The vast majority of AI startups are simply wrappers around standard functionality from ChatGPT and other LLMs, dressed up as “agents” with organizational structures that break tasks into logical chunks as a supposedly structured mechanism for achieving results. These so-called “AI agent startups” rarely offer anything new—most are little more than glorified interfaces sitting atop a pre-existing model, often marketed with buzzwords like automation, autonomy, or intelligent orchestration. Beneath the surface, they are little more than shallow layers of prompt engineering disguised as innovation.
However, most of these systems run on OpenAI-style token models, which can become extremely costly very quickly—especially when you could access nearly identical functionality directly from the LLM providers themselves, often at a lower cost or even for free. The markup on tokens, paired with added infrastructure, API usage fees, and maintenance costs, frequently destroys the unit economics of these startups. Many founders underestimate how quickly token usage compounds as users scale, turning what seems like a cheap MVP into an operational liability.
These wrapper systems are ultimately doomed to fail. They may provide a temporary convenience, but if they gain traction, they will almost certainly be absorbed, replicated, or outcompeted by larger corporations. Big players like Google, Microsoft, and OpenAI have far greater distribution, more efficient infrastructure, and direct access to the same models. Once these corporations recognize a successful niche, they can integrate that feature into their ecosystems overnight, wiping smaller competitors off the map. Big platforms win through brand trust, integrations, and the simple advantage of having a global audience already locked into their ecosystems.
Instead, founders and developers should focus on practical, grounded solutions where AI acts as a tool to enhance productivity—not as the product itself. The selling point should always be the problem solved, the efficiency gained, or the value delivered, not “we use AI.” Overemphasizing the AI component is a rookie mistake; it attracts hype, not customers. And fundamentally the word AI is becoming less attractive and producing more distrust with potential clients. People are already sick of hearing the term AI… They are scared of losing their jobs and livelihood and the impact that AI has on their relationships. The truth of the matter is that in reality, only the rich have benefited from AI startups while the majority of the worlds population has seen retrenchment , lower wages and less business opportunity as AI automates more and more. Even though the automation of your business with AI comes at incredible risk, in the short term is does pose significant benefits for at least half of the corporations that have implemented it… This is why it is critical to focus principally on niche business and not to try to directly compete with large corporations
Recent history proves this pattern clearly. Dozens of AI startups launch and vanish every single day. For instance, since Loveable launched only a few months ago, at least seven other VIBE app builders have emerged—none of which gained significant traction. Similarly, after Cursor was released, hundreds of open-source projects and premium clones followed, only for GitHub Copilot and OpenAI Codex to make large portions of Cursor’s functionality redundant. This is a recurring cycle: early excitement, rapid cloning, market saturation, and eventual collapse.
When clients ask me to evaluate their product ideas, I examine the market carefully—analyzing competition, pricing, user targeting, and scalability. If the product is another “AI-powered social media content generator” or a basic wrapper for ChatGPT or DeepSeek, I advise against pursuing it. Unless a startup has something truly unique—such as proprietary datasets, a defensible workflow, or strong regulatory barriers—its offering will be copied, commoditized, and buried within months.
If AI is merely one part of a larger offering, however, it can be a powerful enhancer. I use my experience in systems design, along with training in AI safety and security, to help companies develop minimal viable offerings that can scale responsibly. The key is ensuring the AI component has guardrails—retrieval boundaries, validation layers, and human-in-the-loop checkpoints. There must also be clear cost controls, monitoring systems, and auditable outputs to ensure both dependability and accountability.
Fundamentally, when someone asks me if they should invest in a “groundbreaking” AI service, my answer is almost always no. Unless it possesses a genuine moat—exclusive data rights, deep integrations that create switching costs, or a proven ROI in a niche vertical—its long-term survival is unlikely. Venture capitalists and lay investors are pouring money into projects that will not survive past the hype cycle. The data already shows that over 90% of new AI startups fail within two years.
Other sectors are becoming dangerously saturated as well. Countless businesses are now spinning up “custom ERPs” using VIBE builders, believing they are innovating. In reality, the ERP market is being flooded with near-identical systems—minor variations of the same dashboards, workflows, and integrations. The result is a marketplace overflowing with undifferentiated slop. If something is easy to build, it is easy to copy. And if it’s easy to copy, it’s not a sustainable business.
Real innovation isn’t copying prompts or wrapping APIs—it’s solving hard, valuable problems. True innovation comes from applying AI within scientific, industrial, or socio-economic domains where there are measurable results, strict compliance requirements, and proprietary data. Industries like logistics optimization, healthcare documentation, and claims processing are examples where AI can enhance efficiency, accuracy, and compliance without being the “selling point” itself.
As someone who studies the market closely and is trained in critical thinking, I specialize in analyzing products, identifying weak points, refining business logic, and stress-testing the viability of an idea before it goes to market. You can prompt ChatGPT all day long, but that doesn’t replace real-world analysis. You still need someone who can look past the hype, spot unseen vulnerabilities, and anticipate business, legal, and technical risks.
In the past, when a company launched something truly innovative, it often led to healthy competition—copycats and variants built around genuine improvement. But with today’s low-code “VIBE” tools and generative app builders, cloning has become instant and thoughtless. Developers can spin up “enhanced” clones of successful apps overnight without understanding the problem space. The result isn’t progress—it’s market decay, with meaningless variations drowning out true innovation.
That’s why apps built on VIBE coders and similar platforms have a disproportionately high failure rate. Their creators mistake ease of creation for innovation. The market, however, rewards depth, originality, and resilience—not convenience.
Business owners should instead look inward and ask how they can use AI to streamline internal processes, optimize decision-making, or improve customer experience—not to create another disposable app. They should follow established safety and security principles, or work with professionals like myself who understand AI vulnerabilities, risk assessment, and secure systems design.
Moreover, the more automation you give your business, the fewer staff you have—and the greater your personal accountability becomes. When an AI makes a critical mistake because a developer failed to verify an algorithm before deployment, that liability rests on you, not the machine. By replacing people with automation, you are not removing responsibility—you are concentrating it. If your system fails, your reputation, your business, and potentially your legal standing are all on the line.
In short: the AI gold rush is not about innovation—it’s about imitation. Those who survive it will be the ones who use AI responsibly as a tool, not as the product itself, and who treat automation with the caution and discipline it deserves.
