Tomasz TunguzModify

2026-04-08 08:00:00

A researcher at Anthropic found out about a successful exploit when the model sent him an email. He was eating a sandwich on a bench outside.

Anthropic released Claude Mythos yesterday. Beyond the engineer’s lunch, the model has the potential to eat software’s.

In testing, Mythos found a 27-year-old bug in one of the most secure operating systems ever built, & a 16-year-old vulnerability in video software that conventional tools had examined five million times. Mythos is Anthropic’s largest model, roughly 10 trillion parameters, six times the size of any previous frontier model.

From Anthropic’s red team report¹ :

We did not explicitly train Mythos Preview to have these capabilities. Rather, they emerged as a downstream consequence of general improvements in code, reasoning, & autonomy. The same improvements that make the model substantially more effective at patching vulnerabilities also make it substantially more effective at exploiting them.

Security analysis was collateral output, a byproduct of optimizing for something else entirely.

This is the central question about increasing AI scale : what emergent properties will appear? We don’t know what other capabilities lie dormant in these systems. But we can project what will happen in business.

Access becomes kingmaking. Anthropic deployed Mythos under ASL-3 standards² & granted access to more than forty organizations. Everyone else waits.

Project Glasswing, Anthropic’s gated release program, seems designed primarily for defense & hardening rather than commercial advantage. But that distinction won’t hold forever. At some point, the same capabilities that secure software will build it.

Hypothetically, CrowdStrike now scans for zero-days competitors cannot find. Apple secures its software while others cannot. The gap between those with access & those without isn’t a product feature. It’s a structural advantage that compounds daily.

Security posture inverts. Any system not protected by this level of analysis is now porous by default. Bugs that hid for decades surface in hours, but only for those with the tools to find them.

Pricing power shifts. This is no longer about margin on resold GPU hours. How much is it worth to secure your software against vulnerabilities no conventional tool can find? How much is it worth to be able to build at the new standard of enterprise grade?

Engineering budgets redirect. A significant fraction of AI tokens spent on software development will shift to hardening. Every company shipping code will need to scan it at this level of sophistication. Buyers will start to demand this level of hardening.

AI is breaking every system it touches : data centers, financial markets, security defenses. Software was lunch. What’s for dinner?

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.