Dear friends,

Last week, China barred its major tech companies from buying Nvidia chips. This move received only modest attention in the media, but has implications far beyond what’s widely appreciated. Specifically, it signals that China has progressed sufficiently in semiconductors to break away from dependence on advanced chips designed in the U.S., the vast majority of which are manufactured in Taiwan. It also highlights the U.S. vulnerability to possible disruptions in Taiwan at a moment when China is becoming less vulnerable.

After the U.S. started restricting AI chip sales to China, China dramatically ramped up its semiconductor research and investment to move toward self-sufficiency. These efforts are starting to bear fruit, and China’s willingness to cut off Nvidia is a strong sign of its faith in its domestic capabilities. For example, the new DeepSeek-R1-Safe model was trained on 1000 Huawei Ascend chips. While individual Ascend chips are significantly less powerful than individual Nvidia or AMD chips, Huawei’s system-level design approach to orchestrating how a much larger number of chips work together seems to be paying off. For example, Huawei’s CloudMatrix 384 system of 384 chips aims to compete with Nvidia’s GB200, which uses 72 higher-capability chips.

Today, U.S. access to advanced semiconductors is heavily dependent on Taiwan’s TSMC, which manufactures the vast majority of the most advanced chips. Unfortunately, U.S. efforts to ramp up domestic semiconductor manufacturing have been slow. I am encouraged that one fab at the TSMC Arizona facility is now operating, but issues of workforce training, culture, licensing and permitting, and the supply chain are still being addressed, and there is still a long road ahead for the U.S. facility to be a viable substitute for manufacturing in Taiwan.

If China gains independence from Taiwan manufacturing significantly faster than the U.S., this would leave the U.S. much more vulnerable to possible disruptions in Taiwan, whether through natural disasters or man-made events. If manufacturing in Taiwan is disrupted for any reason and Chinese companies end up accounting for a large fraction of global semiconductor manufacturing capabilities, that would also help China gain tremendous geopolitical influence.

Despite occasional moments of heightened tensions and large-scale military exercises, Taiwan has been mostly peaceful since the 1960s. This peace has helped the people of Taiwan to prosper and allowed AI to make tremendous advances, built on top of chips made by TSMC. I hope we will find a path to maintaining peace for many decades more.

But hope is not a plan. In addition to working to ensure peace, practical work lies ahead to multi-source, build more chip fabs in more nations, and enhance the resilience of the semiconductor supply chain. Dependence on any single manufacturer invites shortages, price spikes, and stalled innovation the moment something goes sideways.

Keep building,

Andrew 

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News

Agents of Commerce

Google launched an open protocol for agentic payments that enables agents based on any large language model to purchase items over the internet.

What’s new: Agent Payments Protocol (AP2) is designed for buyers and sellers to securely initiate, authorize, and close purchases. AP2 works with Google’s A2A and Anthropic’s similar MCP, open protocols that instruct agents or provide access to data and APIs. It manages diverse payment types including credit cards, bank transfers, digital payments, and cryptocurrency.

How it works: Agentic payments pose challenges to security, such as manipulation by malicious actors, and liability, particularly with respect to whether a user or agent is to blame for mistakes. AP2 aims to solve these problems by using cryptographically signed contracts called mandates. Three distinct mandates record the terms of the purchase, its fulfillment, and the user’s authorization of payment. If a fraudulent or incorrect transaction occurs, the payment processor can consult this record to see which party is accountable. To buy an item using AP2:

• An intent mandate specifies rules for the purchase such as price limits, timing, and the item’s attributes. It may create an intent mandate while a user is present or ahead of time. For instance, if a buyer instructs an agent to “buy [brand and model] running shoes the moment they go on sale,” the agent will prompt the user to specify and authorize the terms of the mandate, such as the desired top price, size, and color.
• A cart mandate covers the other end of the sale. This contract describes the contents of the virtual shopping cart including a description of items sold, their prices, and terms of the deal.
• A payment mandate tells a payment network (a financial institution plus payment processor that moves funds electronically) that the transaction was authorized by a user or an agent, so it can complete the transaction.

Behind the news: Many companies have experimented with agentic payments with varying degrees of success. For example, last year Stripe launched an agentic payment toolkit that issues a one-time debit card for each purchase. This approach reduces risk, but it requires Stripe’s payment system, particular models, and specific agentic frameworks. Google’s approach is more comprehensive, initially including more than 60 partners including payment processors, financial institutions, and software giants.

Why it matters: AP2 opens up automated sales in which any participant can buy and sell, and it does this in a standardized, flexible way. For instance, a user could tell an agent to book a vacation in a specific location with a specific budget. The agent could transmit those requirements to many sellers’ agents that might assemble customized packages to meet the user’s demands. Then the user’s agent could either present the packages to the user or choose one itself. The buyer would get the vacation they want and the seller would make a valuable sale, while AI did the haggling.

We’re thinking: The internet didn’t make travel agents obsolete, it made them agentic!

What ChatGPT Users Want

What do ChatGPT’s 700 million weekly active users do with it? OpenAI teamed up with a Harvard economist to find out.

What’s new: ChatGPT users are turning to the chatbot increasingly for personal matters rather than work, and the gender balance of the user base is shifting, OpenAI found in a large-scale study. “How People Use ChatGPT,” a preliminary report published by the National Bureau of Economic Research, is available in return for an institutional email address.

How it works: The study examined 1.58 million messages entered by users and drawn at random from over 1.1 million conversations between May 2024 and July 2025.

• The messages were written by logged-in users over 18 who used consumer-level (as opposed to business) subscriptions.
• The authors classified users by gender (based on names the authors deemed typically masculine, feminine, or indeterminate), self-reported age, and geography.
• They classified messages by topic, general intention (such as asking for information or requesting action), and specific task (such as writing or coding).

Results: Most users of ChatGPT were young adults, and apparently more women are joining their ranks. Uses shifted from work to more personal tasks over the course of the study period. Writing and guidance were most popular uses, followed closely by seeking information.

• ChatGPT was most popular with users between 18 and 25 years old, who sent 46 percent of the messages. Users between 26 and 66 were more likely to use ChatGPT for work.
• Women may now outnumber men using ChatGPT. Messages from users with names classified as typically feminine increased from 37 percent in January 2024 to 52 percent by June 2025.
• Messages categorized as asking were more common than messages categorized as doing (requests for generated output such as plans, writing, or code) or expressing (such as idle conversation, reflection, or playing a role). The most common requests were for practical guidance (28.3 percent) or writing (28.1 percent), while seeking information was nearly as popular (21.3 percent).
• Uses of ChatGPT for personal matters rose. In June 2024, messages divided roughly equally between work and non-work uses. By July 2025, roughly 73 percent of them likely were not related to work. (Overall use grew during that time. The number of likely non-work messages increased by around 8 times, while the number of work-related messages increased by more than 3 times.)
• Among non-work uses, the most common were seeking information (24.4 percent) or practical guidance (28.8 percent). When ChatGPT was used for work, the most common use was writing, mostly requests to edit, critique, translate, or otherwise transform existing text rather than produce all-new text.

Behind the news: OpenAI said its report is the largest study of chatbot usage undertaken to date, but its peers have published similar research. Anthropic released its third Economic Index, which analyzes consumer and business use of its Claude models. Anthropic’s study shows that Claude API users are much more likely to automate tasks than consumer users. Claude is used overwhelmingly for computational and mathematical tasks, but education, arts and media, and office and administrative support are steadily rising.

Why it matters: In OpenAI’s study (and Anthropic’s), AI users and uses are becoming more diverse. The initial user of AI chatbots was disproportionately likely to be based in the U.S., highly educated, highly paid, male, young, and focused on technology. Nearly 3 years after ChatGPT’s introduction, they are far more varied, as are their wants, needs, and expectations.

We’re thinking: Early on, it seemed as though large language models would be most useful for work. But people are using them to seek information and advice about personal matters, plan their lives, and express themselves. It turns out that we need more intelligence in our whole lives, not just at the office.

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Sports Betting Goes Agentic

AI agents are getting in on the action of online sports gambling.

What’s new: Several startups cater to betting customers by offering AI-powered sports analysis, chat, and tips, Wired reported. Some established gambling operations are adding AI capabilities to match.

How it works: Most AI sports-betting startups analyze which bets are the most statistically likely to pay off based on publicly available data. Increasingly, agents suggest specific bets. Only a few take bets from users and pay winnings to them, and fewer offer agents that actively place bets on third-party web sites on a user’s behalf.

• Monster.bet hosts MonsterGPT, a GPT-style chatbot that uses retrieval-augmented generation (RAG) to gather sports data from across the web while a proprietary algorithm predicts winners. The chatbot allows bettors to ask questions, and a history function tracks the results of bets they place and tailors its analysis to their strategies. Access to Monster costs $77 a month.
• Rithmm, based in Massachusetts, allows users to create their own “prediction models” using no-code tools. It also focuses on “prop bets” (not whether a team will win a game, but whether a player will achieve a particular outcome like score a touchdown). Subscriptions start at $30 a month.
• With roots in fantasy sports, FanDuel is an older sports-betting operation that has integrated AI. Unlike many competitors, it takes bets and pays winnings. The mobile app integrates a chatbot called AceAI that helps users construct bets that require more than one event to occur; for example, that football champions Argentina will win a particular match and their star Lionel Messi will score at least one goal.
• Sire (formerly DraiftKing [sic]) uses an agentic approach. AI agents currently have limited access to bank accounts and other payment services like PayPal or Venmo, so Sire’s agents place bets using a crypto wallet. This enables an agent to react to events within a match and place bets automatically faster than a human can. For example, if a tennis player serves an ace, an automated bet can be made that the next serve will also be an ace. But instead of placing separate bets by individual bettors, Sire sells shares to customers who divide any profits from a wide range of bets.
• Few other betting agents have succeeded. The blockchain platform Zilliqa developed an agent called Ava for picking horse-race winners but abandoned it because synchronizing the agent, crypto wallets, and betting sites — all of which operate independently — was too slow. Some other purportedly agentic tools, including one called WagerGPT, collapsed under inflated promises.

Behind the news: Most AI gambling startups are based in the United States, where online betting recently became legal. In 2024, Americans bet over $150 billion on legal sports wagers, up 22 percent from 2023. The share of online betting has grown steadily from 25 percent of the total in 2024 to 30 percent in 2025 and shows no sign of slowing down.

Why it matters: Online gambling is an AI laboratory that uses nearly every emerging element of the technology. It requires quantitative reasoning to analyze bets, RAG to scour sports statistics and other relevant information, classification models to identify potentially profitable bets, and payment agents to place bets automatically. As these technologies advance, betting analysis and tools will advance with them.

We’re thinking: Whether you gamble with cash or just wager your time and energy, learning more about AI is a smart bet.

Faster Reinforcement Learning

Fine-tuning large language models via reinforcement learning is computationally expensive, but researchers found a way to streamline the process.

What’s new: Qinsi Wang and colleagues at UC Berkeley and Duke University developed GAIN-RL, a method that accelerates reinforcement learning fine-tuning by selecting training examples automatically based on the model’s own internal signals, specifically the angles between vector representations of tokens. The code is available on GitHub.

Key insight: The cosine similarity between a model’s vector representations of input tokens governs the magnitude of gradient updates during training. Specifically, the sum of those similarities that enter a model’s classification layer, called the angle concentration, governs the magnitude of gradient updates. Examples with higher angle concentration produce larger gradient updates. The magnitude of a gradient update in turn determines the effectiveness of a given training example: The larger the update, the more the model learns. Prioritizing the most-effective examples before transitioning to less-effective ones enhances training efficiency while adding little preprocessing overhead.

How it works: The authors separately fine-tuned Qwen 2.5 1.5B, Qwen 2.5 7B, and Llama 3.2 3B using the GRPO reinforcement learning algorithm with examples ordered according to their angle concentration. The datasets included math problems in GSM8K and AMC 23, and coding problems in LiveCodeBench and HumanEval+.

• Given a training set, the authors calculated the angle concentration of each example by performing a single forward pass on the entire dataset. They sorted examples from highest to lowest angle concentration.
• They fine-tuned the models, focusing first on examples with the highest angle concentrations and shifting toward lower angle concentrations as training progressed. They tracked the models’ learning according to accuracy and the angle concentration on each batch of data. They shifted the focus more toward less-effective examples as the model learned and shifted less when it struggled.
• They continued training for 200 epochs.

Results: The authors compared models that were fine-tuned using GAIN-RL with counterparts that used GRPO performed on randomly ordered examples. GAIN-RL generally accelerated learning by a factor of 2.5.

• Whether the task involved math or coding, GAIN-RL took 70 to 80 training epochs to match the performance of fine-tuning using typical GRPO for 200 epochs.
• For instance, on GSM8K, Qwen 2.5 Math Instruct 7B after GAIN-RL fine-tuning achieved 92.0 percent accuracy after 70 epochs. The version fine-tuned on typical GRPO needed 200 epochs to reach the same performance.

Why it matters: Many strategies for ordering training examples rely on external, often expensive heuristics based on their difficulty, for example judgments by human annotators or a proprietary LLM. By using a simple signal generated by the model itself, this method provides a direct and efficient way to identify the most effective examples, making reinforcement learning much faster.

We’re thinking: Ordering training examples is much older than applying reinforcement learning to fine-tuning large language models. Applying earlier methods to more recent approaches holds many advances in machine learning!

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