2025-05-30 08:00:00
Pricing changes are hard.
Fundamental shifts in go-to-market strategy tied to pricing? Monumentally difficult.
We recently dove deep into one such transformation with Barr Moses, CEO of Monte Carlo, during a Theory Ventures Office Hours. Monte Carlo, a data & AI observability pioneer, moved from traditional annual contracts to a daily revenue model.
These were the three most important takeaways for me from the conversation:
Monte Carlo customers were used to buying usage-based rather than contract-based & the alignment was an important & critical evolution. Ali Ghodsi said the annual contract is “selling like Oracle in the 1980s.”
The shift to daily revenue was a fundamental cultural change. First Barr asked every functional leader to pick who should own pricing. Ultimately, everybody but product decided it should be product. That settled, the go-to-market team “redefined the job functions for everyone in the go-to-market starting from scratch.”
Last, the company evolved through a cultural change, pursuing one key metric that everybody could align with : daily revenue. It was clear from our conversation that this is more than just a pivot. It’s a fundamental reimagining of the core operations of the company. The company embarked on an internal rebrand including “MCCC - The Monte Carlo Consumption Company.”
This involved “changing the language both inside of the company & with the board around one key metric.” As a VC, I felt 15 years of education about at ARR & ACV had been rightly thrown out the window. This daily figure brought “tremendous simplicity” & was crucial for optimizing for learning in the early days. It became, as Barr termed it, the “ultimate bet on yourself & your team.”
https://www.youtube.com/watch?v=list=PLWyXqNxVbHP2qY4BL40Kst473-kF3TBPN&index=16
This move has had profound effects on the company :
Monte Carlo’s journey is a powerful testament: pricing reflects your entire GTM philosophy & commitment to customer value. It’s a dare few take so comprehensively, but the rewards in alignment & customer-centricity are immense.
2025-05-29 08:00:00
NVIDIA announced earnings yesterday. In addition to continued exceptional growth, the most interesting observations revolve around a shift from simple one-shot AI to reasoning.
Reasoning improves accuracy for robots - like telling a person to stop and think about an answer before they reply. Here’s an example where I asked Gemini to create a financial projection for NVIDIA for the next five years.
Reasoning is compute-intensive, requires hundreds to thousands more – thousands of times more tokens per task than previous one-shot inference.
Software engineers also use reasoning extensively as AI coding agents examine code bases, plan modifications, and execute them. Each time I watch one of these reasoning traces I wonder how many GPUs are firing to produce the result.
OpenAI, Microsoft and Google are seeing a step-function leap in token generation. Microsoft processed over 100 trillion tokens in Q1, a fivefold increase on a year-over-year basis.
In addition to increased demand and greater usage, these reasoning models are driving significant volume increases in tokens as we saw in the Microsoft earnings announcement a few weeks ago.
On average, major hyperscalers are each deploying nearly 1,000 NVL72 racks or 72,000 Blackwell GPUs per week and are on track to further ramp output this quarter. Microsoft, for example, has already deployed tens of thousands of Blackwell GPUs and is expected to ramp to hundreds of thousands of GB200s with OpenAI as one of its key customers.
72,000 GPUs deployed per week is quite a statistic!
The pace and scale of AI factory deployments are accelerating with nearly 100 NVIDIA-powered AI factories in flight this quarter, a twofold increase year-over-year, with the average number of GPUs powering each factory also doubling in the same period.
To match the demand, hyperscalers are deploying more than $300b in capex this year to fund data centers, which interestingly, NVIDIA calls AI factories. What is the marketing rationale behind this framing? A new industrial revolution?
To date, the algorithmic improvements that reduce the overall model sizes are helping to staunch some of the geometric explosion in demand for AI, but it’s clear that both the demand for AI and more sophisticated reasoning are outpacing those advances.
2025-05-28 08:00:00
With Salesforce announcing its intent to acquire Informatica & Google’s announced acquisition of Wiz, 2025 is the best year in the last six in terms of M&A value.1
These massive acquisitions, totalling more than $32b propel 2025’s year-to-date number to decade highs - exceeding the fast-money pre-Covid days of 2020.
But the total number of acquisitions with disclosed deal values (typically a few hundred million or more) is also greater than any year in the decade. At this rate, the total number of M&A could be twice the average in the last five years.
The IPO market remains relatively quiet. However, the recent quarterly earnings reports from Cloudflare, Snowflake, Microsoft and others demonstrate strength in the enterprise software market. Snowflake raised guidance for product revenue & Microsoft did as much for next quarter.
Combined with some hope of clarity in trade policy, perhaps the second half the year will shower more liquidity on startupland - a welcome reprieve to the parched conditions of the past few years.
1 These figures include both announced and closed. Sometimes announced acquisitions take 12 to 18 months to pass US, UK, & EU regulatory scrutiny. And so this is a bit forward looking of an analysis.
2025-05-27 08:00:00
Which level do I want to use AI?
I find myself asking this question more & more frequently & I think the answer means at work I’ll be using many AIs - not just one or two.
| AI Level Use Case | Description |
|---|---|
| Chat-Based AI | Find the best Italian restaurant in the North Beach neighborhood of San Francisco. |
| In-App AI | Find a document or generate an overview paragraph within Notion. |
| Browser-Based AI | Deep research queries, such as estimating the market size of data center construction. |
| Computer-Based AI | Transcribe a video call and upload the notes to an investment memo. |
| Multiple AI Agents | Newer coding agents (e.g., Codex & Jules) work in parallel on the same codebase. |
Why are there so many levels? It depends on the context I want the AI to have.
An individual chat is a Google query. I have a very specific question I want to answer. No other details necessary.
On the other hand, the in-app AIs are context specific. For example, I find myself using Gemini to find questions within my Gmail - ferreting a particular clause from our employee handbook.
When working with deep research queries, I want a browser swarm to scour the internet, finding chestnuts within warrens on the web. “Run a market size calculation on data center construction in the US and estimate how much software spend will be applied there.” A deep research agent might process more than 150 websites.
Sometimes I want to operate across multiple applications in my computer, which is why I want the operating system AI. Take notes from this video call, summarize it, send it to the CRM, and then add my tasks to my task manager.
The last one, the multiple coding agents, they operate in parallel and can work together. So it’s like managing a team and I can accomplish much more than I could managing an individual coding agent.
Like working with a person, an AI succeeds with clear direction & the relevant related information to the task. At some point, one AI may be able to switch between these layers easily, but for now, as a user,
2025-05-23 08:00:00
Only five months in, 2025 has been the year of stablecoins. A Fireblocks survey of banks conducted in May underscores how quickly the market is moving.
Why are so many banks and payment processors aggressively moving to adopt stables?
The answer is the same as every platform shift promises, a greater revenue opportunity. Stablecoins allow international expansion without the hassle and the cost of setting up bank accounts in different geographies.
Lower costs, faster settlement times, and recent government regulation have all catalyzed adoption.
Banks and payment processors can leverage this technology to compete in new markets with a differentiated offering. They are also less expensive because they eliminate middlemen (correspondent banks).
This slice of the population surveyed is likely a bit skewed towards early adopters, but the trend at this point is inexorable. With $250b in stablecoins in circulation, I’m only $50b away from one of my 2025 predictions..
When the late majority adopts, we might see $1t of stablecoins & with it, an entirely new way of banking.
2025-05-22 08:00:00
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