Tomasz TunguzModify

2025-12-08 08:00:00

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

The deal values Confluent at $11.1 billion, or 10.0x LTM revenue. Confluent commands more than 40% of the Fortune 500 as customers & has grown into a $1.1 billion revenue business.

The founders of Confluent, including CEO Jay Kreps, created Apache Kafka, a streaming technology built inside LinkedIn. Founded in 2014, Apache Kafka now runs at more than 80% of the Fortune 100. Kafka powers real-time data pipelines & stream processing, updating data systems whenever a new event happens. When a taxi ride is booked, a credit card is swiped, or a user likes a comment, Kafka handles the data flow.

Confluent continues to grow nicely, with recent quarterly revenue of $298.5 million, growing 19.3% year over year. It has a gross margin of 74.1%, which is typical for software companies. Although its operating margins are negative, roughly -27%, this reflects a high cost of sales.

The company’s sales efficiency sits at 0.38x : Q3’s marginal gross profit of $13.5M annualized ($54M) divided by Q2’s selling & marketing expenses of $143.6M. For every dollar spent on S&M in one quarter, the company generates 38 cents in incremental gross profit the next year.

New customer acquisition reinforces the challenge.

Confluent added 36 net new $20K+ customers in Q3. The $100K+ cohort represents the largest sequential increase in 2 years.

Growth comes primarily from expansion : net revenue retention sits at 114%, meaning existing customers increase spend by 14% annually. Gross retention hovers near 90%. The $100K+ customers account for more than 90% of ARR. Confluent’s challenge isn’t keeping customers. It’s acquiring new ones.

The most compelling trend : 10-percentage-point YoY improvement in operating margins, suggesting a path to profitability within 12-18 months.

Is Confluent a bargain or appropriately priced?

The bull case points to category-defining technology in Apache Kafka, 21%+ growth with improving margins, a clear path to profitability & critical infrastructure for AI & real-time applications. The bear case notes the company remains unprofitable, faces competitive threats from AWS Kinesis & Azure Event Hubs, competes with open-source Kafka alternatives & carries IBM integration execution risk.

A major question for Confluent over the last few quarters has been : what is the next adjacent product category to catapult another wave of growth? The AI surge has been a positive for the company.

Within IBM, Confluent may find its technology complementary to the raft of AI technologies demanded by large enterprises.

2025-12-05 08:00:00

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.

The agent tackles the same task three different ways. Sometimes it nails the solution on the first try. Other times it needs more input. That engineer has paralleled her time by 10x to 15x.

Why can’t a business person do the same?

I read Twitter articles about developments in AI & have questions, but I don’t have time to delve deeper then. I often pop out of meetings with five tasks to process, & I’d love to dictate my thoughts. I have an idea for a vibe-coded experiment to run internally.

So I hooked up Gemini command line to my Asana. Now I can bark out orders like Gunnery Sergeant Hartman & have a team of agents respond in minutes. Accessing all the tools I’ve built.

This is different than accessing AI in the browser. The agent has access to my systems : Asana, email, calendar, CRM. It can read my files, pull data from my tools, & act on my behalf. I’m not copying & pasting between browser tabs. The agent works within my environment.

I’ve gone from producing 10 to 31 tasks per day. Agents are working on my behalf while I am in meetings.

I had an idea this morning : run a statistical analysis on my Asana usage. An agent wrote a piece of code to hit the Asana API & save the data into a particular folder. An R script generated these two charts & many others. I ran a collection of different statistical analyses. We went back & forth in the Asana comments on which visualizations were the right ones. These aren’t simple tasks; many of them have 30 or 40 comments, representing iterations on the original idea.

Then I created an outline in the SCQA format. Within five minutes, this post had been written. Another agent graded the post & ensured it matched my style.

This is the power of AI : parallel work while we’re passive (I was at the gym at the time).

When we give agents the right tools, the velocity of work changes instantly. Business people can now do what software engineers discovered months ago. We can spin up 15 parallel workstreams. We can evaluate results over coffee. We can 3x our daily output without working longer hours.

The Agent Inflection Point isn’t coming. It’s here.

We will be open-sourcing this next week.

2025-12-03 08:00:00

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 :

Are we in a bubble echoing the telecom crash, or building the next internet? Do traditional exit paths still work when secondaries dominate & IPOs vanish? How do you design tools when the user is AI, not human? 2025 forced a reckoning with reality.

1. How AI Tools Differ from Human Tools : I consolidated my 100+ AI tools into unified, parameter-rich interfaces based on Anthropic’s research. The counterintuitive finding : AI systems need complex tools with complete context, while humans need simple, chunked interfaces. Claude’s success rate approached 100% after the redesign.

2. Back to Text : How AI Might Reverse Web Design : I watched an open-source agent book flights by navigating airline websites, extracting data from visual chaos. If AI thrives on pure text, the future of the web might look exactly like it started : simple text, but for robots instead of humans. The better AI performs, the fewer websites we’ll visit.

3. Circular Financing : Does Nvidia’s $110B Bet Echo the Telecom Bubble? : Nvidia’s vendor financing totals $110B in direct investments plus $15B+ in GPU-backed debt, 2.8x larger relative to revenue than Lucent’s exposure in 2000. But unlike the telecom bubble, Nvidia’s top customers generated $451B in operating cash flow in 2024. The merry-go-round has paying riders.

4. The Scaling Wall Was A Mirage : Gemini 3 launched with the same parameter count as Gemini 2.5 but achieved massive performance improvements, breaking 1500 Elo on LMArena. Oriol Vinyals credited improving pre-training & post-training, with no walls in sight. Nvidia’s earnings confirmed Blackwell Ultra delivers 5x faster training than Hopper, translating scaling into capability.

5. The Complete Guide to SaaS Pricing Strategy : The only three pricing strategies that matter : Maximization (revenue growth), Penetration (market share) & Skimming (profit maximization). Usage-based pricing experienced 29% longer sales cycles in 2023, but companies like Twilio achieved 130%+ net dollar retention by deliberately underselling initial contracts & expanding naturally.

6. The Great Liquidity Shift : 71% of exit dollars in 2024 came from secondaries, not IPOs or M&A. With target ARR for IPO growing from $80M in 2008 to $250M today, secondaries have become a permanent fixture in venture capital markets. Venture is evolving to look more like private equity.

7. 2025 Predictions : I predicted the IPO market would rip, voice would become a dominant AI interface & the first $100M ARR company with 30 or fewer employees would emerge. Data center spending by hyperscalers would eclipse $125B, stablecoin supply would hit $300B & consolidation would define the Modern Data Stack.

8. OpenAI’s $1 Trillion Infrastructure Spend : OpenAI committed $1.15T in infrastructure spending from 2025-2035 across Broadcom ($350B), Oracle ($300B), Microsoft ($250B), Nvidia ($100B), AMD ($90B), Amazon AWS ($38B) & CoreWeave ($22B). At OpenAI’s projected 70% gross margins, this implies nearly $1T in revenue by 2030.

9. The AI Cost Curve Just Collapsed Again : DeepSeek released two breakthroughs : V3 slashing training costs by 90%+ & R1 delivering top-tier performance at 1/40th the cost. The innovation? Simply asking AI to show its work. The net : powerful smaller models with 25-40x reduction in price, plus explainability that could satisfy GDPR & enterprise auditability requirements.

10. The Mirage in the Software Clouds : Public SaaS growth rates halved from 36% to 17% since 2023. It’s not that software spending is slowing, it’s that high-growth companies aren’t going public. The IPO drought since 2022 means public SaaS analyses no longer reflect the true state of software anymore.

I’m grateful for your readership & engagement throughout 2025. Thank you.

2025-11-30 08:00:00

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.

Listening to Lenny’s Podcast with Jeanne Grosser, COO at Vercel, I discovered how some companies are achieving that milestone by transforming their inbound sales teams in six weeks.

For us, we had 10 SDRs doing this inbound workflow, & now we just have one that is effectively QAing the agent. The other nine, we deployed on outbound.

It was six weeks before we felt confident going from 10 to 1. So it wasn’t like this was a multi-quarter process. It actually moved super quickly.

We were able to hold that lead to opportunity conversion rate flat. So the agent is as good as our humans were.

In retrospect, it should be obvious that speed creates an unexpected advantage. A sales representative, or an AI sales representative, can respond & analyze a lead at any point during the day. It’s important to meet a customer at the point of maximum interest to capitalize on their inertia or excitement.

It’s actually condensed the number of touches it takes to convert because it’s so much quicker at responding relative to leads inevitably sitting in the queue or coming in at nighttime & no one can get to it.

The build? One engineer, part-time.

The person who built the lead agent was a single GTM engineer. He spent maybe 25, 30% of his time on this.

The target that every sales leader at that dinner articulated is now within reach.

I think we’re getting to a point where with layering in agents, ideally, we finally get salespeople to a point where they’re actually spending 70% of their time interacting with humans.

Thirty percent customer-facing time becomes seventy percent. Double the human contact means salespeople developing deeper relationships, driving more success for customers, & less on administration.

That’s the promise of AI in the workplace.

2025-11-25 08:00:00

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 crossover happened in 2009, when PE inventory overtook public company counts for the first time. By 2024, PE-backed companies outnumber public firms by roughly 4:1.

The shift is driven by the massive expansion of PE ownership across corporate America.

That’s not to say the sizes of PE-owned companies are the same as publics. In fact, they are smaller.

The point isn’t that startups previously focused on public companies. Rather, the data reveals the immense scale of private equity portfolios.

Data Sources : CRSP¹, Wilshire 5000², PitchBook³, American Investment Council⁴, Citizens Bank⁵.

The mid-market profile of these PE-owned companies suits AI startups’ desires for faster sales cycles.

Plus, the profit motive of private equity aligns perfectly with AI startups’ capacity to cut costs & drive efficiency. PE firms acquire companies to improve margins & operational performance before exit.

AI tools that reduce headcount, automate processes, or accelerate workflows deliver exactly what PE operating partners need.

A private equity firm owning 25 companies proves value in one or two before rolling out to the entire portfolio. Control enables rapid deployment. This creates an efficient channel for AI startups to demonstrate value & cross-sell.

PE firms gain operational leverage while AI startups access more than 14,000 motivated buyers. This new go-to-market motion redefines how AI software reaches the market, bypassing the traditional enterprise sales grind in favor of networks that can deploy at scale.

2025-11-21 08:00:00

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.

Market share is fluid because no one yet knows what AI can do & the second we think have grasped it, models improve. The Nvidia chip performance & the launch of Gemini 3 the biggest gain ever in Google model performance suggest no simmering ahead.

As long as the underlying models hurtle towards PhD level performance, people will continue to test. How much better is Gemini 3 at coding? tool calling? writing?

If the progress is material, then the benefit of switching is worth the activation energy.

Today, startups, incumbent software companies, cloud providers & AI labs all are competing. First the model, then infrastructure (memory & retrieval), then tools, then applications. Will the foundational models play at the application layer? Or will the applications differentiate themselves enough to overcome model differences?

Who can take advantage of the next big leap in model performance fastest? Which sales team can reach the target customers first & write the RFP?

This is the Great Game of Risk in Category Creation & aggression wins.

But this era of fluidity won’t last forever. The rate of improvement in AI models will eventually attenuate. When the performance gap between the best model & the second-best model shrinks, the incentive to switch evaporates.

Switching costs will start to matter more than marginal performance gains. The custom tools I’ve built, the muscle memory I’ve developed, the integrations my company has deployed, the enterprise contracts signed, all inertia.

At that point, the fat begins to congeal.

The winners will be those who use the sizzling phase to build fat worth congealing around.

This fun analogy came up during my conversation with Harry, Jason, & Rory.