The first software lesson I learned without a computer
Long before I worked with enterprise technology, I learned a version of the build-versus-buy problem as a child.
I grew up in a family with very limited resources. We had what we needed, but not much room for non-essential things. One day, a cousin whose family could afford more invited me to his home to play Monopoly. For me, it was a shock. The game felt sophisticated, strategic, almost like a small operating system for money, ownership, negotiation, and chance.
When I came home, I asked my parents if we could buy one. They told me, gently, that it was too expensive.
So I built my own.
With paper, glue, improvised pieces, and a lot of imagination, I created a handmade version of Monopoly. But I did not only copy it. I changed it. I added rules. I adapted the board. I made it ours. My younger brother and I played it for hours, and he still remembers how much fun we had with that handcrafted game.
I did not know it at the time, but that experience taught me something that is now becoming central to enterprise software: when access is constrained, people look for ways to build. And when the tools for building become radically cheaper, the boundary between buying and creating starts to move.
That is what AI is doing to software now. It is not making ambition smaller. It is making execution more accessible. Intelligence is becoming more abundant, and the scale of what a person or a small team can create is expanding dramatically.
This is the personal reason the “death of SaaS” question matters to me. The story is not really about SaaS disappearing. It is about what happens when capability stops being locked behind expensive access and starts becoming something more people can shape for themselves.
Why this matters now
At enterprise scale, the same pattern has shaped software for two decades. If the business needed a capability, it bought a subscription. Building internally was slow, expensive, risky, and politically difficult. SaaS won because it turned software into an operating expense and made capability feel instantly available.
That logic is now being repriced.
The argument is not that all SaaS disappears. That would be intellectually lazy. Systems of record, regulated platforms, deep collaboration networks, and high-trust infrastructure will remain. The narrower and more important argument is that licence-based gatekeeping is losing its economic privilege. Paying per seat for generic capability is becoming harder to defend when half the seats are unused, vendor prices keep rising, and AI-assisted teams can reproduce narrow internal workflows in days.
The attached research essay makes the full case. This article is the executive version: what changed, where the pressure lands, where SaaS survives, and what leaders should do before the next renewal cycle.
The SaaS waste problem
The first problem is not AI. It is waste.
Zylo's 2025 SaaS Management Index records average annual SaaS spend of $4,830 per employee, up 21.9 percent after several years of restraint. Productiv estimates that roughly 53 percent of provisioned SaaS licences are unused or underutilized. Zylo's earlier benchmark put average annual licence waste at approximately $18 million per enterprise, while the average enterprise portfolio contains roughly 275 distinct SaaS applications.
This is not a healthy market operating at the margin. It is a market where buyers struggle to see what they own, sellers can bundle and reprice, and the renewal habit does more work than the business case.
The SaaS model was rational when buying was cheaper than building. But a subscription is not intrinsically efficient. It is efficient only when the rented capability is cheaper, safer, faster, and better than the alternative. AI changes the alternative.
The build economics have inverted
The classical build-versus-buy argument assumed that software engineering capacity was scarce. A single engineer-year could cost hundreds of thousands of dollars. A SaaS tool spread product development across thousands of customers. The arithmetic favored buying.
AI-assisted development breaks that arithmetic in specific categories.
The strongest evidence is not the hype around coding assistants. It is the behavioural evidence from enterprises and operators. Retool's 2026 build-versus-buy survey reported that 35 percent of enterprises had already replaced at least one SaaS tool with a custom-built equivalent, while 78 percent expected to build more internal tools in the year ahead. The most exposed categories were exactly what one would expect: workflow automations, internal admin systems, dashboards, basic CRM extensions, ticketing flows, and form builders.
This is also visible in my own portfolio. The AI Model Benchmarking Dashboard was the kind of internal decision tool a traditional team estimated at seven months and more than $50,000. It was built in hours with AI-assisted development. That does not mean every enterprise system should be built this way. It does mean the default answer has changed for internal tools where the requirements are clear and the risk profile is bounded.
The question is no longer: can we build this? The question is: is this generic enough that continuing to rent it is the expensive choice?
From SaaS to Intelligence-as-a-Service
The deeper shift is not build versus buy. It is the unit of value.
SaaS monetized access. A seat gave a person permission to use software. Intelligence-as-a-Service monetizes completed work: a resolved customer case, a generated workflow, a processed claim, a reconciled account, a qualified lead, a delivered decision.
That shift is already visible. Sierra prices customer-service agents per resolved case. Intercom prices Fin per resolution. Microsoft Copilot Studio uses action-based consumption. Salesforce has experimented with conversations, flex credits, per-user Agentforce licences, and now agentic work units. Anthropic and OpenAI charge around usage rather than human seats.
Aaron Levie's observation is the cleanest version of the point: if enterprises eventually have a hundred or a thousand times more agents than people, per-seat pricing stops being coherent. Non-human users do not map cleanly to human licences. The monetization surface becomes the action.
This is why the Helion Operational Intelligence Dashboard matters as a proof point. The value is not a dashboard someone reads. The value is an AI-native operational layer that watches the business, interprets signals, and briefs leaders in natural language. That is not software-as-access. It is intelligence delivered as work.
The same pattern appears in Pipesignal. The system does not merely provide a place to store sales data. Agents discover projects, enrich signals, prioritize accounts, and move humans closer to revenue conversations. The interface becomes secondary. The executed workflow becomes the product.
The SaaSpocalypse signal
Public markets began to notice the same structural pressure in early 2026. The iShares Expanded Tech-Software ETF fell sharply in February and March, while several listed enterprise software companies suffered deeper drawdowns. Commentators named the episode the SaaSpocalypse.
This is not a market-timing argument. It is not investment advice. The strategic signal is simpler: investors started questioning the durability of software margins built on seat expansion, switching costs, and product differentiation that AI may compress.
A SaaS vendor can still be a great business. But the market is separating software with real moats from software that was expensive mainly because building used to be hard.
Where SaaS survives
The counter-thesis matters. The death of SaaS is not literal. The death is selective.
SaaS survives when the moat is not just the interface. Salesforce remains deeply embedded as a system of record for many commercial organizations. Figma benefits from collaboration networks. Stripe and Shopify are defended by trust, distribution, merchant scale, and ecosystem gravity. Regulated systems survive because assurance, compliance, auditability, and operational resilience matter more than feature parity.
The exposed product is different: a workflow tool with countable users, generic logic, weak network effects, limited compliance burden, and functionality that can be described precisely. If an operator can specify it clearly, if the data is internal, if the process is bounded, and if the risk is manageable, the build option is back on the table.
That does not make internal building free. AI-generated and AI-assisted code still carries security risk, architectural debt, hallucinated dependencies, governance gaps, and maintenance obligations. The right answer is not to build everything. The right answer is to classify the estate intelligently.
The executive response: rent, replace, wrap, defend
The practical response is a portfolio discipline.
Every SaaS renewal should be classified into four buckets:
- Rent. Keep paying where the product is efficient, differentiated, and not strategically worth rebuilding.
- Replace. Rebuild narrow internal workflows where AI-assisted development creates a clear cost, speed, or fit advantage.
- Wrap with agents. Keep the system of record, but shift the user experience and execution layer into agentic workflows.
- Defend. Protect platforms with real moats, regulated data, deep integrations, or high operational risk.
This is where the tools on this site connect directly to the research. Use the Build vs. Buy vs. Partner Framework to evaluate acquisition strategy, the Agentic AI ROI Calculator to model the financial case, and the Use Case Prioritization Matrix to decide which workflows deserve attention first.
The worst response is ideological. “Always buy” is obsolete. “Always build” is reckless. The disciplined response is to treat software capability as a living portfolio.
What this means for CIOs and CFOs
For CIOs, this is a governance challenge. Shadow building will happen whether IT approves or not. The safer posture is to create rules, approved patterns, security review, reusable components, and clear thresholds for when internal builds are acceptable.
For CFOs, this is a capital allocation challenge. SaaS renewals should no longer pass because last year's renewal passed. Licence utilization, workflow criticality, replacement cost, vendor lock-in, and outcome measurability belong in the same conversation.
For business leaders, this is an imagination challenge. When the cost of producing software falls, the scarce resource becomes judgment: what should exist, what should not, and where the organization has enough taste and context to build something better than a vendor's generic average.
The licence economy rewarded access. The next economy rewards articulation, distribution, data, and judgment.
Source and disclosure
This article is based on my full research essay, *The Death of Software-as-a-Service*. The complete PDF is available for download below. It includes the full argument, figures, source list, counter-thesis, and references.
This is not investment, legal, financial, procurement, or security advice. The thesis is strategic: SaaS does not vanish, but the economic default that made per-seat licensing feel inevitable is breaking.