Tomasz TunguzModify

2026-03-04 08:00:00

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. Micromanagement at 10x speed. The agent would finish a step, then wait. I’d scan the output, type the next instruction, wait again. Prompt, response, prompt, response. I was the bottleneck in my own system.

A year ago, this was necessary. The models couldn’t hold a complex task in their heads. Now they can.

But this leverage requires planning. Now I sketch the workflow before I touch the machine. I anticipate the decision branches : what if the company isn’t in the CRM? What if the website is down or the call transcript isn’t available? I flag the gaps before the agent encounters them.

This morning’s notebook page :

I took a photo & shared it with Claude & walked away. Workflows as images work beautifully.

The agents run in the background. The memo sat in my inbox, formatted, sourced, ready to send.

Not prompts. Blueprints.

2026-03-02 08:00:00

Kirkland ibuprofen is the same molecule as Advil. Same dosage, same FDA requirements, same therapeutic effect.¹ It costs 80% less.

AI has its generic drug moment. DeepSeek V3 matches GPT-5.2 on most benchmarks.² It costs 90% less. OpenAI & Anthropic generated $22 billion in 2025.³ Chinese AI labs generated $1.8 billion.⁴ The ratio : 12:1.

Pricing explains the gap. Chinese AI API prices collapsed 90% in 2024.⁵ US frontier models average $3.38 per million input tokens. Chinese models average $0.48.

OpenAI processes roughly 8.6 trillion tokens per day.⁶ Chinese labs likely match or exceed this volume. The 12:1 revenue gap isn’t usage. It’s price.

Three forces drive Chinese prices down.

First, distillation commoditizes capability. Anthropic accused DeepSeek, Minimax & Moonshot AI of conducting “industrial-scale campaigns” to extract knowledge from Claude.⁷ OpenAI made similar accusations to Congress.⁸

Second, hyperscalers subsidize AI to win cloud customers. Alibaba Cloud cut LLM pricing by up to 97%.⁹ Baidu, ByteDance & Tencent spent $1.1B on AI subsidies during Chinese New Year 2026 alone.¹⁰

Third, DeepSeek set the floor. They trained V3 for $6 million versus OpenAI’s $100 million+ for GPT-4,¹¹ price at $0.14 per million input tokens & hit $220 million ARR with 122 employees.

In the US, Chinese models also price at a discount. Together AI charges $1.25 per million input tokens for DeepSeek V3.¹² DeepInfra offers $0.21 per million.¹³ DeepSeek’s own API charges $0.14 - 12x less than GPT-5.2.¹⁴

Pharma companies spend billions developing a molecule, then enjoy 20 years of patent protection to recoup R&D costs before generics flood the market. AI follows the same pattern - massive R&D costs upfront, then commoditization. But the timeline is compressed.

In pharma, the generic window opens after two decades. In AI, it opens in weeks. DeepSeek V3 costs $0.14 per million tokens. GPT-5.2 costs $1.75. Same capability. Different label. The 90% discount isn’t coming. It’s here.

The question : how to protect an asset that takes hundreds of millions to develop when it can be copied in a month?

Sources

2026-03-01 08:00:00

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. Too hard to process. Too messy to query. Too expensive to store in formats that mattered.

Snowflake took the opposite bet. Structured data. Clean tables. SQL queries that ran fast & returned answers executives could read. The market agreed. Snowflake went public at a $70 billion valuation. Databricks raised private rounds at half that.

Then AI arrived. Suddenly the data that was too messy to query became the data that models needed to train. Unstructured data wasn’t a liability. It was the asset.

Databricks has overtaken Snowflake in revenue. Two years ago, Snowflake led by $220 million per quarter. Today, Databricks leads by $120 million. Databricks’ growth rate is accelerating at scale, from 50% to 55% to 65% year over year. Growth rates don’t accelerate at $5 billion in revenue.

The crossover happened because AI is an architectural transition, not a feature addition.

Most enterprise data never made it into Snowflake. It sat in object storage, unstructured, waiting. Databricks built tools to use it there. No migration required.

Databricks SQL, their direct assault on Snowflake’s core business, grew from $100 million to $1 billion in under three years. AI products are growing faster still. Lakebase, their serverless database for AI agents, is six months old & already growing twice as fast as data warehousing.

Snowflake sees the threat. They’ve launched Intelligence, signed thousands of customers, inked $200 million in partnerships with OpenAI & Anthropic. CEO Sridhar Ramaswamy is betting the company can catch up.

By FY27, the gap widens further. Databricks projects $8.9 billion. Snowflake guides to $5.7 billion.

2026-02-27 08:00:00

Could you operate your company with half the people?

Jack Dorsey’s announcement yesterday¹, reducing Block’s headcount from 10,000 to 6,000, should provoke this question in every management team. The stock surged 24%. Dorsey’s memo framed it as inevitable :

Within the next year, I believe the majority of companies will reach the same conclusion & make similar structural changes. I’d rather get there honestly & on our own terms than be forced into it reactively.

Block isn’t alone. Through February, tech companies have laid off 23,000 employees.² Annualized, that projects to 153,000, exceeding 2023’s peak.

What makes 2026 different is who’s cutting. These aren’t distressed companies. They’re modestly growing businesses concluding they can operate with fewer people.

The revenue per employee gains are tremendous.³ Block’s jumps 67% post-layoff, from $2.4M to $4M per person. Once competitors demonstrate this efficiency, it’s untenable not to match it.

This changes efficiency expectations for investors. Five years ago, $100K ARR per employee was standard for SaaS startups.⁴ Today, AI-native companies like Cursor & Gamma hit $2-4M. Cursor runs $3.3M. Gamma hits $2M. An order of magnitude difference.

Management teams now expect twice the productivity. For employees, there’s never been a better opportunity to meaningfully impact a company through the leverage of AI. The game on the field has changed.

2026-02-26 08:00:00

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.

So I added tools to constrain what the LLM could call. Limited its ability to deviate. I added a Discovery tool to help the AI find those tools. Better, but not enough.

Then I found Stripe’s minion architecture. Their insight : deterministic code handles the predictable ; LLMs tackle the ambiguous.

I implemented blueprints, workflow charts written in code. Each blueprint specifies nodes, transitions between them, trigger conditions for matching tasks, & explicit error handling.

extract_domain (code) → attio_find (code) → harmonic_enrich (code)
    → generate_summary (LLM, 1 turn) → notion_prepend (code)

This differs from skills or prompts. A skill tells the LLM what to do. A blueprint tells the system when to involve the LLM at all.

Each blueprint is a directed graph of nodes. Nodes come in two types : deterministic (code) & agentic (LLM). Transitions between nodes can branch based on conditions.

Deal pipeline updates, chat messages, & email routing account for 29% of workflows, all without a single LLM call.

Company research, newsletter processing, & person research need the LLM for extraction & synthesis only. Another 36%. The workflow runs 67-91% as code. The LLM sees only what it needs : a chunk of text to summarize, a list to categorize, processed in one to three turns with constrained tools.

Blog posts, document analysis, bug fixes are genuinely hybrid. 21% of workflows. Multiple LLM calls iterate toward quality.

Only 14% remain fully agentic. Data transforms & error investigations. These tend to be coding tasks rather than evaluating a decision point in a workflow. The LLM needs freedom to explore.

AI started doing everything. Now it handles routing, exceptions, research, planning, & coding. The rest runs without it.

Is AI doing less? Yes. Is the system doing more? Also yes.

The blueprints, the tools, the skills might be temporary scaffolding. With each new model release, capabilities expand. Tasks that required deterministic code six months ago might not tomorrow.

2026-02-25 08:00:00

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?

Aramco isn’t a good proxy. The next biggest example, Alibaba at $231b, is a better parallel.

In 2019, Aramco floated 1.5% of the company. Six years later, it’s still just 2.4%. The Saudi government holds 81% directly; the sovereign wealth fund holds another 16%. The IPO served strategic goals beyond capital markets.

Contrast Alibaba & SoftBank. Both started with real floats & expanded as founders & early investors exited. Alibaba’s float grew from 15% to 86%. SoftBank’s nearly doubled.

Float expansion is only half the story. The other half is timing. When do those shares actually hit the market? The typical IPO has a 180-day lockup, a period when insiders cannot sell shares.

The September 2015 unlock released 5x more shares than the IPO itself. The stock fell 52% from its $120 peak to $58, below the IPO price.

SpaceX, OpenAI & Anthropic may face less pressure. All three have run regular tender offers for years, allowing employees & early investors to sell before any IPO. SpaceX runs tenders 2-3x per year. OpenAI completed a $10.3b secondary sale in 2025. Anthropic launched its first tender in early 2025 at $350b. Much of the pent-up selling has already occurred.

But even Alibaba at $231b was a fifth to a tenth the size of SpaceX’s target. The market has never absorbed a trillion-dollar IPO where liquidity was the goal.