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Today’s issue covers AI labs writing their own rules for increasingly capable models, Anthropic locking in billions of dollars of compute, and China building a market around the human labor that trains AI.
These are today's updates.
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AI labs are starting to write rules for their own models
Anthropic commits $13.7B to secure AI compute
China is building a market for the human labor behind AI
Tools, jobs, resources, and last issue’s winning prompt ⬇️
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Top News
Microsoft has published a draft code of conduct for its future AI models, built around keeping humans in control. The proposed rules require models to accept correction and shutdown, communicate uncertainty, avoid deception, and stay within defined safety constraints.
The move comes as Anthropic, OpenAI and Google have been discussing an industry-led AI safety standards body focused on testing, evaluation and common safety benchmarks.
Elon Musk has also proposed that AI companies should test each other’s models for safety, while Anthropic CEO Dario Amodei is calling for independent evaluators to get deeper access to frontier models. Sam Altman is pushing for mandatory U.S. requirements covering independent assessments, cybersecurity and incident reporting for advanced AI systems.
It seems model governance will soon become part of vendor evaluation, alongside capability, price and performance.
Anthropic has signed a six-year, $13.7 billion computing deal with RUM Group, which is building an AI data center in Georgia. The facility currently has access to 120 megawatts of power, with the potential to reach 180 MW. Anthropic will also receive an option to buy up to 51M RUM shares for one cent each, tied to its compute purchases.
RUM said in an August filing that it did not have the financing needed to build the facility or purchase the GPUs required to fulfill the contract, meaning debt and equity financing will have to fund a substantial part of the project.
Anthropic has already secured at least 14.8 GW of compute capacity through multiple agreements, potentially representing hundreds of billions in spending over the next decade.
With this, compute availability and cost are becoming strategic planning variables, especially for workloads that rely on large-scale agents or repeated model inference.
And compute is only one part of the stack. The race is also creating demand high-quality human data.
A Chinese AI data company has reached a roughly $1 billion valuation despite having only about $30 million in orders. The company is effectively building a Chinese counterpart to Surge AI, providing the human-generated training and evaluation data needed to improve models.
Chinese AI companies are increasingly paying specialists such as lawyers, engineers and finance professionals to generate and evaluate difficult training examples. At the same time, Beijing is treating AI safety and loss-of-control risks as issues that require their own governance frameworks.
This is another example where investors are increasingly pricing the infrastructure around AI models, including data, evaluation and specialized human expertise.
Signals
Tools
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Hyperfocus: Free planner that turns goals into daily progress
Resources
Poll
Choose the prompt you want us to publish next
Market
Funding
Cornelis raises $205M to help AI chips communicate more effectively
Simile raises $200M to simulate human behavior for enterprise decisions
Roles in AI
Data Annotation Specialist, Engineering at Cohere
Opinion Piece
An Alien Mind: What happens when AI starts becoming smarter than our ability to understand it? OpenAI Chief Scientist Jakub Pachocki explores that question, and why the race toward self-improving AI may be moving faster than our safety systems.
This means:
The new model AI race could potentially change to how much can an AI contribute to building the next model.
“Human in the loop” may not mean “human in control.”
AI progress may be evaluated in terms of confidence of how increasingly capable systems are behaving as intended.
Governments may have to work on regulating capability thresholds, evaluations and development processes, instead of just applications.
Can humanity coordinate around the pace of AI development when everyone has an incentive to move faster?
Prompt of the Day
AI Strategic Narrative Builder
When to use this?
Use this before an important leadership meeting. Write everything you know and have the AI find the argument, gaps, trade-offs, and decision you actually need to communicate.
You are a strategic communications advisor for enterprise leaders.
I’ll give you a messy set of ideas, facts, data, decisions, or business context. Turn it into a clear strategic narrative that helps an executive audience understand what is happening, why it matters, what changes, and what decision or action is needed.
First, identify the central message and the business tension behind the situation. Then structure the narrative around:
Context: What has changed or what problem are we facing?
Why now: Why does this need attention now?
Business impact: What does it mean for revenue, costs, customers, risk, productivity, or competitive position?
Options: What are the realistic paths forward and their trade-offs?
Recommendation: What should we do and why?
Next step: What decision, action, or alignment is needed?
Remove unnecessary jargon, repetition, and generic corporate language. Keep the reasoning sharp and grounded in the information provided. Flag assumptions, missing evidence, or weak logic instead of filling gaps with invented facts.
Write for a busy senior executive who needs to understand the situation quickly and make a decision. Give me:
A one-sentence strategic takeaway
The recommended narrative
The strongest supporting evidence
Key risks or objections an executive may raise
A concise recommended next step
If the input is too vague to build a credible narrative, ask me the minimum number of questions needed.Get more such prompts in the Prompting Playbook (free for you)
Stay curious, {{first_name | reader}}
PS. If you missed yesterday’s issue, you can find it here.
