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For the last decade, being a SaaS founder was the ultimate arbitrage. You cooked the meal once and served it a billion times. The marginal cost for the ten-thousandth user was effectively zero. It was the economic equivalent of Jesus feeding the 5,000 with a couple of loaves and fishes.
This economic miracle is why the market awarded these companies 20x and 30x revenue multiples. You weren’t just buying growth. You were buying the Gold Standard business model: subscription-based software.
SaaS was the ultimate compounder. It offered predictable, recurring, and gravity-defying economies of scale. Analysts modeled cash flows 20 years out, assuming that 95% GRR and 110% NRR were laws of physics. These companies weren’t treated like risky equities; they were treated like government bonds, where revenue retention was considered as guaranteed as the US Dollar itself.
The model was so bulletproof that even Private Equity, notorious for demanding durability, felt comfortable piling massive leverage onto these businesses. The subscription revenue was supposed to be a sure thing.
But the menu has changed. We are witnessing the McDonaldization of software. This is a fundamental shift from high-margin luxury to a high-volume, thin-margin, variable-cost utility.
This forces a pricing model shift from predictable seat-based subscriptions to metered, usage-based billing. That move protects gross margin, but it destroys the thing SaaS sold investors: recurring, forecastable revenue. Consumption behaves like a utility. It fluctuates with budgets, workloads, and throttling. The result is worse quality of earnings, with less visibility, more volatility, and lower valuation multiples.
Here is why the quality of earnings in SaaS has permanently degraded.
# The Death of the Asset-Light Model
Traditional SaaS enjoyed upwards of \~95% gross margins because software scaled sub-linearly. But Generative AI has reintroduced Cost of Goods Sold to a sector that had forgotten they existed.
In the pre-AI era, the only friction on a software P&L was the AWS Tax. These were the basic hosting fees required to keep the lights on. Crucially, these were step-function costs rather than linear ones. You paid up front to spin up the infrastructure, but the marginal cost of adding the 10,000th user was effectively zero.
Because of this, gross margins of 95% weren’t an outlier. They were the baseline. The revenue scaled, but the costs stayed flat.
Fast forward to present day and take a look at Duolingo. In late 2025, despite crushing revenue estimates with 41% YoY growth, their gross margins compressed to 72.5%. This was down from nearly 74% in previous years. Why? Because every time a user asks for a grammar breakdown, tokens are burned. Every time they video-chat with Lily, a GPU hums. It consumes real power to manufacture the tokens required for that single interaction.
Now scale that to millions of users spending tens of millions of minutes on Duolingo every day. That is billions of tokens. These costs bleed directly into the P&L. In their Q3 2025 letter, management explicitly cited increases in generative AI and hosting costs as the driver for this compression.
Gross margins are supposed to rise with scale. Every incremental dollar of revenue should be accretive, driving margins higher. But with Duolingo, we are seeing the exact opposite.
Here is the terrifying question for the rest of the industry: If a hyper-growth compounder growing at 40% annually cannot outrun the inflation of AI inference costs, who can?
# The First Principles of Valuation Gravity
So you don’t believe the narrative? Then look at the math.
Businesses are valued on the sum of the free cash flow they produce over time, discounted back to the present day. Over the long term, stock prices track free cash flow per share. They do not track revenue.
So how do we estimate the free cash flow “ceiling” for a business? The answer is gross margins. Revenue minus the cost of goods sold equals the gross margin. This is the gate that determines what could actually drop down to free cash flow.
* **At 95% margins**, you have massive leverage. Every new dollar is 95 cents of dry powder. Once fixed costs are covered, practically every incremental dollar is pure profit.
* **At 60%**, that leverage breaks. You need 58% more revenue just to generate the same gross profit dollars as your high-margin peer.
The compression isn’t linear; it’s geometric. If gross margins fall from 95% to 60%, your free cash flow margin likely collapses from 45% to 10%. That isn’t a 35% valuation haircut. That is a **78% wipeout** of the business’s value.
# The Efficiency Trap (Jevons Paradox)
The most common rebuttal to this thesis is that token costs are collapsing. In theory, as tokens get cheaper, margins should snap back to 95%, right?
That sounds reasonable until you remember Jevons Paradox. When something gets cheaper, we don’t conserve it. We consume more of it. Compute is no different.
Yes, the cost per token is falling. That fact is real. It is also meaningless on its own, because the bill is not set by price alone. The only equation that matters is: **Compute spend = (price per token) × (tokens per task) × (tasks per user) × (active users)**
If price falls 10x but tokens per user rises 10x, nothing gets cheaper. In practice, costs rise because competition forces you to reinvest every efficiency gain into a better product, not a cheaper one. As soon as tokens get cheaper, teams expand the product. They shove more context into the prompt, run more reasoning in the background, and add more verification steps.
Token consumption climbs in three phases:
1. **Standard retrieval:** The basic chatbot era. Short prompt, short answer.
2. **Reasoning models:** The interface shows one paragraph, but the model outputs 8x more tokens in the background to "think."
3. **Always-on agents:** A chatbot is a transaction; an agent is a machine left running. Loops are token-hungry by design.
There is no AI Lite equilibrium. You match the experience of your competitors or you get labeled obsolete. Token prices are falling; token requirements are rising faster.
# The Edge Illusion
The edge thesis is the comforting conclusion everyone wants: eventually, inference runs locally and the Tax disappears. The problem is that "good enough" is decided by your competitor, not your CFO.
Offloading compute does not restore 95% SaaS economics because gross margin was never the whole story. The old SaaS miracle was centralization. The moment inference becomes something the customer must provision and power, the vendor loses pricing power. If the customer is paying the electric bill for the GPUs, they will not pay you a premium subscription for the intelligence.
The only way edge solves the cost problem is if companies freeze capability to protect margin. History is clear: Carnegie did not win steel by limiting investment. Kodak, Blockbuster, and BlackBerry failed because they treated margin preservation as a strategy while a competitor treated product improvement as leverage.
# The Collapse of Revenue Predictability
Historically, SaaS was valued on predictability. Today, that logic breaks. If you stay on a flat-rate subscription, your power users are your biggest financial liability.
So you pivot to usage-based pricing. You move from a contract to a meter. Seat-based subscriptions behave like annuities; usage revenue behaves like a utility. It moves with workloads and budget scrutiny. Revenue can fall without a single logo leaving. That shift collapses visibility, and when visibility collapses, the premium multiple disappears.
# The Macrohard Threat
Incumbent SaaS companies carry a heavy cost stack: sales, support, HR, and management. Now add inference.
xAI is aiming at this with "Macrohard"—the digital emulation of entire companies. If a new entrant can deliver comparable outcomes with far fewer people, its cost base is structurally lower. They can price below incumbents and still earn margins. Customers stop paying for overhead; they pay for results.
# So Who Survives?
The durable survivors cluster where verification is not the whole story. They sit at the intersection of accountability, rights, and trust:
* **Regulation and Systems of Record:** Where "mostly right" is unusable. Audit trails, legal defensibility, and liability cannot be outsourced to a probabilistic model.
* **Identity, Security, and Governance:** As autonomous systems scale, the control layers (permissions, logging, provenance) become more critical.
* **Financial Rails and Risk:** Infrastructure that enforces rules and tracks real-world exposure remains essential even as workflows automate.
# The Private Equity “Agent LBO”
The biggest winners may be the buyers, not the builders. When multiples compress, software starts trading like an operating asset.
Bending Spoons is already running this playbook: Buy a stalled asset, strip out the legacy cost stack, replace internal work with automation, and run it for cash. In a world where the marginal cost of "doing work on a computer" falls, the old SaaS org chart is the liability.
# The Great Repricing
The software isn’t going away. But the financial asset class we called SaaS is dead. AI has stripped away the scarcity that protected software margins. The margin that used to belong to the software founder now belongs to the energy provider, the data center REIT, and the chip manufacturer.
The magic of SaaS has been replaced by the physics of manufacturing. You are no longer running a high-leverage software monopoly; you are running a digital factory.
**And factories do not trade at 30x revenue.**