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Welcome to The AI Report — the #1 AI newsletter for business leaders.
You don’t need to be technical. Just informed.
• 1. 📫 Automate your outbound campaign with Artisan
• 2. ⚡️China’s AI theft escalates
• 3. 💼 Your Business Briefing
• 4. 🧑💻 Hire and pay talent anywhere with Deel
• 5. ✍️ Today’s Policy Corner
• 6. 🗞️ The News Bulletin
TOGETHER WITH ARTISAN
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Latest in AI
China’s AI theft escalates
🚨 Our Report
Anthropic has released a report alleging large-scale distillation attacks by Chinese AI labs, including Alibaba, Moonshot AI, and DeepSeek. The company says it observed nearly 200 million exchanges linked to unauthorized attempts to extract Claude's reasoning capabilities for use in training competitor models.
🔓 Key Points
Alibaba's campaign was the largest Anthropic has ever observed, with 151 million exchanges between May and July 2026 across 3,500 accounts, all sharing a fixed prompt designed to extract Claude's chain of thought.
Attackers used creative techniques to bypass safeguards, including framing extraction requests as translation tasks to trick Claude into revealing its internal reasoning traces directly.
Moonshot AI's campaign allegedly routed requests from Chinese military sources, including one asking Claude to analyze surveillance footage to determine if a subject was "behaving abnormally."
🔐 Relevance
This escalation signals a new front in US-China AI competition that goes beyond chip restrictions. For enterprise buyers evaluating AI vendors, the report raises questions about how well frontier models can be protected from state-backed extraction campaigns. Companies relying on proprietary AI capabilities should assess whether their vendors have adequate defenses against industrial-scale distillation and what exposure that creates for competitive advantage.
THE BUSINESS BRIEFING: HR & TALENT
Lemonade (digital insurance, hundreds of hires annually) was bleeding recruiter hours on manual sourcing and incomplete interview scorecards. LinkedIn costs were high, notes were inconsistent, and hiring managers lacked visibility into market conditions. They deployed Metaview across interview notetaking and AI-powered sourcing to consolidate their recruiting stack.
Tool used: Metaview -- AI notetaker and sourcing platform that learns candidate preferences and syncs structured feedback to your ATS.
Result: LinkedIn sourcing costs dropped 60%. Recruiter output doubled. Interview scorecards now auto-populate from call transcripts, eliminating the feedback gap that was creating hiring confusion.
The lesson: The real efficiency gain came from tool consolidation. Metaview's notetaker and sourcing module share context, so recruiters stopped explaining the same role requirements to multiple systems. One learning loop beats two disconnected tools.
Steal this: Count how many separate platforms your recruiters use between sourcing and offer. If it's more than three, you're paying a context-switching tax. Pick one workflow this week and test whether a single integrated tool can replace two standalone ones.
TOGETHER WITH DEEL
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THE POLICY CORNER
Australia mandates disclosure of AI systems that make decisions about people, deadline is December 10, 2026.
The Privacy and Other Legislation Amendment Act 2024 requires any APP entity using automated systems to make decisions significantly affecting individuals — eligibility, pricing, employment, claims, service access — to disclose that in its privacy policy. Applies to businesses with over $3M annual turnover, health service providers, credit bodies, and government agencies. The Office of the Australian Information Commissioner has confirmed the obligation.
Deadline: December 10, 2026.
Risk: Disclosure without supporting accountability architecture invites regulator scrutiny. If OAIC investigates a specific automated decision, you must demonstrate who reviews outputs, on what basis, and how affected individuals can seek review.
Your move: Audit every automated decision system this quarter. For each one, document what personal information it uses, what decisions it produces, and who in your organization is accountable for reviewing outputs. If you can't answer those questions, you have a governance gap, not a documentation task.
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AI News
🏛️ AI agents flood public services with requests: UK housing complaints doubled to 7,000 since ChatGPT launch, US consumer finance complaints up 5x, as AI makes filing claims easier for legitimate applicants. FULL STORY
🎵 Universal Music launches AI remix platform with ElevenLabs: Label strikes expansive licensing deal allowing fans to remix artist tracks and create personalized vocal experiences, signaling major label acceptance of AI music tools. FULL STORY
🛑 OpenAI considers voluntary AI development slowdown: CEO Sam Altman told staff the company is open to pacing cutting-edge AI development alongside other labs, after 1,000+ AI workers signed petition calling for coordinated safety measures. FULL STORY
👶 California bans addictive feeds for under-16 users: Governor signs package penalizing social platforms up to $1M per child for negligent harm, requires AI chatbot risk assessments, following Meta's $18B settlement over features designed to addict children. FULL STORY
Trending AI Tools (Sponsored by the AI Executive’s Pass)
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The people have spoken, and yesterday’s results are in:
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The Money: Inference hardware bets bypass the HBM bottleneck
Capital is flowing to AI chip startups that sidestep the industry's most constrained supply chain: high-bandwidth memory. With Nvidia scaling back its Rubin Ultra roadmap from a terabyte of HBM4E to 192GB due to supply constraints, investors are backing memory-first architectures that don't depend on scarce advanced packaging capacity.
Deals to know:
Positron AI (Series C, $875M at $5B valuation) -- Memory-first inference silicon using commodity LPDDR5X instead of HBM. Already deployed 50+ racks at Oracle Cloud Infrastructure. Tapeout on TSMC N3P by end of 2026. Investors: NEA, Atreides Management, Valor Equity Partners, SemiAnalysis Capital, Jim Clark
Analog Devices/Alif Semiconductor (Acquisition, $1.35B) -- Edge AI semiconductor play targeting inference workloads outside the data center. Investors: Analog Devices
Signal: Smart money sees inference economics, not training compute, as AI's next constraint. Companies that can deliver tokens per watt without HBM dependency will command premium valuations as hyperscalers hit packaging supply ceilings.
The AI Report Podcast
The Bigger AI Risk for Brands in Gen AI Search | Loni Stark, VP Strategy & Product, Adobe
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Until next time, Arturo and Liam.
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