Tomasz TunguzModify

2026-04-07 08:00:00

Anthropic added $10b in revenue in the last month alone, twice Databricks’ annual run rate.

Crossing $10b is a milestone few software companies ever reach: ServiceNow took 20 years, Shopify took 18, Palo Alto Networks took 19, & Anthropic crossed that threshold in under four years.

But who’s looking backwards? How long until Anthropic is the most valuable company in the world?

NVIDIA generates $215b in annual revenue & trades at 22x, producing a $4.8 trillion market cap. To surpass it, Anthropic needs $200b in annual revenue¹. If growth continues, three years. If it decelerates steadily, four. If it normalizes rapidly, seven².

The usual caveat applies: significant customer concentration.

ServiceNow needed two decades to reach $10b. Anthropic needed forty-two months; then added another Databricks in thirty days.

2026-04-05 08:00:00

Two years ago, the idea of useful AI on your phone was fantastical. Siri couldn’t finish a sentence. Local models hallucinated nonsense.

Last week, Google released Gemma 4 E4B¹, a free model that matches GPT-4o and runs entirely on your phone.²

The next few weeks promise even more advanced pocket models. The market expects new releases from DeepSeek³, Qwen⁴, Kimi⁵ & Minimax⁶.

Frontier models don’t stay frontier for long. Within three to four months, you can run a model with similar performance on your laptop; 23 months later, you can run the same model on your phone.

Three forces are driving this compression. Better algorithms : distillation & reinforcement learning squeeze more capability into fewer parameters. Talent density : the biggest prizes in capitalism attract the best minds in the field. These are the fastest growing software companies in history. And capital : a trillion dollars invested in data centers powering training.

In 23 months, the same capability that needed 1.8 trillion parameters now fits in 4 billion parameters. A 450x compression. At this rate, the phone in your pocket will run today’s frontier models before you upgrade it.

2026-04-01 08:00:00

Two days ago, I burnt 250 million tokens in a single day.

That’s up 20x in six weeks. This idea, called tokenmaxxing, is the deliberate practice of maximizing token consumption. The question : how much electricity can we turn into useful work?

The secret is parallelization. Structure a plan at the start of the day that allows multiple agents to work simultaneously. METR research shows the latest models can now work autonomously for 12 hours, up from 1 hour a year ago. Here’s the ramp once I started implementing a daily plan :

So, what did I do two days ago? Here’s one example. I prepared a presentation for the AI Engineers Tech Talk on the infrastructure for building with agents that I’m delivering tonight.

One agent pulled git commit history from the code repository & generated a lines-of-code chart. Another queried the agent error logs & built a time series of agent failures by root cause. A third fact-checked the METR research citations. A fourth built the presentation using a JavaScript library. A fifth critiqued the overall flow & content. All of this happened in the background.

This was just one of the parallel flows in a day. The productivity ceiling? Still unmaxxed.

2026-03-31 08:00:00

On April 9th at 10:00 AM PDT, Lena Waters will kick off a new version of Office Hours.

Lena led marketing at Notion, Grammarly, & DocuSign. At Notion, she was CMO during the company’s AI product transition. She guided the shift from product-led growth to enterprise expansion while the company deepened its position in AI-powered work. At Grammarly, she oversaw marketing as the writing assistant added AI features. At DocuSign, she managed enterprise go-to-market strategy.

Today, Lena advises startups & later-stage companies rethinking growth in an AI-driven world. She has some of the most forward-thinking ideas about the future of marketing in the AI era. The pattern she’s seen across dozens of marketing leaders: most companies adopted AI tactically without redesigning their marketing operations around it.

We’re also changing the Office Hours format. The new format: 15 minutes online. One topic. Call-in questions live. No slides. No pre-written questions. Just a real conversation.

On April 9, Lena will share what she’s seen at Notion, Grammarly, & DocuSign. Plus, how she envisions the AI-native marketing organization of the future.

Register here

2026-03-30 08:00:00

Jevon & Veblen walk into a data center.

The dominant motif around AI has been Jevon’s Paradox¹ : the cheaper a product becomes, the more it is consumed.

Token prices dropped 10-20x over the past 18 months & demand exploded in response.

Anthropic surged past $19 billion in run-rate last month, up from $9 billion at the end of 2025.² OpenAI topped $25 billion in annualized revenue in February, a 17% increase in two months.³

We know GPUs, CPUs, & memory are already in short supply.⁴ Rumors of next-generation models, including Claude Mythos, suggest pricing that moves in the opposite direction.

This weekend, an accidental data leak revealed Anthropic’s secretive Mythos model.⁵ A leaked blog post described it as :

“A step change” in capability, “dramatically higher scores on tests of software coding, academic reasoning, and cybersecurity.”⁶

Anthropic stated the model is “very expensive to serve & will be very expensive for customers.”⁷ Some have speculated on inference pricing 5-6x more than existing models.

If these rumors hold, the most powerful intelligence would trade at a stiff premium. Jevon’s Paradox would give way to Veblen goods.⁸

Veblen goods are those whose demand increases with price : front-row concert tickets that cost 10x more despite worse acoustics. Nike Jordans that retail for $110 and resell for $500+. Ivy League tuition where selectivity is the value proposition.

Could AI follow this dynamic for competitive advantage? The company with capital to access the most powerful model wins. How much is that worth?

Consider a Series A founder building an AI coding assistant. Today, she pays $25 per million output tokens for Opus 4.6. Her burn rate assumes that price. If Mythos launches at $150 per million tokens, 6x more, she faces a choice : raise prices, raise capital, or watch her AI-native competitor ship features she can’t match.

The token-maxxing era ends. Companies will stop optimizing for cheap inference. They’ll deploy capital aggressively, both GPUs & dollars, to maximize capability rather than minimize cost.

Balance sheets become a moat. The most profitable companies or those who can raise capital cheaply will have the biggest advantage in their industries.

For companies that cannot respond quickly enough or afford the most sophisticated AI, the gap widens. If AI-native companies can build 10x faster with Mythos-class models while competitors are stuck on Opus 4.6, valuations will diverge further.

Jevon & Veblen walked into a data center. We don’t yet know who walks out.

2026-03-26 08:00:00

Would you choose one software over another because it has a proprietary model with better performance?

Two companies shipped custom AI models today (three in a week counting Cursor!¹), raising that question. Intercom launched Apex 1.0, a model for answering customer support tickets.² Chroma released Context-1, a model for multi-hop agent search.³

Apex 1.0 beats GPT-5.4 & Claude Opus 4.5 on customer service tasks.² Context-1 scores 97% on agent search benchmarks.³ One Intercom gaming customer saw resolution rates jump from 68% to 75%.²

History suggests⁴ these gains may be temporary. As general-purpose models improve, today’s specialized advantage erodes. But with GPU shortages, inference costs will spike, perhaps this will be the moment for built-for-purpose more efficient models.

Intercom built Apex to differentiate in a competitive market. Chroma’s bet is different. Context-1 is open-source under Apache 2.0.³ Anyone can use it. The model isn’t the product. It’s marketing rather than sales. Distribution & brand building for their vector database infrastructure.

Two philosophies. Proprietary model as differentiation versus open-source model as adoption mechanism.

“As features become ~free to build, the technology factors that will differentiate the players will be the AI under the hood. If you’re using the same general-purpose off-the-shelf model as everyone else, you have no durable differentiation.” - Eoghan McCabe²

AI models offered by software vendors have become a new axis upon which to compete. In the marketing arena, models drive attention & distribution. At the bottom of the sales funnel, they serve as competitive differentiators in performance.