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Jersey Mike's IPO illustrates how bad the AI hype has become

A sandwich chain just mentioned "artificial intelligence" 22 times in its IPO filing. Let that sink in. Jersey Mike's IPO illustrates how bad the AI hype has become — and if you're a developer or founder in Asia building something real, this moment deserves your full attention. Because the signal-to-noise ratio in tech just hit a new low, and the distortion is about to affect how capital, talent, and product decisions flow across every market, including ours.

What Happened

Jersey Mike's, the American submarine sandwich chain best known for its "Mike's Way" subs and Danny DeVito as its celebrity spokesperson, filed for an IPO in mid-2026. TechCrunch writer Julie Bort decided to check the S-1 filing out of curiosity — surely a sandwich shop wouldn't need to invoke AI. She was wrong.

According to Bort's reporting on TechCrunch, the terms "artificial intelligence" and "AI" appear 22 times in Jersey Mike's IPO documents. The company sells submarine sandwiches. It cannot credibly claim to be an AI software business. Yet it found ways to weave AI into its investor risk disclosures — which, as Bort notes, may be even more telling than a company simply boasting about AI capabilities. Risk disclosures are legal documents. Someone made a deliberate decision to put AI language there.

This isn't an isolated case. Bort also references Bending Spoons' public debut — a company whose core business is acquiring aging, non-AI tech companies and rehabilitating them — as another example of the compulsion to sprinkle "AI dust" on investor pitches. Non-AI startups raising venture capital are doing the same thing. The pattern is consistent: investors are hungry for AI exposure, and companies — regardless of what they actually do — are feeding that hunger with language rather than substance.

The mechanics here are straightforward. When a narrative captures enough capital, every actor in the system has an incentive to align their story with it. This is not new. We saw it with "mobile-first," with "blockchain," with "cloud-native." What's different this time is the speed and the scale. AI has captured institutional investor imagination faster than any previous cycle, which means the distortion is deeper and the correction, when it comes, will be sharper.

Why It Matters for Asia

Asia's tech ecosystem is not insulated from this dynamic — if anything, it's more vulnerable to it in specific ways. Southeast Asian founders raising cross-border rounds from US or Singapore-based funds face direct pressure to frame their pitches in AI terms, whether or not AI is genuinely central to their product. A logistics startup in Vietnam, a fintech in the Philippines, a B2B SaaS company in Indonesia — all of them are being asked by investors: "What's your AI story?"

That pressure produces predictable outcomes. Roadmaps get distorted. Engineering resources get redirected toward AI features that serve pitch decks rather than users. Hiring decisions skew toward AI specialists before the product actually needs them. And perhaps most damaging: founders start measuring their progress by how convincingly they can talk about AI rather than by whether their product solves a real problem better than the alternative.

The Asia tech landscape has its own version of the Jersey Mike's problem. We see it in funding announcements where every SaaS tool is suddenly an "AI platform," in accelerator cohorts where every pitch opens with a large language model use case, and in developer job postings where "AI experience required" appears for roles that have nothing to do with model training or inference. The hype has become a lingua franca — and like all linguistic inflation, it devalues the currency for everyone.

There's also a structural risk specific to markets like Southeast Asia, where the developer talent pool is growing fast but remains relatively concentrated. When hype cycles redirect that talent toward AI-flavored projects that lack genuine technical depth, it creates skill debt. Developers spend cycles integrating AI APIs into products that don't need them, rather than building the core infrastructure, data pipelines, and domain-specific tooling that would actually compound in value over time.

The irony is that Asia has genuine, substantive AI adoption happening in manufacturing automation, agricultural technology, healthcare diagnostics, and financial services. Those applications are real. They're measurable. They don't need 22 mentions in a filing to justify their existence. The noise from the hype cycle makes it harder, not easier, to identify and fund the signal.

What This Means for Developers

If you're a developer — whether you're building your own product, working inside a startup, or advising a founder — the Jersey Mike's moment is a useful calibration tool. Here's how to use it.

First, audit your own AI language. Go through your README, your pitch deck, your product documentation. Count how many times you use "AI" and ask yourself: does each instance describe something technically specific, or is it doing the job of making the product sound more fundable? If the answer is the latter more than twice, you have a clarity problem that will eventually become a credibility problem.

Second, distinguish between AI as infrastructure and AI as decoration. AI as infrastructure means your product fundamentally could not work — or would work dramatically worse — without the AI component. An AI-powered code review tool that catches security vulnerabilities a human would miss is infrastructure. A project management app that uses GPT to reword task descriptions is decoration. Both might be useful products. Only one of them should lead with AI in its positioning.

Third, be specific about what "AI" means in your stack. "We use AI" is the 2026 equivalent of "we use the internet" — technically true of almost everything, meaningless as a differentiator. "We fine-tune a domain-specific model on proprietary transaction data to detect fraud patterns that general-purpose models miss at a 40% lower false-positive rate" is a claim. It's verifiable. It tells an engineer something real.

For developers building on platforms like MonstarX, the practical question is the same one that should drive every architectural decision: does this AI integration make the product genuinely better for the user, or does it make the product story better for the investor? Those two things can align — but only if you start from the user problem, not the pitch narrative.

The developers who will build durable products through this hype cycle are the ones who maintain that discipline. They're not anti-AI — they're pro-substance. They use AI where it compounds value and skip it where it doesn't. That sounds obvious. The Jersey Mike's filing is evidence that it isn't.

Key Takeaways

The Jersey Mike's IPO is a useful artifact precisely because it's so absurd. A sandwich chain mentioning AI 22 times in a legal filing isn't a scandal — it's a mirror. It reflects back what the market has been rewarding, and therefore what every actor in the market has learned to do.

For founders in Asia, the takeaway is not to avoid AI — it's to avoid the performance of AI. The distinction matters because investors are getting smarter. The same institutional money that rewarded AI language in 2024 and 2025 is now starting to ask harder questions: What model? What data? What benchmark? What retention curve? The window for AI-flavored pitches without AI-grounded products is closing. Founders who spent the hype cycle building real AI capabilities will be in a strong position when it does. Founders who spent it polishing AI language will be exposed.

For developers, the takeaway is about where you invest your learning time. The engineers who understand when AI adds genuine value — and can articulate why in technical terms — are more valuable than engineers who can integrate any AI API quickly. The former requires judgment. The latter is a commodity skill that will be further automated within years.

For the Asia tech ecosystem broadly, the Jersey Mike's moment is a prompt to double down on the substantive AI work already happening here — in logistics optimization, in multilingual NLP for underserved languages, in computer vision for agriculture and manufacturing — and to be more rigorous about separating that work from the noise. The region doesn't need more AI mentions. It needs more AI results.

The documentation for any serious AI-native product should tell you more about what the AI actually does than any investor filing. If it doesn't, that's the real risk worth disclosing.

Hype cycles end. They always do. What survives them is whatever was genuinely useful underneath the noise — and in Asia's tech ecosystem right now, there's more of that than the sandwich-shop filings would suggest.

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