So Many Pointless Apps
Many AI offerings today fall into a category of low traction and low defensibility—essentially “AI features disguised as products.” These solutions often lack workflow ownership, don’t solve problems worth solving, and are easily replicated, leading to poor retention and negligible monetization.
Here are some AI offerings that are on their way to the AI graveyard:
Prompt engineering tools, when offered as standalone products. Reason: prompting is a technique, not a business.
AI meme, quote, or caption generators may go viral, but they deliver little value, drive no revenue, and fail to build lasting usage.
AI cover letter or resume writers suffer from the same fate: they’re free, widely available, and used only once, offering no reason for customers to keep ‘em.
Voice note summarizer apps, once novel, are rapidly being absorbed into operating systems by Apple, Microsoft, and others, eliminating any standalone market.
AI brainstorming and idea generators may feel creative or entertaining, but they lack deep integration into actual workflows, reducing them to novelty tools.
In the same vein, AI dating or companionship apps rarely demonstrate real insight into the market, struggle with monetization, and operate in an oversaturated space.
Finally, AI-powered cooking recipe tools, coloring apps, and similar hobby-focused offerings fall squarely into the “fun but not functional” bucket—delightful but not useful, entertaining but not monetizable.
Some AI products enjoy moderate popularity but lack defensibility, falling into the category of “Popular, but easily copied, commoditized, and not true AI platforms.” These offerings may attract attention initially, but they operate in crowded spaces with low differentiation, making them vulnerable to substitution or absorption by incumbents.
Here are a few AI offerings that are still very much alive and active, but have their limitations:
No-code AI chatbot builders are a prime example—while widely used, the market is supersaturated, the products look nearly identical, and there is no meaningful moat.
AI-generated marketing copy tools, especially Jasper-like clones, thrived briefly but have been rendered largely obsolete as GPT-4 and other foundation models now produce higher-quality content out of the box.
Slide and PDF generators face a similar fate, quickly overshadowed by native integrations in Microsoft, Google Workspace, and Adobe, leaving little room for standalone offerings.
AI note-taking and meeting transcription apps once felt promising, but Zoom, Microsoft Teams, Notion, and Google have now embedded these capabilities natively, effectively closing the door on dedicated apps.
Even AI personal finance assistants struggle—without deep workflow integration, regulatory compliance, or proprietary financial data, they remain easily replicable features rather than defensible products.
In each case, these tools failed to establish platform-level ownership, leaving them stuck in the commodity zone with no path to durable advantage.
Common Traits of Low-Traction, Low-Defensibility AI Offerings
Low-traction, low-defensibility AI offerings are easily absorbed by larger platforms because they do just one discreet task, lack end-to-end workflow ownership, and deliver feature-level value rather than standalone product or company value. The main reasons why AI products struggle to gain traction or maintain defensibility are these offerings:
Have zero lock-in because users can leave without losing data or customization, lack persistent state or habit-forming value, and offer no compelling reason to return.
Lack differentiation because they rely on generic models and public data without proprietary context, specialized corpus, fine-tuning, or domain depth.
Offer no moat because they’re cheap to copy, easy for new entrants to replicate, and quickly diluted by open-source and API-based tools.
Lack defensibility because they’re inexpensive to clone, simple for competitors to replicate, and rapidly commoditized by open-source and API-based alternatives.
Struggle to monetize because they solve non-critical problems with no clear ROI or urgency, are not tied to business outcomes, and are often used only for novelty or one-off tasks rather than something worth paying for.
Sit on the surface with no deep workflow integration, living outside core systems and real work environments, offering only a shallow AI layer without meaningful automation.
Prioritize novelty over necessity—great for a quick wow or viral moment but quickly forgotten because they don’t solve durable, meaningful problems.
Generate no compounding value because they don’t improve with more users, lack data or learning loops, and offer no ecosystem, collaboration, or platform effects.
Have no clear buyer, champion, or budget owner—floating between consumer, prosumer, and enterprise without serving any one deeply enough to earn commitment.
Summary
AI features that do not own workflows, do not leverage proprietary data, and do not solve costly, persistent problems — will be replicated, commoditized, and eventually absorbed by platforms.
I share a similar sentiment, but want to point out two counter examples that may point to wedges where one can create value.
It's true that Zoom, Google Meet, Teams, etc all have built in note taking tools, but I continue to pay for a third party (Fathom in my preference), because I have meetings across all these conferencing systems and I want a single place to consolidate my transcripts and AI summaries. So there may be opportunities to create value by being neutral, integrating across a variety of different systems.
Second is in the arena of "slide generators," where it was recently reported that Gamma has raised $68M Series B, at a $2.1B valuation coming off momentum reaching $100M ARR, profitably, with a team of just 50 people ($2M ARR per employee). The CEO asserts their success is "proof that an AI-native company can disrupt a category everyone assumed was won."
I have not used Gamma, so I can't provide a direct rationale for their success, but here is Google's AI Overview summary:
"Gamma excels at fast, design-forward AI generation, resulting in web-based formats, while PowerPoint offers a more robust and customizable environment for manual control, with Copilot assisting in the creation process. Gamma's outputs are often praised for a modern look, whereas PowerPoint + Copilot offers more manual control and features like automatically generated speaker notes."
So some products will be burdened by their legacy and novel native AI approaches might create new modalities / UX that people just love better.
Superb framing on workflow ownership as the dividing line between features and products. Your point about "features disguised as products" cuts right to why so many AI tools feel impressive on day one but disappear by week two. The absence of compounding value is crucial here,too. When tools don't get smarter with usage or accumulate context over time, they're stuck offering the same shallow experience indefinitely. The chatbot builder example is telling because even though the category seemed hot, there was no moat once everyone realized they all looked identical. What's interesting is that this mirrors the enterprise software playbook from 20 years ago: the winners embedded themselves into core workflows and became impossible to rip out, not because of tech but because of data and habit.