The End of Seat-Based SaaS?

For years, the SaaS playbook was straightforward: charge per user (per “seat”) on the system. This made sense when one human user roughly equated to one unit of value from the software. However, generative AI – and more specifically agentic AI (AI that can take autonomous actions to accomplish goals) – is turning that model on its head. As Box CEO Aaron Levie argues, AI agents break the link between users and value: one person with an AI “co-pilot” can now accomplish the work of many, and agents can even operate in the background without direct human input . In other words, an enterprise is no longer limited by how many employees are using a tool; AI-driven workflows have “no upper limit” on deployment. We’re already seeing AI agents perform tasks in coding, research, and legal work that are billed at multiples of the old seat-based pricing for equivalent software. I share Levie’s view that this is likely the biggest shift in enterprise software business models we’ve ever seen – a shift from per-user licensing toward pricing that reflects work done and outcomes delivered. Even leading economists are signaling how profound this change could be. Erik Brynjolfsson describes AI as “the biggest transformation wave the economy has ever seen”. He notes that tech executives must be ready to adapt their business models for a world where every customer and supplier employs AI – especially as autonomous agents enable a new “service-as-software” paradigm in SaaS . In practical terms, if a software agent can perform tasks 24/7 at a cost far lower than a human salary, how do vendors capture that value? It likely won’t be through the old per-seat licenses. As one industry observer put it bluntly, “It’s no longer ‘How many seats?’ It’s ‘How many tasks can AI handle, and at what cost?’”. The traditional model of selling seats is starting to look obsolete when the “user” might be an AI or a single human overseeing dozens of AI-driven processes.

New Models: From Seats to Outputs

If seat-based subscriptions don’t fit an AI-first world, what will? Many, myself included, believe the future lies in usage-based and outcome-based pricing. Aaron Levie himself has sketched out a few possibilities for agentic AI pricing:

• Labor- or Task-Based Pricing: Charge for the amount of work the AI agent performs, analogous to paying an hourly rate or per unit of output. “An AI agent performs a certain amount of work, and you pay for [the] time or units it took to do that work,” Levie explains. This essentially treats AI as a workforce: if an agent does the work of a data analyst for 10 hours, you pay a fee comparable to 10 hours of that analyst’s time (perhaps at a discount since it’s software). This aligns price with workload accomplished – a fair trade in theory for both customer and provider.
• Outcome-Based Pricing: Charge based on successful completion of tasks or the results achieved. In this model, the customer pays for a defined outcome – say, an AI agent resolved 100 customer support tickets or processed 1,000 invoices – rather than for usage per se. The appeal here is a direct link between what the customer needed done and what they pay. As Levie notes, this makes the value obvious: you pay when the AI actually accomplishes something for you. It’s essentially “pay-per-result,” which can be very attractive if those outcomes drive business value. (Notably, some early SaaS AI offerings resemble this: e.g. charging a few dollars per conversation an AI agent handles, rather than a flat fee.) One bonus of outcome pricing is that if the underlying AI gets cheaper to run over time, the vendor’s margins improve while the price per outcome could stay the same. In other words, efficiency gains go to the provider once pricing is pegged to outcomes delivered, not compute used.
• Cost-Plus (Consumption) Pricing: Charge based on the AI’s underlying compute costs, with a markup. For instance, pricing by the number of API calls, tokens processed, or CPU hours consumed by the AI agent. This is transparent – the customer pays roughly for resources used – and it can feel logical to more technical buyers who understand infrastructure costs. However, simply passing along AI compute costs with a small margin may not sustain high profitability long-term. It also doesn’t directly tie to business value – it’s more like utility pricing. Still, many generative AI services today use a form of consumption pricing (e.g. OpenAI charging per 1,000 tokens), so this model is already common at the API level.
• Unlimited Subscription (AI-as-a-Service Seat): Continue with a flat per-user (or per-“agent”) fee that grants unlimited use of the AI. Levie describes offering “AI Agents with unlimited work tied to a seat” - basically an all-you-can-use plan for each licensed user or agent. This might be simpler and familiar to buyers (just like traditional SaaS subscriptions). It could work well in scenarios where you have many end-users each benefiting from AI assistance (e.g. enhancing a whole sales team with AI). But it breaks down if only a few people use the AI to generate outsized output – you’d be “giving up too much value” in low-seat, high-usage cases. In my opinion, this model might survive in hybrid form (e.g. a base seat fee plus overage charges), but by itself it doesn’t fully capture the value an unleashed AI can create.

These models aren’t mutually exclusive, and we’ll likely see creative hybrids. For example, a vendor might charge a base platform fee (for predictability) and then add usage tiers or outcome-based add-ons. The key point is that pricing will gravitate toward reflecting the AI’s contributions – the work done, tasks completed, or decisions made – rather than just the number of people using the app. As a VC at SignalFire quipped, “With no users left to charge, the future is outcome-based pricing—charging for results.”. While that’s a bit tongue-in-cheek, it captures the sentiment that value-based monetization is the endgame. If AI agents deliver 10x more output, software providers will seek to monetize a portion of that added value, not just stick to the old per-seat fee.

The Conundrum: Value vs. Predictability

Shifting to usage- or outcome-based models isn’t straightforward – it creates a real conundrum for businesses. On one hand, customers love the idea of “pay only for what you use or what you get”; on the other, both buyers and sellers worry about how to measure and manage this new model. Several challenges loom:

• Defining Outcomes and Units: What counts as a “successful outcome” for AI? In some domains it’s clear (e.g. a customer service query resolved by an AI agent), but in others it’s fuzzy. An outcome-based deal could invite disputes – was the result truly achieved to the client’s satisfaction? – especially if AI’s work quality varie. Likewise, if pricing by tasks or time, how do we count and normalize those units across different contexts? The industry is still figuring out the right unit of measurement for AI work. Business leaders like Levie openly ask: should we charge per action? per outcome? or something entirely new?  The winners of this transition will be those who invent intuitive metrics that customers accept as fair value for AI-driven results.
• Customer Budgeting and Predictability: Traditional SaaS subscriptions gave enterprise buyers cost certainty – a fixed annual license count. Usage-based pricing can introduce volatility. CIOs and CFOs worry about getting a surprise bill when AI usage spikes. “People get annual budgets and cannot tolerate variability,” notes one CEO, cautioning that a sudden jump in AI agent activity could burn through budget in weeks . Early adopters of cloud consumption models learned this the hard way, and AI could repeat it. In fact, IDC’s research finds that many enterprises “look for predictability” and thus favor subscriptions or at least tiered plans that cap the risk of runaway costs. Vendors will need to balance usage-based fairness with safeguards (e.g. usage caps, enterprise volume discounts, or hybrid plans) to make pricing predictable and aligned with value.
• Scaling Costs and Margins: For SaaS companies, AI features can be expensive to run – think of all those GPU-heavy computations. If you stick to flat per-seat pricing while users offload tons of work to AI, your cloud costs could skyrocket and erode margins (the “AI margin trap”). On the flip side, if you charge purely by consumption, you risk commoditizing your offering or scaring off customers with volatile bills. Some experts suggest hybrid models will emerge: combining cost-based transparency (so customers see the resource costs) with performance-based fees or outcome bonuses. The goal is to ensure the provider is rewarded when the AI delivers big wins for the client, without making costs too opaque. Transparency will be key – as Levie emphasizes, vendors that offer clear, predictable pricing (and demonstrable outcomes) will have the edge. No one wants to see a mysterious 10x jump in their invoice and not know why.
• New Usage Patterns: Agentic AI also changes who or what is using the software. We might have scenarios where an AI agent itself is the primary user of a SaaS API (with minimal human involvement), essentially a software-to-software interaction. SaaS providers may end up selling more to algorithms than to people! This raises questions: do you count an AI agent as a “seat”? (Probably not meaningful.) Do you license an enterprise for a certain number of concurrent AI agents or workflows? Or do you abstract away the user count entirely and just charge for outcomes? These are uncharted waters. Levie points out that technically there’s “little difference between having 100 agents complete one action a minute vs 1 agent completing 100 actions a minute” – so simply charging per agent process might be moot. The value is in the volume and quality of work done, not the number of bots or users in the system.

Despite these challenges, I’m optimistic. If done thoughtfully, moving beyond seat-based pricing can be a win–win. Customers would pay in proportion to the value they receive – measured in real work completed – which builds trust. Vendors, in turn, unlock a vastly bigger market (no more TAM tied strictly to human headcount  ) and can monetize the true productivity boost AI provides. One LinkedIn commenter noted that smaller companies might actually be willing to pay more than before if AI helps them automate aggressively – because the ROI is clear and they’re no longer constrained by needing more staff to scale. In other words, a business with 50 employees could pay like one with 500 seats, if AI agents are doing the work of those extra 450 – and both sides come out ahead in that deal. That dynamic is brand-new to SaaS economics.

Embracing an AI-Native Business Model

To succeed in this new era, software providers need to rethink not just pricing, but their whole value proposition. Many SaaS apps today are essentially forms and workflows around a database (classic create-read-update-delete operations). With AI agents in the mix, the product must evolve: users will expect to delegate more drudgery to the AI and focus on higher-level guidance. As Wharton professor Ethan Mollick suggests, we should view generative AI not as “just software” but as “pretty good people” – essentially eager interns that can take on tasks under human supervision. In practice, that means your software is no longer just a tool, it’s a team member. When every knowledge worker has an AI assistant (or ten) working alongside them , the metric of success shifts from user activity to productivity outcomes. SaaS companies must align their revenue model to how much work their AI features get done for the customer – how much faster, cheaper, or better the customer’s operations run thanks to the AI. If you can demonstrably save a client $1M in support costs by handling inquiries with AI, you could price based on a share of that success, not per agent user. This is a fundamental change from selling software licenses to selling business results. It’s challenging, yes, but also incredibly exciting. In closing, I believe the advent of agentic AI is forcing a healthy re-examination of the SaaS business model. Monetization will need to track more closely to value creation. Those clinging to purely per-seat models may find themselves undercut by upstarts charging per task or outcome (and delivering clear ROI). On the other hand, those who jump headlong into consumption pricing must ensure they maintain transparency and trust, and avoid simply shifting unpredictability onto customers. The likely reality is a mix of models: subscriptions that give enterprises baseline predictability, combined with usage/outcome components that scale with the work the AI actually does. We’re in uncharted territory, and as Levie notes, these are “fairly exciting times to watch new business models in software emerge”. There will be experimentation and even missteps, but one thing is certain: SaaS can’t remain business-as-usual in the age of AI agents. The companies that thrive will be those that price and deliver their services in a way that genuinely reflects the work accomplished and value achieved by AI – however we ultimately measure it. After all, when AI is “triggering the biggest transformation wave” in our economy, the business of software has to transform along with it. It’s time to move beyond seats and start charging for the ride.