marvinvista.com

The unit economics of SaaS create a predictable organizational structure: ARR compounds through new bookings and net retention, so companies optimize ruthlessly around those metrics. This means the roadmap becomes a sales tool—every feature maps to a deal or a churn save. The best orgs maintain product integrity while doing this. The median org ships the top 10 feature requests from their biggest customers and calls it a strategy. From Ben Thompson's excellent interview with Bret Taylor:

Broadly speaking, sales and marketing matter a lot in enterprise software companies and tend to be the gravitational center... Because at the end of the day, annual recurring revenue is an annuity, it throws off cash every year, you basically need to add to that annuity by signing new recurring revenue contracts or adding to them, and you subtract attrition. That's just how software as a service works.

With that, when you stop growing, the business model breaks down. That's where things like the Rule of 40 come from, where your growth rate and your EBITDA margin sort of need to be in lockstep. You can grow very profitable with that business, when you stop growing, no one wants to work at one of those companies.

As a consequence, you end up with a really customer-centric, go-to-market-centric orientation, and product serves that. The best enterprise software companies, that voice of the customer dictates their product roadmap, and they can really meet that demand.

The worst ones stop innovating on the product and hold your feet over the fire in the sales process. Depending your level of cynicism, all enterprise software companies have been guilty of each at different points.

Another interesting thing about this is how the business model shapes culture. When you're growing 80%, product can lead and sales follows. When you're at 15%, sales leads and product follows. It's hard—the companies that stay interesting are the ones that maintain product velocity even as they scale GTM.

Three major AI video launches in three weeks, and each company picked a radically different target audience:

...compelling AI video generation is very much here, and it's widespread: over the last three weeks we have actual AI video products from Google, Meta, and now OpenAI.

...it feels like each company has an entirely different target audience: YouTube is making tools for creators, Meta is building the ultimate lean back dream-like experience, and OpenAI is making an app that is, in my estimation, the easiest for normal people to use.

In this new competition, I prefer the Meta experience, by a significant margin, and the reason why goes back to one of the oldest axioms in technology: the 90/9/1 rule.

• 90% of users consume
• 9% of users edit/distribute
• 1% of users create

If you were to categorize the target market of these three AI video entrants, you might say that YouTube is focused on the 1% of creators; OpenAI is focused on the 9% of editors/distributors; Meta is focused on the 90% of users who consume.

...it's striking how this target market evaluation tracks with the companies themselves: YouTube has always prioritized creators, while OpenAI's business model is predicated on people actively using AI; it's Meta that has stayed focused on the silent majority that simply consumes...

OpenAI partners with Stripe to launch instant checkout within ChatGPT.

It has been clear for a long time now that ChatGPT is a powerful tool for commerce, and the company has been taking baby steps to close the loop, with things like product carousels, an in-app browser (which lets OpenAI capture web usage and attribution), and the overall concept of having a router deciding how to answer every query. This, however, is an explicit step into the transaction itself — and it's almost certainly not the last.

…Meta can still build a good product. Vibes may not end up being for everyone — I hesitate to generalize too broadly from what I like, given my rather esoteric media consumption habits — but I think it is executed very well, with a strong and unique point of view. That sort of product sensibility is something that has generally been missing in AI.

..Meta has the courage and conviction to lean into what AI can uniquely do. There are a lot of things to wring one's hands about when it comes to Vibes, but Meta is leaving that hand-wringing to the peanut gallery, and actually shipping. There's something to be said for that.

“Scaling laws are real, ofc. ++ model power keeps raising the ceiling. But capability ≠ adoption. The moat in workflow AI is bottling institutional context and making it useful in the moment of work.”

…

“Pretrained models are powerful, but not turnkey. Extracting Quinn’s win rate, UCSF’s pathways, or Zappos’s ethos is closer to RL than pretraining. You shape behavior with constraints and feedback until it fits the environment. Workflow products do that shaping.”

…

“The Bitter Lesson is obvi right: scaling keeps raising the baseline. But raw capability isn’t adoption, because no user wants to give superintelligence full context all the time. App winners are the ones who bottle context into systems trusted at the point of action.”

Our whole thesis is that for every company in the world, their AI agent will be their most important digital interface they have, as important as their website or their mobile app. Just like mobile apps didn’t replace websites, and it also differs but not every retailer even has a mobile app. Some do, some don’t. You’ve covered this a lot, just like do you have the brand equity to warrant that?

I think across the board, your AI agent, whether it’s published in your mobile app or available via WhatsApp, or available over CarPlay or whatever it might be, that will be the primary digital interface to your company. We’re really focused on customer experience more broadly. What’s exciting for us, if we do our job, we’re the most important AI agent you make, we’re the one that actually powers your customer experience.

“Upon opening a project or folder, you’re likely to jump into editing files. This means Cursor needs to generate autocomplete suggestions, which the Cursor team calls tab suggestions. A low-latency sync engine powers the ‘tab model’. This generates suggestions that are greyed out and can be accepted by hitting the ‘Tab’ key. The suggestions need to be generated quickly in ideally less than a second. This ‘tab model’ must be as fast as possible, and data transfer as low as possible. There’s always a tradeoff between how much context to send, and the quality of the suggestions: the more relevant context Cursor can send, the better the suggestions. However, sending lots of context can slow down the display of suggestions, so getting this right is one challenge for Cursor’s engineers.”

I had this goal, which was we needed to somehow create a more imaginative world. I mean, one of the biggest risks in the world I think is a collapse in belief, a belief in ourselves, a belief in the future. And part of that I think comes from a lack of imagination, a lack of imagination of what we can be, lack of imagination of what the future can be. And so this imagination thing I think is an important pillar of something that we need in the world. And I was thinking about this and I saw this, I'm like, "I can turn this into a force that can expand the imagination of the human species." It was what we put on our company thing now. And that felt realistic. So that was really exciting.