2026-04-09 08:00:00
The demand for software is infinite. Kyle Daigle, GitHub’s COO, made the case concrete :
There were 1 billion commits in 2025. Now, it’s 275 million per week, on pace for 14 billion this year if growth remains linear (spoiler : it won’t.) GitHub Actions has grown from 500M minutes/week in 2023 to 1B minutes/week in 2025, and now 2.1B minutes so far this week.
But that’s not true for all roles. I use a 2x2 matrix that separates work along two axes : the ceiling of demand & whether the loop can be closed.
On one axis, demand. Infinite Demand means more output creates more value. There is no saturation point.
On the other axis, open vs closed loops. Closed Loop means AI can verify correctness without human intervention.
Closed Loop + Infinite Demand = Economic Engines. Software engineering lives here. AI writes the code. Tests verify correctness. More code enables more features. Companies will always need more software.
Closed Loop + Finite Demand = Efficiency Plays. AI bookkeeping categorizes transactions, reconciles accounts, files returns. Deterministic rules applied to numbers. But a company only has so many transactions. A company files taxes once a year. It closes the books each quarter.
Open Loop + Infinite Demand = Creative Amplifiers. Content creation & marketing strategy. AI can generate a thousand ad variations or blog posts. A person must judge the right ones to publish. Does this ad campaign align with our values? Is this strategic positioning correct? Some problems are open loop today but will close over time.
Open Loop + Finite Demand = Utility Tools. Preparing 10-Ks & 10-Qs. Legal contract review. Insurance claims processing. One report per quarter, one contract per deal. AI makes the work faster, but doesn’t create new work to do.
Every role fits somewhere on this 2x2. I would put venture capitalist in finite demand & open loop. There’s only a certain amount of venture capital dollars entering the ecosystem in a year, & investment selection remains an open problem.
Where does yours fit?
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?
ASL-3 is Anthropic’s safety tier requiring the most stringent protections for models that substantially increase risk of catastrophic misuse. ↩︎
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.
Assuming a 25x forward revenue multiple, compared to NVIDIA’s 22x on its $215b run rate producing a $4.8 trillion market cap. ↩︎
Bull case assumes 150% growth in year one, declining to 100%, 50%, then 25%. Base case assumes 100% growth declining to 67%, 50%, then 33%. Bear case assumes 50% growth declining to 40%, 30%, then 25%. ↩︎
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.
Gemma 4 E4B matches or exceeds GPT-4o across multiple benchmarks including MATH, GSM8K, GPQA Diamond & HumanEval. Full benchmark comparison ↩︎
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.
