The Most Important Chart In AI Right Now — Visual Edition
🎬 Matthew Berman📅 Aug 26, 2026⏱ 20:46
open weightstokenomicsDeepSeekChinaplatform risk📎 with slides
📊 The Chart (Vercel)
"I've been predicting this for a while — and the flip just happened." The chart, from AI-hosting company Vercel, shows the share of open vs closed weights models going through their platform from June to August 2026. The yellow (closed: ChatGPT, Claude) line is trending down; the blue (open weights) bars are trending up. 1:00
The original Vercel chart — open vs closed weights, June→August 2026. (Slide: "First: the chart everyone shared")Open-weight token share surged: 28.4% → 62% (Aug 22 peak) → 50.3% (Aug 24 snapshot). "A one-day peak is not a stable market share." (Slide 04)
Important caveat: this is token volume, not revenue — and the total pie of tokens is itself growing. Open weights are winning usage, not (yet) dollars. 2:38
🐋 DeepSeek Eclipses Anthropic
"DeepSeek has actually eclipsed Anthropic in total token share percentage." The red line: DeepSeek at 25.2% vs Anthropic's 24.5%. But look at the purple line — total model spend — and the story inverts. 2:58
The headline chart: tokens vs modeled spend. DeepSeek 25.2% tokens but 2.8% spend; Anthropic 24.5% tokens but 64.6% spend. (Slide 05)
The wild number: DeepSeek captures just 2.8% of token-spend dollars vs Anthropic's 64.6% — "Anthropic's spend share is 23× DeepSeek's, and I don't think that's going away anytime soon." DeepSeek wins the tokens; Anthropic wins the value. 3:28
💎 The Frontier Is Worth Billions
"These models are quite close — a few percentage points in benchmarks. But the absolute frontier is worth billions." The difference between "95% as good" and the frontier is massive. Zoomed out: the top models take ~50% of token share but ~90% of revenue. 4:00
The money table: Anthropic+OpenAI = 40.1% of tokens but 82.6% of modeled spend. (Slide 06)
Gavin Baker's prediction (VC, tokenomics): "frontier tokens (Anthropic, OpenAI) are 60–90% of all economic value, but only 10–25% of tokens." The hardest tasks are where people pay a huge premium — "high-frequency trading: the value of a slight edge over your competitor is literally billions." 6:45
💰 The Pricing Gap
Model
Output price
Claude Fable 5
$50 / million output tokens
DeepSeek V4 Flash
$0.18 / million output tokens
Listed price per 1M output tokens: $50 (Claude Fable 5, closed) vs $0.18 (DeepSeek V4 Flash, open). (Slide 09)
"That's a crazy price difference." "If DeepSeek V4 Flash can handle the vast majority of tasks for the vast majority of people, why pay $50? But when the right answer matters — when the Flashes can't get there on the hardest problems — the extra cost is well justified." Anthropic above $65B annualized, OpenAI ~$40B — "more than every open model provider combined." 7:43
🏢 Who's Building On Them
Big, household-name US companies — not anonymous startups: Thomson Reuters (Qwen), Harvey legal AI (Kimi K3), Cursor (Kimi K2.5), Airbnb (Qwen), Perplexity (DeepSeek). 9:25
Five Western firms building on Chinese AI: Thomson Reuters→Qwen, Harvey→Kimi K3, Cursor→Kimi K2.5, Airbnb→Qwen, Perplexity→DeepSeek. (Slide 14)Specialization changes the math: Harvey Tenet — 2× legal tasks vs base model, 90% lower cost, 58% fewer tokens. (Slide 21)
The CTO of Thomson Reuters: "Renting a house — you have a roof and someone takes care of it, but you're not building equity that compounds. You're renting intelligence from OpenAI and Anthropic. Train the open model yourself, give it your data — that's real equity." 17:05
🧭 The Three-Way Split
Christian Catalini (MIT cryptoeconomics lab, ex-Meta head economist) predicts token-spend value splits three ways. 11:55
Cost-per-task, not per-token: (Bindu Reddy) — Kimi K3 is half the per-token price of GPT-5.6 Soul, but a completed task costs 84¢ vs 96¢ because "some models use many more tokens to complete the same task." Judge by the finished job, not the sticker price. 10:55
⚠️ The Geopolitical Risk
The closing argument — and Matthew's one worry. "Open weights win on volume, closed labs win on revenue. But most open-weights models are coming out of China." 18:55
The dependency trap: "if US enterprises build on Chinese open-weights models, it sounds good short-term — but when those models start being co-designed with Chinese chips, and we're building on top of them, the United States becomes dependent on Chinese chips. That's a big geopolitical risk." He's "still worried the US does not have a strong open-source strategy yet." 19:05
The advice: "go get an open-source model. Download it, customize it, use it at your business, get familiar — you'll save money and have much more control." 19:45
✅ Key Takeaways
The verdict slide: "Open models win token volume. Frontier labs win model spend." (Slide 27)
The flip happened: open-weights models are winning token volume on Vercel's platform; closed weights are trending down.
But volume ≠ value. DeepSeek has 25.2% of tokens but 2.8% of spend; Anthropic 24.5% of tokens but 64.6% of spend (23×).
The frontier is worth billions — "the difference between 95% as good and the frontier is massive" (60–90% of value, 10–25% of tokens).
Open weights win on ownership, bargaining power, customization, and ecosystem (151k Qwen derivatives) — and platform risk is the reason enterprises switch.
Judge cost-per-completed-task, not per-token — Kimi K3 at half the token price still costs ~the same per task.
The geopolitical catch: most open-weights models are Chinese, and co-design with Chinese chips creates US dependency risk.
📎 Visual edition note: the nine slide images embedded in this story are extracted directly from the presenter's official slide deck (Open Models, Frontier Labs, and Who Gets Paid — the Box link in the video description), rendered from the original 30-page PDF and re-compressed for web. Slide numbers referenced in each caption map to that deck.
