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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.

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.

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.

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 :

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.

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

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

The SaaS era was defined by unbundling : find a workflow, optimize it, own it. Salesforce chose sales automation. Slack chose chat. Dropbox chose file sharing. Point solutions won by perfecting single workflows. The playbook : own one pain point, expand from there.

AI is moving faster than anyone predicted. When models change every 42 days, buyers can’t assemble a best-of-breed stack. They want a platform they can trust for three to five years.

Last week, Cursor launched Composer 2 to over one million daily active users.¹ Within hours, a developer discovered Cursor had built its flagship model on top of Moonshot AI’s Kimi K2.5, a Chinese open-source model.²

Moonshot AI’s response? “This is the open model ecosystem we love to support.”³

Cursor’s model is at near parity with state-of-the-art at one-eighth the price.⁴ It’s also no coincidence the editor powering Cursor is open-source, VS Code.

In 2025, we predicted that 2026 would be the year agents would earn as much as a person.

It’s already happening.

In markets where there’s a labor shortage and an urgent need to hire people, we are seeing agents command 75%, 85%, even 100% of a human equivalent salary. This is faster than we were anticipating.

The first-order benefit is completing the work.

But there are second-order benefits that are now starting to appear. Training agents is significantly faster since all materials can be presented at once & in parallel to the AI.