A few months ago, I sat across the table from the CEO of a well-known tech firm—a company that had invested millions into integrating AI into their business. They had gone all in.
I’m talking about hiring armies of machine learning engineers, redesigning entire workflows around AI, restructuring departments, and subscribing to every shiny, AI-powered tool they could find.
And now? They were pulling the plug.
He looked at me, exhausted and visibly frustrated.
“It was supposed to be the future,” he told me bluntly. “Instead, it became an expensive experiment.”
This was hardly an isolated incident.
At Webiniser Ltd, we’ve been deeply immersed in AI research and deployment, regularly engaging with top tech executives, investors, and startup founders. They all got promised the same thing:
“AI will automate everything. It’ll slash your costs. It’ll make your business unstoppable.”
But now, after burning through millions of dollars, many of them were deeply skeptical—not just about AI—but about innovation itself.
That’s the real danger when technology gets overhyped and underdelivers. Entrepreneurs don’t just lose money; they lose trust.
And losing trust is far more damaging than losing cash.
The AI Hype Cycle: Why Businesses Keep Falling into the Trap
We’ve seen this hype cycle before.
Remember blockchain? In 2018, it seemed every business desperately needed a “blockchain strategy.” How did that play out?
Or take the Metaverse—major corporations spent millions buying “virtual real estate.” Today, those virtual headquarters stand abandoned.
Now, it’s AI’s turn to ride this hype cycle, only the stakes are significantly higher. Unlike blockchain or the Metaverse, AI does have genuinely transformative potential. It solves real problems, improves efficiencies, and can fundamentally enhance decision-making.
But there’s a massive difference between AI’s potential and AI’s current reality.
Most entrepreneurs, unfortunately, fail to see that distinction until it’s too late.
Understanding AI’s Current Limitations Clearly
AI today isn’t a finished, plug-and-play product. It’s still experimental—particularly Large Language Models (LLMs) like ChatGPT.
Why Large Language Models (LLMs) Are Just Prediction Machines
Let’s clarify what LLMs like ChatGPT actually are.
When you interact with ChatGPT, Claude, Gemini, or any similar AI, it feels astonishingly human. The responses seem intelligent, thoughtful—even creative. But that’s an illusion.
Here’s what’s really happening:
- LLMs process your prompt and predict the next word (or phrase) based purely on statistical patterns learned from billions of sentences in their training datasets.
- They don’t “understand” the meaning behind your words. They don’t possess reasoning abilities, genuine comprehension, or human-like insight.
- They’re simply sophisticated autocomplete tools, albeit extraordinarily advanced ones.
To put it bluntly, they’re brilliant guessers. They’re predictive machines, not thinking beings. And when you’re running a business, prediction alone isn’t always enough—especially when accuracy and reliability are critical.
For instance, if you ask an LLM a question, it gives the statistically most likely response, not necessarily the correct one. This mechanism explains why these models confidently “hallucinate,” providing entirely fabricated yet plausible-sounding answers.
If your business relies on accurate data, relying exclusively on these predictive models can become disastrous.
Why AI Still Isn’t a Fully Functional Business Product
Here’s the truth: as powerful as today’s AI is, it’s still not business-ready for several key reasons:
- Reliance on Human Oversight: Rather than automating work entirely, most AI products require significant human oversight. If your goal is to reduce costs by automating repetitive tasks, AI currently might paradoxically increase the workload.
- Unreliability in Critical Tasks: AI’s propensity to confidently provide incorrect information (hallucinations) makes it unsuitable for sensitive fields like healthcare, legal, or financial decision-making, where accuracy isn’t negotiable.
- Scalability and Cost: Scaling AI solutions exponentially increases your costs. The massive computational power needed—primarily expensive GPUs—means businesses typically underestimate their true AI expenses.
The Hidden Compute Problem: AI Isn’t Just Another SaaS Product
AI is fundamentally different from traditional software.
When you subscribe to a typical SaaS product, costs scale predictably. But AI is computationally intensive, and its costs escalate dramatically as you scale from experimental deployment to enterprise-level integration.
Consider this example clearly:
- A single ChatGPT query costs OpenAI about ten times more than a Google search. Imagine the overhead if you scale that to millions of interactions per day without OpenAI’s infrastructure.
- GPUs, the core component powering AI models, are scarce and expensive. NVIDIA holds a near monopoly, and GPU prices continue to soar as demand drastically outpaces supply.
- Beyond hardware, there’s electricity consumption. Running AI models at scale devours power, increasing operating costs exponentially.
Businesses that initially saw AI as a cost-saver soon discovered the opposite—it became a financial drain. AI isn’t just another line-item expense; it’s a continuous, resource-intensive burden.
AI Hallucinations: A Business Disaster Waiting to Happen
One of the most dangerous issues with today’s AI models is “hallucination”—the generation of incorrect but plausible-sounding information.
Some clear real-world examples:
- A New York attorney used ChatGPT to write a legal brief in 2023. The AI confidently cited entirely non-existent court cases, creating professional embarrassment and serious legal consequences.
- AI-powered financial tools have fabricated market data, causing serious investment mistakes and financial losses.
- Customer support chatbots powered by AI have provided inaccurate or even offensive answers, triggering PR crises.
Now, ask yourself this plainly: Would you risk your business’s reputation—or even its existence—on software that unpredictably generates falsehoods without accountability?
That’s exactly the risk entrepreneurs face today by integrating AI without understanding its fundamental limitations.
The ChatGPT Illusion: It Was Never Meant for Your Business
Let’s clear another common misconception: ChatGPT was never built to be an enterprise-grade business solution.
OpenAI explicitly described it as a research demo—not a finished product. Yet, due to its widespread popularity, businesses mistakenly believed ChatGPT represented mature, ready-to-integrate AI technology.
Here’s the critical takeaway:
- ChatGPT (and similar LLMs) still requires significant human oversight for accuracy.
- Employees must frequently fact-check and correct outputs, ironically adding to their workload instead of reducing it.
Smart businesses today understand this clearly. They use AI as augmentation—an assistant to human tasks—not as a replacement for critical human thinking and decision-making.
Barons Insights: How to Actually Use AI Wisely
As an entrepreneur, your goal is to deploy resources strategically. Here’s how to approach AI intelligently, avoiding costly mistakes:
Solve Clearly Defined Problems
Don’t chase AI just because it’s trendy. Clearly define the business problem you’re aiming to solve. Can AI realistically and immediately deliver measurable improvement? If not, reconsider.
Demand Results, Not Promises
Avoid AI vendors selling dreams. Request live demos and concrete proof of effectiveness in real-world scenarios—not just optimistic pitches about future capabilities.
Calculate Total Costs, Honestly
Entrepreneurs often underestimate true AI expenses. Consider GPU costs, power bills, storage infrastructure, and human oversight. If total AI costs exceed the benefits, step back.
Run Rigorous Tests Before Investing
Never trust AI blindly. Run pilot programs, gather data, and objectively evaluate results. Don’t fall into the trap of assuming today’s issues will magically disappear tomorrow.
Use AI for Augmentation, Not Automation (Yet)
Until AI matures, use it to enhance human efficiency rather than replace human judgment. Treat it as a powerful assistant—not a decision-maker.
Final Thought: AI Isn’t Magic—It’s a Strategic Tool
AI will not replace entrepreneurs. But entrepreneurs who strategically leverage AI will unquestionably outperform competitors who either ignore AI entirely or recklessly adopt it without understanding its limits.
The biggest entrepreneurial mistake you can make today is assuming AI is fully ready when it’s clearly still experimental.
The second biggest mistake? Completely ignoring AI and missing out on its transformative potential.
Remember, AI isn’t magic. It’s merely a sophisticated, powerful tool—valuable only if wielded wisely.
The entrepreneurs who succeed in the AI era will be those who clearly understand AI’s limitations, manage its costs carefully, and integrate it thoughtfully into their broader business strategy.
Those who blindly chase hype without critical thinking will inevitably discover that innovation isn’t about following trends—it’s about solving real, practical problems with measurable outcomes.
Entrepreneurs: approach AI with caution, clarity, and strategic intent. Your business success depends on it.