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Derek Slager’s argument is that AI’s impact on conventional software is being overstated, while its potential to coordinate and organize work is being underestimated. In a TechFinitive interview published on October 9, 2026, the Amperity co-founder and co-CEO said AI can make it easier to build a basic app, but dependable business software still takes operational expertise. He sees a stronger opportunity in using AI to interpret information and handle work that crosses processes or does not fit neatly into a standard interface.
What is Derek Slager’s argument about AI and traditional software?
Slager is drawing a distinction between generating software and making software dependable in production. AI can help create a prototype quickly, but an organization still needs to keep the finished system available, secure, supported, and maintainable. In his view, treating easier app creation as proof that AI will replace conventional software misses the operational work that makes software reliable.
He also argues that AI’s less appreciated potential lies in coordinating work. Traditional software is often organized around defined tasks and interfaces; AI systems can help with work that spans information sources or has no tidy, predefined workflow. That is a change in how people and systems get work done, not simply a faster way to write an application.
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Is AI really replacing traditional SaaS?
Slager’s interview does not present AI as a wholesale replacement for SaaS. It suggests a more qualified view: conventional applications remain useful for stable, repeatable tasks, while AI can extend what organizations can do around less-defined work.
Where conventional software still fits
A conventional interface gives people a consistent way to perform a known task. If the process is repeatable and the necessary choices are well understood, a purpose-built application can make the work predictable. That predictability is valuable in business settings where teams need to know what a system does and how it behaves.
AI does not automatically make a defined task better. A company would still need to establish that an AI-enabled alternative improves a meaningful result, rather than simply adding a new interaction layer or producing a modest time saving. Slager’s point is not that traditional software has no limits; it is that those limits should not be confused with the entire value of software.
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Where AI may add something different
Slager describes AI agents as useful for problems that do not “fit into the neat lines often expected by conventional interfaces.” An agent may be able to take context from a broader task and help coordinate work that would otherwise require a person to move between systems or interpret scattered information. That potential is distinct from replacing a single established application.
The two approaches can coexist: an interface can remain the dependable route for a routine task, while an agent helps bridge gaps around it. This is a way to think about complementary capabilities, not a guarantee that any particular agent can safely carry out every cross-system action. The task, permissions, data quality, and consequences of mistakes all matter.
What can AI agents do that normal software cannot?
They may help with work that is ambiguous, information-heavy, or spread across processes, where a fixed interface is not designed to guide every possible path. Their potential is to interpret context and coordinate steps rather than only expose a predefined set of operations.
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Slager’s framing is important: he does not say that agents are inherently more reliable or more capable than ordinary applications. Instead, they may address problems that do not align with the tidy task boundaries conventional software often assumes. A company should therefore judge an agent by the particular work it handles, including whether its actions can be governed and checked.
| Dimension | Conventional software | AI agents |
|---|---|---|
| Task shape | Strong fit for defined, repeatable tasks with known steps. | Potential fit for ambiguous or cross-process work that does not follow one fixed path. |
| Interaction | Usually organized around established interfaces and workflows. | Can offer on-demand assistance around problems that do not fit neatly into those interfaces. |
| Reliability and uptime | Must still be operated and maintained to meet business needs. | AI capability alone does not establish dependable service; operations remain necessary. |
| Security and governance | Requires security and operational controls. | Requires controls appropriate to the data and actions the agent can access. |
| Business value | Should be assessed against the result the application supports. | Should be assessed against measurable business results, not novelty or speed alone. |
This comparison describes the roles Slager’s argument points toward, not a universal ranking. A fixed workflow may be the better choice when consistency is essential; an agent may be worth evaluating when the work is hard to express as a sequence of fixed screens and rules. Domain expertise remains relevant in both cases: a system must be designed around what the business actually needs and what a correct outcome means.
Does vibe coding produce reliable business software?
It can make a basic app easier to prototype, but a working prototype is not evidence that the application is ready to run a business process. Slager accepts the appeal of building an app quickly and emphasizes that the harder work includes availability, security, support, and operational excellence.
He illustrates the difference with a specific availability figure: “99.99% of the time.” That number is Slager’s example in the 2026 interview, not a measured performance result for an app or a universal service-level requirement. It conveys the kind of reliability expectation a professional team may need to meet; it does not mean that every business application must promise that exact level.
A prototype can demonstrate that an idea is technically possible. Production readiness asks different questions: who will maintain it, how will failures be handled, what information can it access, and who supports the people relying on it? A business should not confuse speed of initial creation with the ongoing responsibility of operating software.
What to check before relying on an AI-built app
- Availability: Decide what downtime would mean for the process and who is responsible for restoring service.
- Security: Identify what data the app can access and how access is controlled.
- Support: Establish where users go when the system behaves unexpectedly.
- Maintainability: Ensure someone can understand, update, and safely change the application after the initial build.
- Domain fit: Have people who understand the business process check that the app handles its real requirements, not just a demonstration case.
These checks follow from Slager’s distinction between making an app and delivering software that can be depended on. The interview does not provide a testing standard or a step-by-step certification method, so the checklist should be treated as practical questions for an organization to answer—not as a claim that completing them guarantees safety.
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How should a company decide whether an AI workflow is worth paying for?
Start with the business result, then measure whether the AI-enabled process changes it. Slager says a 10–20% workflow speedup is not automatically transformative or worth paying for; the figure is his interview example, not a benchmark established for all workflows or a result from a specified experiment.
Time saved can matter, but its value depends on what happens as a result. A faster step may free people to do higher-value work, reduce a meaningful bottleneck, or improve an outcome. It may also fail to justify its cost if the saving is small, difficult to use, or offset by new operating work. Slager’s test is whether the change correlates with business results, rather than whether the AI looks impressive in a demo.
A practical evaluation sequence
- Name the outcome: Define the business result the workflow is supposed to improve before choosing a model or agent.
- Describe the current process: Identify where work is delayed, repeated, or handed between systems and teams.
- Choose a bounded use case: Select work where the relevant input, expected result, and acceptable failure modes can be described.
- Measure the effect: Compare the AI-enabled process with the existing one using a metric that reflects the intended business outcome, not only task speed.
- Account for operating requirements: Include the work needed for data quality, security, support, oversight, and ongoing maintenance.
- Decide whether to expand: Scale only if the result is valuable enough to justify the operating model and the risks are manageable.
Slager’s emphasis on outcome-based value also has a commercial implication: he says companies creating real customer value with AI are getting paid for it because that value correlates to business results. The interview does not quantify the revenue or return those companies receive, so the statement is best read as his value test, not as a promise of financial gain from adopting AI.
Why does enterprise AI need trusted customer data?
AI can only use the context available to it. For customer-facing work, that means an organization needs customer information that can be brought together and kept dependable enough for the intended use. Slager calls building a strong data foundation to help models perform well “the single best investment a company can make in the AI era.” That is his judgment in the interview, not an independently measured ranking of all possible AI investments.
Amperity describes itself as an AI-powered customer data cloud focused on unified customer context and identity resolution. Its official product descriptions emphasize identity resolution, real-time profiles, governed customer data, and agent-driven use cases. This company context helps explain why Slager puts customer data at the center of the argument, but it should not be treated as independent proof that a particular platform or data strategy produces a particular result.
Identity resolution aims to connect records that refer to the same customer, while a unified profile can give teams a more coherent view of that person. If relevant records are fragmented or unreliable, an AI system may lack the context needed to make a useful recommendation or coordinate an appropriate next step. Better data does not by itself guarantee a good model output; it gives the system a more useful foundation to work from.
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What does it take to scale AI beyond isolated pilots?
Slager’s answer is organizational as well as technical: scaling AI requires shared ownership, a reliable context layer, and an operating model designed to use it. A pilot may work inside one team while still failing to connect with the data, decisions, and responsibilities needed to make it useful across a company.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA reliable context layer is the information an AI system needs to understand the task in the setting where it is being performed. For customer applications, that can include trusted customer information; more broadly, the organization must decide what context is relevant, who maintains it, and which teams may use it. If each pilot assembles information in isolation, the organization risks creating disconnected tools rather than a repeatable capability.
Shared ownership also means that AI cannot be treated only as a technical-team project. Business teams know the outcomes and exceptions that matter; technical and operational teams are needed to manage the systems and data. Slager’s argument implies that scaling depends on those responsibilities working together, not merely on producing more prototypes.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do consumer survey figures say about AI and brand discovery?
Two figures cited in the interview context suggest that AI recommendations may be influencing how some U.S. consumers discover brands, but they do not establish how consumers in every market behave. Unite.AI reported results from an Amperity survey of 1,000 U.S. consumers in 2026: fewer than a quarter said they go directly to a familiar brand without first factoring in an AI recommendation, and 60% said AI had led them to a brand they had not previously considered.
Those are reported survey responses, not observed purchase records or a measure of the effect on revenue. They point to a possible customer-discovery role for AI, but the figures alone cannot show which recommendation systems respondents used, how often recommendations changed a purchase, or whether the findings generalize beyond the surveyed U.S. consumers. For businesses, the practical connection to Slager’s thesis is that customer context and measurable outcomes matter when judging whether AI-driven discovery creates value.
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What should business leaders take from Slager’s view?
Slager is not arguing that AI has little effect on software. He is challenging the assumption that the most important effect will be software creation itself. AI may make prototypes cheaper and quicker, but dependable production systems still require professional operation; agents may matter most where they coordinate work that conventional interfaces do not handle well.
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For a company deciding what to do next, the useful sequence is to identify a real business problem, choose whether it is repeatable or ambiguous, and determine what reliable context the solution needs. Then measure the result and include the costs of operating it. This approach keeps AI’s promise in view without mistaking a quick demo, a small speed gain, or a survey statistic for proof of business impact.
Frequently Asked Questions
Is Slager’s interview saying AI will not change software development?
No. His argument allows for AI to make basic applications easier to create; it questions whether that capability alone amounts to replacing dependable business software. The distinction is between generating an application and sustaining a service people can rely on.
What does identity resolution mean in customer data?
It is the process of connecting customer records that may refer to the same person, so teams can work from a more coherent profile. The quality of that connection depends on the information available and how it is governed.
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No. The reported figures describe what surveyed U.S. consumers said about recommendations and brand discovery. They do not measure purchases or establish a causal effect on revenue.
Why might a small AI time saving still be valuable?
A modest reduction in time could matter if it removes a bottleneck or frees capacity for work with greater value. The decision depends on the resulting business benefit and the operating costs, not the percentage alone.
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