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The risk is not that every established product must be replaced by AI. It is that a company may refuse to test a better way to solve a customer problem because that solution could take demand from its current product. The safer response is deliberate self-competition: test the alternative with customers, compare its value and costs with the existing product, and retire or redesign only when the evidence supports it.
What it means to kill your own product
In product strategy, “killing” a product need not mean shutting it down immediately. It can mean allowing a new product or approach to compete with an established one—even if the new option could eventually replace it. The test begins with the customer problem, not with a commitment to preserve the current feature set, architecture, or revenue stream.
That is a form of self-disruption: a company tests a new way to serve customers even when it may draw demand away from its existing offering. In a 2015 essay, Arjun Sethi connected this challenge to Clayton Christensen’s Innovator’s Dilemma and discussed historical examples involving Apple, Amazon, Facebook, and Netflix. Those examples illustrate the argument at that time; they are not evidence about the companies’ present product strategies.
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“Add AI” can describe very different decisions. Separating them helps a team avoid treating every new capability as a reason to rebuild—or every existing product as something to protect.
#1 Best Overall
Augment the existing product
Add an AI capability to the current product while keeping its main workflow and customer promise intact. This may be the right choice when the existing product still solves the problem well and AI makes a particular task easier or more useful.
Redesign the product around AI
Change the core workflow because AI enables a substantially different way to get the job done. This is more than adding a feature: it may change what customers do, what the product needs to handle, or how success is measured.
Rank #2
Build a competing alternative
Give a team room to solve the same customer problem without requiring it to preserve the existing product’s architecture or assumptions. The new concept may coexist with the old product, serve a different group, or eventually replace it. The point of the experiment is to discover which outcome better serves customers—not to declare the incumbent obsolete in advance.
Why AI makes the choice harder
If AI makes it cheaper or faster for a team to build features, it can also make feature requests easier to accept without asking whether they improve the product. In a June 24, 2026, opinion article, Brian Gallagher argued that this can weaken product-market fit by encouraging teams to add requests without enough scrutiny. That is a strategic warning, not a quantified finding that AI has already caused a measured decline across products.
The practical implication is to be selective. A request may be technically feasible and still make the product less clear, harder to use, or less focused on its core customer problem. Lower development effort does not remove the need to decide what the product should be.
Run a bounded test before committing to replacement
Shahid N. Shah’s 2026 opinion essay proposes a useful experiment: let a small team address the same customer problem without requiring it to preserve the existing architecture, then test the result with customers. It is a proposed approach, not a proven rule that fits every company or product.
- Define the customer problem. Describe the job customers need done and the outcome they want, rather than framing the brief as “build an AI version” of the current product.
- Set boundaries for the experiment. Give a small team permission to explore a competing approach, while making clear that the experiment does not itself authorize a full migration or shutdown.
- Test with customers. Observe whether the alternative actually improves the customer outcome. Interest in a demo or a request for a feature is not, by itself, evidence that the new product is better.
- Compare the whole proposition. Consider customer value, fit with the intended users, adoption, economic sustainability, what the company learns, and the risks of moving customers or maintaining continuity.
- Choose a next step proportionate to the evidence. Keep testing, refine the new approach, integrate selected capabilities into the existing product, plan a transition, or stop the experiment. Do not treat a prototype as proof that wholesale replacement is necessary.
Decide what to do with the existing product
A comparison should cover more than whether the new approach uses AI. The following questions form a practical decision framework, not a validated scoring system: the cited sources do not establish weights or an industry-wide formula.
- Customer value: Does the alternative solve the customer’s problem better, or does it mainly make the product seem more current?
- Product fit: Does the new approach serve the same customers and use case, or is it better suited to a distinct segment?
- Adoption: Do customers use and prefer it in a real evaluation, rather than merely expressing curiosity?
- Economic sustainability: Can the company support the alternative and its ongoing costs in a way that makes sense for the business?
- Strategic learning: Has the experiment revealed something useful about customer needs, even if the alternative is not ready to replace the product?
- Migration and continuity: What would customers have to change, and what would be disrupted if the existing product were retired?
Retirement is part of product lifecycle management, not just a reaction to a new technology. ProdPad’s webinar page, “Why, How, and When to Kill a Product or Feature,” addresses reasons to retire products or features, how to evaluate the decision, stakeholder communication, and integrating retirement into lifecycle planning. The description supports treating retirement as a considered process; it does not quantify the costs of migration or prove that a particular retirement decision will succeed.
Best Value
Why replacing a profitable product can still be the wrong call
A product may continue to serve customers well even when a newer approach is possible. Replacing it too quickly can impose migration work, interrupt established workflows, or damage continuity. Those risks do not mean an incumbent should avoid experimentation; they mean that a better prototype is not automatically a better transition plan.
The opposite risk is protecting the current product so strongly that no team is allowed to test a meaningfully different solution. Tim Cook’s statement, “Our core philosophy is to never fear cannibalization. If we don’t do it, someone else will,” was reported by Arjun Sethi in a 2015 WIRED essay as a remark from Apple’s Q1 2013 earnings report. The earnings transcript is not independently established here, so the quote should be understood as WIRED’s historical reporting—not as a current statement or a universal rule.
Self-competition is therefore a way to reduce strategic blind spots, not a command to sacrifice a working product. Let an alternative earn the right to expand by demonstrating customer value and a viable path forward. If it does not, the company can retain the existing product and still use what it learned.
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