To The Moon

As always, these posts are more of a brain dump of “what I’m thinking” about…And lately I have been thinking a lot about “legacy SaaS",” systems of record, etc. I wanted to write another post today in a similar vein.When I think about how people and companies interact with software today (and for this post when I say software I’m talking about legacy SaaS systems of record) the pattern is generally pretty simple. The system of record is a single, organized place where a human goes to look something up, understand the state of the world, and then take some sort of action based on the information they gathered from the system of record. Like opening Salesforce to check pipeline before updating a forecast, or pulling up NetSuite to reconcile numbers before approving a close. And more often th

I have been thinking a lot lately about education in AI. More often than not, I am seeing education as a core go to market problem (but sometimes an advantage) in AI. The companies that are best able to educate the world and their prospects are winning. Part of the reason for this is that everything in enterprise AI is still so new. Many people, teams, and organizations are facing what I think of as a blank canvas problem. They know AI is powerful, but they don’t even know where to start. Where to use it, where to augment existing workflows, where to create net new workflows, etc.What surprises me the most - even when teams think they know what functionality they want build, it’s not obvious to them how to build it. Build versus buy is just where it starts. Even inside “build,” there are d

Every AI conversation eventually drifts toward the same set of questions. Which model should we use. Build or buy. How much will inference cost. What happens to accuracy at scale. These are all real questions, but they are also strangely orthogonal to whether AI actually changes anything meaningful inside a company. You can answer every one of them well and still end up with an AI strategy that feels impressive in demos and immaterial in practice.There is a more important decision sitting underneath all of this, and most companies are not talking about it directly. They are making it implicitly, often without realizing it. That decision is whether AI is allowed to be authoritative or merely assistive.Most enterprise AI today is firmly in the assistive bucket. AI drafts the email, but a hum

Every few weeks I see some version of the same claim: “The new system of record is the agent,” or “Workflows swallow systems of record,” or “Data is the system of record, apps are just thin views.” There is a grain of truth in each of those, but I think it is also easy to over-rotate and accidentally throw out the thing enterprises still need most, which is not a “system of record” as much as a reliable source of truth.When I think about systems of record, I do not actually think in product categories. I think in the much more boring lens of “where does the truth live.” In other words, if an enterprise workflow needs to know something at a specific step, where is the one place that answer is considered canonical. Because as workflows get more automated and more agent driven, the fragility

I want to write this week about a topic that’s been bugging me recently. It’s an irrational (in my opinion…) behavior that’s starting to emerge in earlier stage startup land more and more. The challenge is - I can see how we are getting to an irrational end state with a rational starting point… And it all comes down to how employees view and value startup equity. So this post is meant for all of the employees evaluating different offers from startups. Let me set the stage with a hypothetical conversationFounder: “I need to raise at the highest valuation possible. And after I do, I need to raise again in 6 months at an even higher valuation.”Me: “Why?”Founder: “Because that’s how I will attract and hire the best talent”Me: “Why? Wouldn’t new hires want a lower valuation for more upside?”Fou

Over the last year we have all been trained (at least I have…) to look at model launches through one lens. What is the benchmark score and who sits at the top of the leaderboard. It felt like every product announcement was immediately reduced to a scatter plot and a few social media hot takes about who beat whom by a few points on MMLU or GPQA. But something subtle started to shift with the Gemini 2.0 family and became much clearer with this week’s Gemini 3.0 launch. The story is no longer only about raw model quality. The frontier is getting crowded, the performance gaps are narrowing, and the real competition is moving up the stack into platforms. Said another way, the next decade in AI will be defined not only by model breakthroughs, but also by distribution, integrations, and the shape

The AI FactoryIf I zoom out for a moment and look at the current trajectory of AI infrastructure, it’s hard not to see an entirely new pattern forming. Inference keeps getting faster. Inference engines keep getting smarter. And the ecosystem around them keeps getting more modular and open. What once felt like specialized machinery locked inside a handful of labs is now drifting into the hands of every company with a GPU budget and a few strong engineers.Neoclouds like $CoreWeave, Inc.(CRWV)$ and $TOGETHER PHARMA LTD.(TGPHF)$ have rewritten the economics of GPU access. Inference clouds like Fireworks, Baseten and fal have done the same for reliable serving (and we’ve already separated into separate infere

If I had to simplify software market cycles, I’d say they come in two phases: the expansionary phase and the consolidation phase.In the expansionary phase, buyers scoop up software almost indiscriminately. There’s little concern for cost or efficiency, what matters is speed. It’s about accelerating product development, capturing market share, or outspending competitors to stay ahead, all under the assumption that growth will take care of everything else. During this phase, public markets shift their focus entirely to growth over profits. Take a look at the multiples chart I post later on breaking out multiples by high, medium, and low-growth companies. You can see the high-growth bucket has seen multiple expansion this year, while the mid-growth bucket has seen steady contraction.In the co

This week the 3 hyperscalers reported ( $Amazon.com(AMZN)$ AWS, $Microsoft(MSFT)$ Azure and $Alphabet(GOOG)$ $Alphabet(GOOGL)$ Google Cloud). What did we learn? Most importantly - they ALL called out still being meaningfully capacity constrained. CapEx guides are going up, data center builds are going up, power constraints are meaningful. This isn’t the telecom bust where the world laid fiber that was “dark” (ie unused). GPUs are being used the second the come online…Here are the numbers:AWS (Amazon): $132B run rate growing 20% YoY (last Q grew 17%)Azure (Microsoft): ~$93B run rate (estimate) growing 39% YoY (last Q gre

Cloud Giants Update:AWS ( $Amazon.com(AMZN)$ ): $132B run rate growing 20% YoY (last Q grew 17%)Azure ( $Microsoft(MSFT)$ ): ~$93B run rate (estimate) growing 39% YoY (last Q grew 39%)Google Cloud ( $Alphabet(GOOG)$ $Alphabet(GOOGL)$ includes GSuite): $61B run rate growing 34% YoY (last Q grew 32%, neither are cc)ImageFor SG users only, a tool to boost your purchasing power and trading ideas with a Cash Boost Account!Welcome to open a CBA today and enjoy access to a trading limit of up to SGD 20,000 with upcoming 0-commission, unlimited trading on SG, HK, and US stocks, as well as ETFs. Find out more here.Other helpful