I'm at SaaStr Right Now and This Article Already Exists

Recorded with Wispr Flow. Written with Claude. Published with Kite. All from my phone, at the conference, in under an hour.

I'm sitting at SaaStr Annual right now, fresh out of a session with Maia Josebachvili, Stripe's CRO of AI, and I'm doing something that would have felt impossible two years ago.

I recorded the session on my phone using Wispr Flow. I handed the transcript to Claude and asked it to help me write an article about it. I'm currently reviewing and editing this piece using the voice feature in Claude, giving feedback in real time, and by the time I leave this conference I'll publish it to my website using Kite, an AI website builder for autonomous websites. All from my phone. The whole thing will have taken under an hour.

I'm not telling you this to show off. I'm telling you this because it's the exact same thesis as the session I just left.

What Stripe's Data Is Telling Us About AI Growth

The numbers shared in this session were hard to wrap your head around.

Top AI companies grew 120% in 2025. In 2026, that number accelerated to 575%. Growth is not slowing down; it is speeding up. Lovable went from $100M to $400M in revenue in 8 months. Cursor hit a $1B run rate in under two years and kept climbing from there.

Consumer adoption of AI doubled in a single year, from under 6 million users to 14 million. And the people who are in are spending more. The average top AI buyer is now spending $371 per year on AI tools, up from $140 just a year ago. That is more than the average American spends on internet, streaming, and their phone bill combined.

(These figures come from Stripe's internal payments network data, shared by Maia at the session. They are not independently audited public benchmarks, but Stripe's data covers a significant share of global AI company transactions, so the directional picture is credible.)

The takeaway from Maia: people are not treating AI like a streaming subscription they might cancel. It has become a critical utility in how they live and work.

Four Forces Driving This Growth

1. Speed Has Fundamentally Changed

Building used to take months. A prototype required a team. Getting to a first paying customer took quarters.

Stripe's data shows builders are now going from idea to first paying customer in under 6 weeks. The monthly cohorts launching on platforms like Replit and Vercel keep compressing that timeline further.

As startup creation has surged, the share of technical founders has actually gone up. AI tools are not replacing technical skill; they are multiplying it. A technical founder can now ship in days what used to take a team months.

The implication for those of us in marketing at AI companies: build, sell, and iterate at the same time. The old linear playbook, where you build the product and then go sell it, does not work anymore.

2. Global Is the Baseline, Not the Bonus

AI products are inherently global. Gamma, the AI-powered slide and website builder, hit $100M in revenue in its first year, with the majority of that revenue coming from outside the US.

Across top AI companies, 48% of revenue now comes from outside the home market. Three years ago that number was 33%.

The practical implication: if a customer in Brazil cannot pay in reals with Pix, you are leaving money on the table. Stripe's data shows localized pricing drives 18% higher cross-border revenue, and adding just one local payment method creates a 7%+ revenue uplift.

3. Pricing Has to Match Elastic Value

SaaS pricing was simple: seats, flat tiers, same price for everyone. AI broke that model entirely.

Think about two users of the same AI tool. One is an engineer who sets up agents before bed and wakes up to reviewed code. The other is someone who replaced their browser search with ChatGPT. The value they get is completely different. The cost to serve them is completely different. The pricing has to reflect that.

The emerging model is subscriptions for predictability layered with usage-based credits for value capture. Two in three companies on the Forbes AI50 now have some form of usage-based pricing, up from less than 50% just last summer.

Getting it right comes down to three things:

• Price in your customer's language. Developers think in tokens. Enterprises think in seats. Match the metric to how your customers actually experience value.
• Show consumption before the bill arrives. Real-time usage visibility is not a nice-to-have; it is a core part of the product experience.
• Sell credits, not costs. Frame value, not spend.

4. Enterprise GTM Is a Year-One Problem Now

The old arc was: start product-led, prove product-market fit, layer in enterprise sales a few years later. That is how most SaaS companies did it.

AI companies are compressing that entire arc into months.

Cursor launched in 2023, self-serve. Within a year they had enterprise contracts and a sales-led motion that would have taken other companies a decade to build.

Every AI founder Maia is talking to is hiring a CRO now, in year one.

And then there is the question everyone is asking: how do you sell to agents?

Agent traffic to Stripe's APIs grew 10x in 2025. By the end of this year, agents will be reading more Stripe documentation than humans. They do not care about psychological pricing anchors. They do not respond to good-better-best tiers. Designing your product for agent buyers is not a future problem.

Why I Am Writing This From My Phone, Right Now, At The Conference

Everything Maia described in that session, the speed advantage, the collapsing timelines, the compression of what used to take years into months, I was living it while listening to her describe it.

I recorded the session on my phone. I handed it to Claude. I am editing this article right now using Claude's voice feature, talking through my feedback in real time. I even ran the draft through ChatGPT to fact-check the stats before publishing. And before I leave SaaStr today, I will publish it using Kite, an AI website builder that lets you manage and publish your website from anywhere, including from your phone at a conference.

Content marketing used to have its own version of this problem. You attended a conference, flew home, opened your laptop, wrote a draft, sent it to an editor, waited for revisions, and hoped the insight was still relevant by the time it went live. The whole process could take weeks.

That is gone now.

The same forces letting technical founders ship in days instead of months are letting marketers publish in hours instead of weeks. The tools are here. The question is whether you are moving fast enough to make the advantage matter.