Tomasz TunguzModify

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.

2026-02-24 08:00:00

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.

AI advertising operates differently than search or social. Inside a conversation, Koah’s system processes the full semantic context of a query in real time.

When a user asks an assistant “I’m training for a marathon and my knees hurt, should I switch to minimalist shoes or get more cushioning?”, the system understands intent, constraint, & decision stage simultaneously. The ad that surfaces isn’t matched to a keyword or a profile. It’s matched to a specific moment in a purchase decision.

Or consider a developer asking “I’m building an agentic app that needs to store conversation history & vector embeddings. Which database should I use?” Only within agentic coding can a vendor be there precisely at the right moment. An agentic app builder can provide inference offset by ads.

The result : 7.5% average click-through rates. That’s 4-5x the display advertising baseline.

Over the past 12 months, Koah has processed more than 170 million queries & 35 million native ad impressions. Publishers using Koah’s native formats saw 122% higher click-through rates than their previous display implementations. Liner grew DAU 454% while monetizing with Koah. Growth & revenue moving together, not against each other. Retention remained near 100% in live chat environments.

Luke Kim, CEO of Liner, put it well :

“Since adopting Koah’s ad formats, we’ve consistently increased CTR & coverage, without eroding any of the trust at the core of our product.”

The Koah team came through South Park Commons. All three founders, Nic Baird, Mike Choi, & Herrick Fang, were Founder Fellows. Before Koah, they overlapped at three companies : Affirm, Supermove, & Qualified.

This category will define the next decade of the internet. We’re thrilled to partner with Nic & the Koah team powering AI for millions.

If you’d like to get started with your AI app, sign up here.

2026-02-23 08:00:00

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%.

At a 15% float, here’s what these three IPOs would require :

At standard float percentages, these three companies would need to raise $432-576b from public markets in a single quarter. From 2016 to 2025, the entire US IPO market raised $469b.⁴

It’s like throwing a boulder into a pond. Standard floats are impossible, so these companies will debut with tiny ones, likely 3-8%.

But that creates a different problem. The S&P 500 requires 50% public float for inclusion.⁵ At 3-8%, none qualify initially. When they do, the disruption begins.

Source : Yahoo Finance⁶

SpaceX at $1.6-2t would challenge Meta for spot #6, potentially slotting in behind Amazon. When they qualify, passive funds managing $20t must buy. Index funds can’t raise cash. They sell existing holdings.

The mechanics become self-reinforcing. Index funds sell existing mega-caps to buy new entrants. Lower mega-cap prices trigger momentum strategies to sell further. Additional selling creates more pressure on the very stocks index funds track.

These companies have challenged every assumption within their core markets. Now their IPOs will challenge every assumption about public financial markets.

References

2026-02-19 08:00:00

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.

The shift accelerated after 2021’s IPO bonanza. When public markets closed their doors in 2022, investors found alternative routes. Secondaries absorbed demand that would have flowed to traditional exits. When Goldman Sachs acquired Industry Ventures, the transaction signaled secondaries have arrived. Morgan Stanley followed with EquityZen, then Charles Schwab announced its acquisition of Forge Global. Wall Street recognized the structural change before most of venture did.

This matters for founders & investors. When IPOs dominated exits, fund models assumed a small number of public offerings would generate the bulk of returns.

Now liquidity arrives through multiple doors. A founder might sell secondary shares to patient capital while the company remains private. A GP might move positions through continuation vehicles. An LP might trade fund stakes on an increasingly liquid secondary market.

The 830 unicorns holding $3.9t in aggregate post-money valuation cannot all exit through IPOs. The math doesn’t work. At 2025’s pace of 48 VC-backed IPOs, clearing the unicorn backlog would take seventeen years. Secondaries provide a release valve that traditional exits cannot.

Companies like OpenAI have embraced this reality, running employee tender offers while voiding unauthorized secondary transfers. The largest private companies now manage their own liquidity programs rather than waiting for public markets.

Today, secondary liquidity concentrates in the top 20 names. SpaceX, Stripe, OpenAI. For the founder of company #50, the secondary market remains largely theoretical. For secondaries to succeed as a broad asset class, buyers must underwrite positions in companies without household recognition. As the market grows, this coverage gap becomes opportunity.

For LPs starved of distributions since 2022, the expansion of secondary channels offers hope. The $169b in cumulative negative net cash flows needs somewhere to go. More exit paths mean more opportunities to return capital.

When a Series B employee asks about liquidity today, the answer isn’t “wait for the IPO.” It’s “we’re planning a tender offer next year.”

A decade ago, secondaries were a footnote. Now they’re infrastructure. Liquidity flows where it can, not where tradition suggests it should.

2026-02-19 08:00:00

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.

3. Use Built-In Spell-Check

Static typing forces the AI to face a spell-check/compiler. Ruby let agents hallucinate valid-looking code that failed at runtime. Rust checks code’s grammar. One-shot success rates improve substantially for medium-complexity tasks.

4. Cajole your Team of Agent Rivals

Build your agentic braintrust. Ask Claude to create a plan. Then prod Gemini & Codex to critique it; Claude addresses the critiques & implements the code. Once implemented, ask Gemini & Codex to critique the implementation relative to the plan & Claude to revise. Agents are great micromanagers.

5. Put All the Clay in One Pot

Building an agent is an exercise in Play-Doh. Some yellow, some red, some green clay. Each from a different pot. I’d like all the tools in one place : manage my memory, manage my prompts, capture my logs because it’s all a single closed loop to improvement with the model. Prompt → Output → Evaluation → Optimization → Prompt.

6. Recognize The iPhone 15 Era of AI

Qwen 3, GLM, DeepSeek V3, & Kimi K2.5 deliver strong performance at a fraction of the cost. The models are now strong enough for workflow tool calling that more intelligence may not have as concrete a benefit. Tau2² shows many models have attained this threshold & now we’re comparing them on cost rather than accuracy.

7. Document FTW

As Harrison Chase put it : “in software, the code documents the app; in AI, the traces do.” Our system runs a nightly prompt optimization system. It collects the last 100 agent conversations, extracts failures (task timeouts, incorrect outputs, user corrections), & generates improved prompts using an LLM-as-judge³. This closed-loop improvement lifts task success rates incrementally each week without manual intervention.

8. Prompt Musical Chairs

We can’t bring the system down for new prompts. The agents watch a prompt file & reload it automatically when it changes. This separates deployment from experimentation & enables DSPy⁴-style optimization to run automatically. Combine with versioned prompt files & you get full rollback capability.

9. Who Do You Work For?

Skills are for interactive conversations. Code is for agents. Skills are easier to debug. When a skill fails, you know exactly where to look. When an agent chains ten function calls & the output is wrong, you’re hunting through logs.

What have you learned?

2026-02-17 08:00:00

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.

Within two quarters, my inference spend grew from $7,200 to $43,000 to over $100,000 run rate.

So I migrated to an open source model. It took a weekend. The key was building the right testing loops : I had six months of historical task data, so I could replay requests through the new model & hill-climb to parity with AI agents working through the night. By Sunday evening, they performed identically. At 12% of the cost.

I’m not the only one paying attention to this cost.

Technology companies are adding a fourth component to engineering compensation : salary, bonus, options, & inference costs. Levels.fyi pegs the 75th percentile software engineer salary at $375k. Add $100k in inference & the fully loaded cost is $475k. That’s 21% in tokens.

The question CFOs will pose : what am I getting for all this inference spend? Can I do it cheaper?

If the metric for a new cloud is gross profit per GPU hour, the employee equivalent is : productive work per dollar of inference.

For me, the answer is 31 tasks a day at $12k annually. The engineer still burning $100k? They’d better be 8x more productive!

Will you be paid in tokens? In 2026, you likely will start to be.