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Product & Management Stage

How come "Solution looking for a problem" is no longer a joke in Product Management due to AI

September 18, 2025 1:40 PM
Long Lecture
🇬🇧 English
Bratysława 1

About

This talk explores how the AI hype is pushing companies to abandon solid product practices, rushing to ship AI features without clear problems to solve. I’ll compare this trend to past tech crazes like 3D and crypto, highlighting patterns and pitfalls. Framework:

1) What “solution looking for a problem” used to mean,

2) The AI hype wave,

3) Historical parallels,

4) How PMs can stay grounded. It’s crucial now to balance innovation with user value.

5)How is it hard as AI can really bring value, much more then previous tech crazes

Talk by

Photo of Dr Bart Jaworski, Senior Product Manager, Product management content creator at Bartosz Jaworski PM
Dr Bart Jaworski
Senior Product Manager, Product management content creator at Bartosz Jaworski PM

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From the recording

Talk in brief

At WaysConf 2025, Dr Bart Jaworski discusses how the rise of artificial intelligence has turned the classic product management joke of a "solution looking for a problem" into an industry-wide reality. Driven by the fear of missing out (FOMO) on the next "iPhone moment," companies are rushing to integrate AI features without identifying genuine user needs, mirroring past tech hypes like 3D TVs, blockchain, and the metaverse. Jaworski highlights notable industry missteps, such as Amazon Go's heavily manual "Wizard of Oz" cashierless system and Apple's rushed Apple Intelligence features, alongside genuine successes like Duolingo's language coach and Microsoft's Co-pilot. To avoid these pitfalls, product managers must treat AI as a tool rather than a standalone solution. Jaworski advises starting with verified user problems, running quiet prototypes, managing stakeholder expectations, and rigorously addressing risks like prompt hacking and data privacy before launching AI-driven features.

Key takeaways

  1. 01

    The Tech Craze FOMO Trap

    Companies rush to adopt new technologies like AI out of fear of missing the next "iPhone moment," often pushing solutions that customers never requested. This mirrors past overhyped trends such as 3D screens, blockchain, and the metaverse.

    Watch from 1:44
  2. 02

    The Illusion of Automation

    Many highly publicized AI products, such as Amazon Go and Builder.ai, rely on "Wizard of Oz" MVPs where human workers manually perform tasks marketed as automated. This makes these solutions highly unscalable and deceptive if kept as permanent setups.

    Watch from 17:34
  3. 03

    The Y2K and Dot-Com Labeling Phenomenon

    Much like the Y2K certification craze and the dot-com bubble, companies today slap "AI" labels on unrelated products to attract investors and customers. Product managers must look past this marketing hype to focus on actual utility.

    Watch from 14:44
  4. 04

    Successful AI Solves Specific Problems

    AI succeeds when integrated into low-risk, highly interactive contexts, such as Duolingo's conversational language tutor or Microsoft Co-pilot's developer assistance. These implementations focus on delivering immediate, practical value rather than general-purpose intelligence.

    Watch from 24:16
  5. 05

    Validate Quietly and Manage Expectations

    Product managers should introduce AI functionalities quietly by starting with small prototypes, validating them with real user data, and even testing without AI first. Overhyping features before they are polished risks damaging user trust and exposing the product to security vulnerabilities like prompt hacking.

    Watch from 31:51
Read edited transcript highlights

These concise notes were edited from automatic captions and checked against the talk structure. They are not a verbatim transcript.

The History of Tech Crazes and Solutions Looking for a Problem

Throughout tech history, companies have repeatedly pushed technologies that consumers did not actually want. For instance, the 3D movie craze sparked by Avatar in 2009 led to a massive push for 3D monitors, games, and DVDs, even though human brains naturally simulate depth and viewers disliked wearing glasses. Similarly, blockchain was marketed as a revolutionary tool for secure records, but largely devolved into highly speculative currencies and confusing NFTs. Even the metaverse was heavily funded by Meta to replace physical meetings, yet it failed to catch on because it ignored the fundamental human need for real, in-person connection.

Watch from 3:40

The Fear of Missing the Next iPhone Moment

The primary driver behind companies pushing unwanted technologies is the fear of missing out, or FOMO. When Steve Jobs launched the first iPhone in 2007, it completely disrupted the mobile market, wiping out giants like Nokia and BlackBerry within a few years because they failed to adapt. Ever since, whenever a new technology emerges, corporate leaders panic that they might miss the next "iPhone moment." The difficulty is that a true technological shift is rarely obvious right at its release; it requires time to cool down, evolve, and prove its actual utility to the public.

Watch from 9:24

The Reality of Fake AI and Wizard of Oz MVPs

Several prominent AI products have turned out to be "Wizard of Oz" MVPs, where human labor is hidden behind a curtain of supposed automation. Amazon Go promised cashierless shopping powered by AI cameras, but in reality, a vast majority of the orders had to be manually verified by a remote team, making the system completely unscalable. Similarly, Builder.ai raised billions by promising to generate fully functional apps from simple text prompts, but they actually relied on human developers to speed-code the requested applications behind the scenes.

Watch from 16:32

Rushing to Market: Apple vs. Microsoft

Rushing to keep up with AI trends can lead to major corporate missteps. Apple initially resisted the AI craze, but investor pressure forced them to announce Apple Intelligence before it was ready, resulting in delayed features and underwhelming tools. In contrast, Microsoft successfully pivoted by embracing a startup mentality. They bypassed their usual slow corporate processes to launch Co-pilot first. Even though Co-pilot suffers from typical LLM flaws like hallucinations, being first to market allowed them to capture investor interest and significantly boost developer productivity.

Watch from 18:41

A Practical Framework for PMs to Adopt AI

Product managers must treat AI as a development tool, like an API, rather than a standalone solution. To implement it successfully, PMs should start with a genuine, unsolved user problem and validate ideas quietly using small prototypes. It is often wise to test the concept without AI first, using manual verification or simple prompt testing to ensure viability. Furthermore, PMs should avoid overhyping "AI" in their marketing, as everyday users do not care about the underlying technology; they only care about having their problems solved effectively.

Watch from 31:11

Navigating the Unique Risks of LLM Implementations

Deploying AI features introduces unique risks that traditional software does not face, such as prompt hacking and hallucinations. For example, when Fortnite introduced an LLM-powered Darth Vader character for players to chat with, users immediately hacked the prompt to make the character swear, creating unwanted viral publicity. Product managers must prioritize privacy, ethics, and security, ensuring they do not rush a product to market under stakeholder pressure only to compromise user trust and expose sensitive data.

Watch from 35:49
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