Daily TEA – The Top AI County In America Isn’t In Silicon Valley
Microsoft’s AI map, a16z’s $34B tokenization reality check, the CFO in your pocket, where AI’s real money lives, and the 1% of devs who eat all the tokens
Hello, dear TEA-mates! Here is what you need to know today.
1. 🗺️ The Top AI County In America Is A College Town
Microsoft’s AI Economy Institute mapped US AI adoption for Q1 2026, and the geography defies expectations. The top AI-using county is not in Silicon Valley. It is Williamsburg, Virginia (home to William & Mary) at 73.7%, followed by Harrisonburg, Virginia (James Madison) at 67.9%, Madison, Idaho (BYU-Idaho) at 67.7%, Brazos County, Texas (Texas A&M) at 64.5%, and Story County, Iowa (Iowa State) at 64.2%. College towns are driving the trend: counties with more 18-to-24-year-olds average 28.6% usage versus 20.3% elsewhere. At the state level, DC leads at 40.6%, then Maryland (36.5%) and Utah (35.9%), with Texas (35.4%) edging out California (34.1%) and New York (32.9%). Nationally, 31.3% of working-age Americans now use AI, lifting the US from 24th to 21st globally. The urban-rural gap stays wide: 32.9% in metro counties versus 16.2% in rural areas. (Read More)
🫖 TEA For Thought: “It’s not as what you’d image”
2. 🪙 Tokenized Assets Proved The Concept. Now Comes The Hard Part.
a16z crypto published 7 charts showing the tokenized real-world asset (RWA) market crossed $30 billion and now sits near $34 billion, excluding stablecoins, a 10x jump from under $3 billion in mid-2024. The GENIUS Act’s clearer US stablecoin rules plus maturing institutional infrastructure drove the surge. Tokenized US Treasuries and commodities make up roughly two-thirds of the market: bonds lead at $15.2 billion (BlackRock and Franklin Templeton dominate), commodities sit near $5.1 billion (almost all gold via Tether’s XAUT and Paxos’s PAXG), and tokenized stocks total about $1.5 billion. Ethereum hosts $15.7 billion, ahead of BNB Chain ($4B) and Solana ($2.2B). The catch: Pantera Capital’s token presence index ranks more than three-quarters of assets in the lowest tier, meaning most are “digital receipts” rather than truly composable onchain assets. Only about 5% of bonds are deployed in DeFi. The hard part is making tokenized assets genuinely programmable, not just digitized. (Read More)
🫖 TEA For Thought: “This is just the beginning.”
3. 💼 The Agentic CFO In Your Pocket
In a CoinDesk opinion piece, Joseph Chalom argues that AI agents powered by stablecoins, tokenized assets, and DeFi will hand retail savers the kind of treasury management once reserved for institutions. The scale of the gap is stark: American households hold $6 trillion in checking and nearly $15 trillion in savings, yet US retail savers forgo at least $180 billion in interest every year. An autonomous agent could monitor real-time cash flows, run securities lending, and vote shares automatically. The infrastructure is arriving fast: the stablecoin market is projected to grow from $330 billion to $3 trillion by 2030, the tokenized-asset industry could reach $100 trillion by decade’s end, and the open X402 payments protocol has already logged 167 million agent-to-agent transactions this year. Standards from Visa, Mastercard, Google, and Stripe (which processed $1.9 trillion in volume last year) are converging on agent payments. (Read More)
🫖 TEA For Thought: “AI is the biggest equalizer of all times.”
4. 🧩 Where AI’s Real Product-Market Fit Lives
Simon Willison argues OpenAI and Anthropic found genuine product-market fit not in consumer chatbots but in enterprise coding agents like Claude Code and Codex. The economics tell the story: he personally burns around $2,180 a month in API tokens ($1,199.79 to Anthropic, $980.37 to OpenAI) while paying just $200 in subscriptions, so both firms moved enterprise plans to pay-per-token pricing in April 2026. Anthropic’s revenue reached $10.9 billion in the second quarter, and SpaceX agreed to pay it $1.25 billion per month through 2029 for compute. By contrast, ChatGPT has 900M-plus weekly users but only 5.6% pay. Willison also calls the viral “company blew its AI budget” stories overblown, including claims about Uber’s CTO maxing out a full-year budget. His thesis: coding agents win because they consume far more tokens while serving highly paid knowledge workers. (Read More)
🫖 TEA For Thought: “Enterprise is where the money is. In the agentic era, B2B is where money lies, not so much B2C, as the amount of money an individual user is willing to pay is too little compared to the cost it generates.”
5. 📊 The 1% Of Developers Who Eat All The Tokens
Cursor’s Spring 2026 Developer Habits report reveals that AI usage among developers is extraordinarily concentrated. P99 developers produce 46x more AI-generated lines than the median user and merge 15x more pull requests. The Gini coefficients are striking: 0.77 for AI lines, 0.75 for spend, 0.72 for tokens, with the top 5% accounting for the vast majority of all AI activity. Output is accelerating across the board: lines added per developer per week climbed from 3.6K in January 2025 to 8.6K in May 2026, “mega PRs” of 1,000-plus lines grew from 8% to 13.8% of merges, and AI-generated code survival rose from 76.6% to 80.6%. Autonomy is surging too: AI changes accepted without manual review jumped from 7% in January to 36.3% in May 2026. Model costs vary 9x per request, from $0.18 (Composer 2.5) to $1.57 (Opus 4.7). (Read More)
🫖 TEA For Thought: “AI usage is highly concentrated, with a small share of developers accounting for a large share of AI lines, spend, and token consumption.”
🛠️ Skill of the Day
The Cost-Per-Outcome Auditor: turn any “should we buy/build this AI tool?” question into a clear one-page decision memo with real numbers.
You are a pragmatic operations analyst. I am deciding whether to adopt
[TOOL OR APPROACH] for [TASK / TEAM]. Help me decide based on cost per
outcome, not hype.
Here is my situation:
- What we do today: [CURRENT PROCESS]
- What the tool/approach promises: [PASTE PITCH OR DESCRIPTION]
- Rough usage: [HOW OFTEN / HOW MANY PEOPLE / VOLUME]
- Budget reality: [WHAT WE PAY NOW / WHAT WE CAN SPEND]
Produce a one-page memo with these sections:
1. Unit of value: define the single outcome that matters here.
2. Cost per outcome, three scenarios: low, expected, and runaway usage.
Show the math and name every assumption you make.
3. The cheaper alternative I might be ignoring (including "do nothing").
4. The breakeven point: at what volume does this win or lose?
5. Verdict in one sentence, plus the top risk that would flip it.
Ask me up to three questions before you start only if a number you need
is missing. Keep it concrete. No buzzwords.
Paste into ChatGPT, Claude, or your tool of choice. Replace the bracketed bits with your own, and let it pressure-test the spend before you commit.
TEAHEE Moment
Stay sharp, stay informed. See you tomorrow!
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