The real tokenomics
SaaS companies aren't losing to AI. They're losing to a pricing model that doesn't have a word for what an agent costs.
When Intercom launched Fin in 2023, they priced it at $0.99 per resolved conversation. Their head of pricing explained why they didn’t use per-seat: “if Fin works as well as we know it does, over time, those 1,000 seats might become only 200.” Fin is on track to cross $100 million in revenue.
The seat wasn’t just a pricing unit. For most enterprise productivity software, it was the unit the product was built on.
One seat meant one person. The price was anchored to that person’s time. The product was designed for that person’s workflow. The moat was what that person depended on: features they used daily, processes they were embedded in, the cost of retraining a team if they switched. Revenue grew when headcount grew. The architecture of these products—pricing, design, defensibility, growth motion—assumed a human doing the work.
That assumption held for thirty years. Then the work no longer required a person.
Companies that had built their entire revenue motion on seat expansion now faced the same structural problem: the seat was both the pricing unit and the moat. When agents could do the work, the assumption behind both came apart at the same time.
Most companies replacing seat pricing have moved to hybrid or token models: usage-based billing that charges for inputs like tokens consumed, actions taken, API calls made. Closer to right. Two known failure modes.
The first is margin compression. Replit’s gross margins swung from positive 36% to negative 14% when its agent consumed more tokens than its pricing covered. The unit of billing looked right. The economics weren’t.
The second is customer avoidance. Users have been reported to actively avoid AI features even when free credits were included, because they’re afraid of getting locked into something unpredictable. Unpredictable bills train users to opt out. That’s the opposite of adoption.
The companies gaining ground aren’t pricing inputs. They’re pricing outcomes. Agents don’t take vacations. They don’t have seats.
AWS figured this out in 2006
Amazon S3 launched March 14, 2006. EC2 followed that August. Rent storage by the gigabyte, compute by the hour. No seat counts, no user licenses. AWS generated $108 billion in revenue in 2024.
SaaS made a reasonable adaptation: it priced by the human doing the work, not by consumption. That made sense when humans were the unit of work. It became a liability when they weren’t.
AWS priced by consumption because that’s what it sold: compute, storage. AWS’s moat wasn’t a set of features workers depended on. It was the infrastructure itself, and the pricing model that made the economics work. The two were inseparable. Now agents are doing the work.
In 2020, running the best available language model cost $60 per million tokens—GPT-3 Davinci at launch. GPT-4o today costs $2.50 per million input tokens: a 24-fold reduction in four years. The cost of inference is falling faster than compute costs fell in the first decade of cloud.
You can’t build a per-unit pricing model on a unit that’s expensive and unpredictable. AWS could price S3 at $0.15 per gigabyte in 2006 because storage costs were falling and the math was clear.
Intercom was first. Zendesk followed in August 2024: $1.50 per automated resolution for committed volume, $2.00 pay-as-you-go. CEO Tom Eggemeier called it an industry first: “customers only pay for problems that are resolved—not for interactions or failed attempts.”
Salesforce’s path was messier. Agentforce launched at $2 per conversation, moved to Flex Credits ($0.10 per action, up to 10,000 tokens each), and now runs three pricing models simultaneously. Credits, outcomes, seats. It looks like confusion. It’s a large company trying not to get caught flat-footed while its customer base is in three different places.
The valley
Goldman Sachs published a note in February 2026 on what’s happening to software multiples. Price-to-sales ratios fell from 9x to 6x. Forward P/E dropped from 35x to 20x, the lowest since 2014. Their analysts flagged specific concern about “products that function as lightweight user interfaces and where the business model is monetized predominantly through seats.”
Goldman Sachs is making a moat argument, not just a pricing one. The moat was the seat model itself: the dependencies, the workflows, the switching costs built around a human user. When the seat became optional, the moat didn’t just weaken. The note is a market-level judgment that the seat model is being repriced out of existence, at least for products where the workflow dependency was the main defense.
Seat revenue is declining before outcome-based and token-based revenue can replace it. Companies that spent fifteen years building their ARR motion around seat expansion are repricing into a model with its own failure modes, most of them still finding out which ones apply to them.
An a16z piece circulating this month frames two viable paths: accelerate growth by 10 points through AI-native products or cut to 40-50% operating margins. Both paths require abandoning the seat model.
The infrastructure has to catch up
Seat-based commerce was simple: monthly invoice, annual contract, net-30, billed to a legal entity.
Token-based commerce is different. Millions of transactions at sub-cent amounts. Agents billing other agents. No human in the loop.
Stripe saw this coming. In December 2025, they launched the Agentic Commerce Suite: usage-based billing at 100,000 events per second, with over 700 agent startups on the platform. They published a case study on Intercom’s pricing transition specifically. They know where the volume is going.
x402 is the more interesting structural question. Coinbase launched it in May 2025: a protocol that repurposes the dormant HTTP 402 “Payment Required” status code for stablecoin micropayments inside HTTP request/response cycles. Cloudflare, Google, and Vercel have announced support. The x402 Foundation has processed over 100 million payments.
The catch: x402 settles in USDC. USDC is issued by Circle. Circle can freeze accounts. The rails are open; the money isn’t. Whether that matters depends on what you think the point of programmable money is.
Lightning Network has been doing sub-second, permissionless micropayments since 2018. The reason it hasn’t become the default agent payment rail isn’t technical. The companies building agent infrastructure are mostly not Bitcoiners.
Both protocols price the transaction. That’s the right instinct. What neither addresses is what the transaction should represent.
Token pricing has an alignment property that seat pricing never did. Per-seat, the vendor gets paid whether the software does anything or not; the contract is with the employee headcount, not the work. Token-based pricing prices activity, not results. That’s why the outcome-based layer—$0.99 per resolved conversation, $1.50 per automated resolution—is emerging on top of token consumption rather than replacing it. The unit is closer to right. It still isn’t right.
Tokens tied to something
“Tokenomics” was created by the crypto industry. Elaborate scaffolding to make speculative assets look like economics. The tokens weren’t tied to anything—print more, manipulate supply, and the price is whatever the market will believe, until it believes nothing.
AI tokens are tied to work done. The cost falls predictably. Per task, per resolution. The pricing model emerging around them is anchored to something seat pricing never was: the work itself.
The seat priced the human. The token prices the input. What the industry is still working out is how to price the output—the work, the resolution, the thing that actually happened.
That’s the real tokenomics question. Not what inputs cost. What the work is worth. And what unit captures it.
The companies that have moved—Intercom, Zendesk, Salesforce—are rebuilding across the stack: pricing model, moat logic, revenue motion, and payment infrastructure. The ones that haven’t are watching their multiples compress.