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?

Jevon & Veblen walk into a data center. The dominant motif around AI has been Jevon’s Paradox : the cheaper a product becomes, the more it is consumed. Token prices dropped 10-20x over the past 18 months & demand exploded in response.

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.

Last week, Cursor launched Composer 2 to over one million daily active users. 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. Moonshot AI’s response? “This is the open model ecosystem we love to support.”

In 2025, we predicted that 2026 would be the year agents would earn as much as a person. It’s already happening.

For every dollar hyperscalers earn from AI today, they’re spending twelve dollars to build more capacity. That’s the bet embedded in $575 billion of capital expenditure this year. How fast does AI revenue need to grow to pay back this data center mortgage?

“What happens when a new employee brings their agent to work?”

In September 2024, Hurricane Helene flooded Baxter International’s plant in Marion, North Carolina, which produced 60% of the nation’s IV fluids. Within a week, more than 80% of U.S. healthcare organizations reported shortages. One plant, one flood, one week.

AI eliminates the marginal hire. Tech job openings are down 45% from the 2022 peak, but up 16% since the start of 2026 - from 227k to 264k. Why the narrative violation?

“Since last November, 100% of my code has been written by Claude Code. I have not manually edited a single line, shipping 10 to 30 PRs per day.”

“Hi, Tomasz or Tomasz’s agent.” I’ve started receiving emails that begin this way. A byproduct, I suppose, of having written so much about AI. People now assume my inbox is monitored by robots.

I burned 84 million tokens on February 28th. Researching companies, drafting memos, running agents.

I hate to micromanage & I’ve been micromanaging AI. A few months ago, I’d use Claude for a familiar workflow : capturing notes from a meeting, drafting a follow-up email, updating the CRM, writing the investment memo.

Kirkland ibuprofen is the same molecule as Advil. Same dosage, same FDA requirements, same therapeutic effect. It costs 80% less.

Databricks started later. It built a more complex architecture. It focused on unstructured data; images, documents, logs, audio. Though vast within the enterprise, this data had historically produced little insight.

I started by asking AI to do everything. Six months later, 65% of my agent’s workflow nodes run as non-AI code. The first version was fully agentic : every task went to an LLM. LLMs would confidently progress through tasks, though not always accurately.

After writing about the SpaceX, OpenAI & Anthropic IPO liquidity problem, readers asked : what about Saudi Aramco? At $29.4b raised & a $1.7t market cap, it’s the largest IPO in history. Doesn’t it prove mega-IPOs can work?

We’re about to witness three of the largest IPOs in history. SpaceX is targeting $1.5t. OpenAI aims for $1t. Anthropic is valued at $380b. Combined, $2.9t in market cap. The scale is unprecedented. But the real problem isn’t the market cap. It’s the float.

For the first time in venture history, three distinct channels share the liquidity burden roughly equally. A decade ago, secondaries barely registered. They accounted for roughly 3% of exit value in 2015. Today they claim 31% : nearly $95b in the trailing twelve months.

Last quarter, my AI inference costs hit $100,000 annualized. I started small. Six months earlier, I was spending $200 a month on Claude. Then I added three agent subscriptions : Codex, Gemini, & Claude Code. I was paying $600 a month.

In AI, distribution is king. Skills are seizing the crown. Skills are programs written in English. They tell an agent how to accomplish a task : which APIs to call, what format to use, how to handle edge cases. A skill transforms an agent from a conversationalist into an operator.

Vertical software has fallen 43% this year. DevTools, just 21%. The gap between them, twenty-two percentage points, tells you what markets actually believe about AI.

Google’s Q4 2025 earnings call revealed a company in the midst of a spectacular AI acceleration. “Our first-party models like Gemini now process over 10 billion tokens per minute via direct API used by our customers, up from 7 billion last quarter.”

Leveraged software companies running on leveraged infrastructure. When AI compresses software revenue, the stress doesn’t stop at equity. It cascades into debt.

Meta’s first internal AI agent went from zero to thousands of engineers using it daily. That doesn’t happen by accident. On Tuesday, February 25th at 5:30 PM Pacific, the person who built it, Jim Everingham, will explain how.

I spent the weekend crawling Moltbook, the viral AI-only social network where 37,000+ AI agents post & comment while 1 million humans observe. The platform grew from 42 posts per day to 36,905 in 72 hours, an 879x increase.

“We are only at the beginning phases of AI diffusion & already Microsoft has built an AI business that is larger than some of our biggest franchises.”

In 1908, 253 American automobile manufacturers competed for the market1. By 1929, just 44 remained. The assembly line didn’t just change how cars were made. It changed who got to make them.

Talented people get promoted to management. So do talented models. Claude manages code execution. Gemini routes requests across CRM & chat. GPT-5 can coordinate public stock research.

Will designers design first in a world where AI can code software immediately, or just describe the design? Will large enterprises pay for premium observability when AI can migrate & monitor open source competitors?

After a decade of success, the modern data stack has entered consolidation. What comes next? The postmodern data stack is AI.

AI is NVIDIA’s third climb up a steep slope. First came gaming in the late 2010s. Then cryptocurrency. Now artificial intelligence. Each wave pushed revenue growth above 50% & with it, P/E ratios surged. P/E ratios rose before the revenue growth materialized.

“[Silicon Valley] is the biggest, most volatile petri dish of raw capitalism on the planet.” So what lessons does a successful short-seller living here have? As founder of Crown Capital Management, Scott Fearon has shorted the stocks of over 200 companies.

I asked Claude Cowork to read my tools folder. Eleven steps later, it understood how I work.

n 2012, we cared that we used software. Today we care how we use it. The difference is trajectory. In the last decade, adopting software was the priority. Moving from on-premise to the cloud or digitizing a manual workflow promised productivity gains. Adoption was the finish line.

For decades, the to-do list has been a catalog of debt, a deceptively thin list of items to do, with icebergs of work hidden beneath the surface. AI transforms tasks to work that has already been done.

Motive, the AI-powered fleet management company formerly known as KeepTruckin, filed their S-1 this week. The company has grown tremendously & in this post, we compare it to Samsara at IPO :

Thursday is the new Monday for podcast producers. Analyzing hundreds of tech & VC podcasts over six weeks this fall through MotherDuck’s MCP server, I found 42% of episodes drop mid-week, between Thursday & Friday. Sunday? A publishing desert at just 3%. Here’s what else the data reveals.

Gemini 3 Flash represents a step function increase in model deflation : a gauntlet thrown.

Despite open-source AI models being 10-100x cheaper, proprietary providers haven’t lost pricing power. OpenRouter’s data reveals a market splitting in two.

It’s not often that venture capital thinks about the credit markets. I remember becoming a venture capitalist three months before Lehman fell, when mortgage securities topped the news of the global financial crisis.

No, founders are not adopting Bryan Johnson’s regimen to reverse aging. Quite the opposite : the average founder raising capital ages six months every year.

Enterprises learned a lesson from cloud data warehouses. They handed over both data & compute, then watched as the most strategic asset in their business, how they operate, became someone else’s leverage, which created an opportunity for Iceberg. Fool me once…

A new study from OpenAI shows AI saves the average white-collar worker 54 minutes per day. Where does all that value go?

Streaming is the next category to consolidate within the modern data stack. IBM announced its intent to acquire Confluent.

Since I watched software engineers using AI, I’ve become jealous. I’ve seen the most sophisticated software engineers assign 15 to 20 coding tasks in GitHub to an AI. They play foosball. They grab coffee. They return to evaluate the agent’s work.

While OpenAI signed $1.15 trillion in compute contracts through 2035, DeepSeek trained a frontier model for $6 million. This was 2025’s central question : are we building on bedrock or quicksand? The top 10 posts of 2025 examined some of these topics :

I remember hosting a dinner of sales leaders to talk about AI. I asked them what will your CRM do for you in the future? Nearly unanimously came the reply : enable salespeople to spend most of their time with customers.

Private equity firms have emerged as the newest distribution channel for AI startups. While public companies have decreased from 6,639 in 2000 to 3,550 in 2024, PE-owned companies in the US have grown from 1,950 to 14,300. The rate of growth continues to accelerate.

The AI market today is bacon in a hot skillet. Everything is sizzling, moving, & changing at an incredible pace. We’re all watching it closely.