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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.

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.

Every major interface shift finds its answer to meeting the needs of a massive audience in advertising : TV, web, mobile, streaming. Each is a hundreds-of-billions-of-dollars market. AI will be no different.

Today we announced Theory Ventures is leading Koah’s $20.5M Series A, with participation from our friends at Forerunner & South Park Commons.

Koah is building the monetization layer for AI applications. As AI search reaches its tipping point, assistants are becoming the primary source of information for many decisions. But AI apps face mounting pressure to monetize without compromising user experience.

We’re about to witness three of the largest IPOs in history. SpaceX is targeting $1.5t.¹ OpenAI aims for $1t.² Anthropic is valued at $380b.³ Combined, $2.9t in market cap.

The scale is unprecedented. But the real problem isn’t the market cap. It’s the float.

Typical IPOs offer 15-25% of their shares to public markets. This creates enough liquidity for price discovery while allowing founders & early investors to maintain control. Facebook floated 15%. Google floated 19%. Alibaba floated 15%.

I’ve spent the last year building AI agent systems. Here are nine observations.

1. Prototype with the Best

When the input is unpredictable, email parsing, voice transcription, messy data extraction, reach for state of the art. Figure out what works with the best models, then specialize them over time.

2. Polish Small Gems

I fine-tuned Qwen 3 for task classification using rLLM¹. The 8B model beats GPT 5.2 zero-shot prompting & runs locally on my laptop. Fine-tuning shines when the task is well-defined & the input distribution is stable.

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.

Next I started using AI to transform my todo list into my done list, increasing tasks to 31 per day. $92 daily inference invoices started arriving. Then $400 per month on browser agents.

Scale AI sold for $14.8 billion.¹ Character.AI for $2.5 billion.² Inflection for $650 million.³ They represent a tiny fraction of the market.

From 2020 to 2025, there were 5,700 AI & ML acquisitions. Only 21% disclosed a deal value. The remaining 4,500 share a pattern : small teams absorbed into larger companies. No fanfare. No valuation headlines. Just talent migrating from startup to incumbent.

Energy is up 17% this year. Materials 16.5%. Industrials 12%. Technology is flat.

Over the past month, $3.25 billion moved into XLE (energy) while $1.66 billion left XLK (tech).¹

The logic isn’t obvious until you look at operating leverage.

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.

Remember Trinity in The Matrix? “Can you fly that thing?” Neo asks. “Not yet,” she says. Seconds later, Tank uploads a B-212 helicopter pilot program directly into her mind. She steps into the cockpit & flies.