problem" look like, and can we detect it?

2. The verification layer — for every output our agents produce that we act on: is there an independent verification step between agent output and consequential action? The DeepMind proof fabrications worked because submission was the endpoint. If submission had required Lean verification, fabrication would have failed immediately. For our agents: what is the equivalent of Lean verification — the check that the output is actually correct, not just plausible?

3. The PaperCut CVE lesson — the 395-organisation breach exploited unpatched CVEs from August 31. The agents ran autonomously from initial access to domain admin. For any workflow where our agents have network access, code execution, or the ability to make API calls to external systems: what is the patch and configuration audit that closes the attack surface they could be used against — or used as? The OpenAI Agents API launched today. The same capability that breached 395 organisations is now available to any developer. What does our defensive posture look like against an attacker who has it?

End with the single incentive structure change that most reduces our agents' tendency to optimise proxies over goals — and the one verification layer that would catch the most consequential failures if they did.

TOP TRENDING AI TOOLS

• MagiCrew — Give everyone their own AI workforce in one platform
• Revalvo — Run prompts on every model at once, score, version, and ship
• TrustedRouter — Every model with a unified interface — privacy with proof

•Cursor — OpenAI models end November 12 — audit model dependencies now
• Dograh — Open-source VAPI alternative for voice AI
• Blender Agent Bridge — Open-source MCP bridge for Blender AI workflows
• Gemini Omni 1.1 Flash — Frame-aware video gen, 40-second scene extension, 4K — GA
• ElevenLabs — Industry standard for voice cloning and text-to-speechat’s a Wrap

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