Top 10 costly GenAI mistakes that could sink your startup

The numbers are mind-blowing. The global AI market is to hit $250 billion by the end of 2025, according to Statista. Venture capital is flooding AI startups at record levels, but not all investments are paying off. To break down where things go wrong, I spoke with Yuval Machlin, former Chief Product Officer at D-ID, who’s helped many startups go through the GenAI boom. Here’s what we found and the most expensive mistakes founders keep making.

Disclaimer: We're not going to teach you how to run your startup. This is just our take on what’s happening in the industry. But hey, maybe it’s time to pause on the GenAI rush and think a bit.

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The ‘GenAI or die’ trap

In the relentless race to integrate generative AI into products, many startups are diving in often without a clear purpose. And this misstep is more common than you'd think. According to the IBM Study published in December 2024, 85% of companies reported progress in their AI strategies, yet only 47% have achieved positive ROI. Oops!

Similarly, a recent report by Boston Consulting Group found that only 26% of companies have developed the necessary capabilities to move beyond proofs of concept and generate tangible value from their AI investments. The takeaway is clear: integrating GenAI without a well-defined purpose leads to wasted resources and missed opportunities.  

Now, let’s get into the real problems. Here are the biggest, most expensive mistakes startups keep making with GenAI.
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Mistake 1. Garbage in, garbage out

GenAI is only as good as the data it’s trained on. Feed them junk, and they’ll spit out junk. Simple. But startups trying to build their own models often lack enough quality data to train them. As a result, they get a system that learns from bad examples and produces unreliable, biased, or totally useless outputs.

Some startups skip the headache by using existing models via APIs, sidestepping the data nightmare. But for those determined to build their own GenAI, high-quality, diverse training data is non-negotiable. For example, if you’re training a model to identify plants, random internet pictures won’t cut it. You need thousands of images in different lighting, angles, and conditions. Sometimes far more, depending on complexity. Otherwise, the model will fail in real-world scenarios. 

Synthetic data is used as a workaround. Microsoft’s Phi models, Google’s Gemma, and Nvidia’s latest AI models all incorporate synthetic data. By 2030, synthetic data could be a $2.34 billion market, but it’s still AI-generated guesswork. Overrelying on it creates a feedback loop detached from reality.
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Mistake 2. No guardrails, no control

AI without guardrails is a lawsuit waiting to happen. Without proper safeguards, AI generates biased, offensive, or harmful content. And the failures keep piling up.

In early 2024, a vulnerability in OpenAI’s DALL- E 3 allowed users to bypass safety filters, producing inappropriate images. This is an embarrassing reminder that even the biggest players struggle to rein in their AI. And a recent report by the Future of Life Institute found that none of the major AI companies have foolproof safety protocols. Every model tested was vulnerable to “jailbreaks” that override built-in restrictions.

Startups using off-the-shelf models believe they’re inheriting the same level of safety as industry giants. They're not. Many open-source models come with zero built-in protections, exposing founders to regulatory backlash, reputational damage, and potential security disasters. On top of that, failing to lock down data is another ticking time bomb. If an AI model is training on user inputs by default – like ChatGPT before OpenAI rolled out privacy controls – startups risk leaking private or proprietary data. And once it’s in the model, there’s no undo button.
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Mistake 3. Ignoring GenAI’s limits

AI models have strengths, but they also have significant weaknesses that can wreck a product if founders don’t test them properly. Many assume a GenAI model will handle any task they throw at it. Need a marketing copy? Just ask the AI. But that’s where things break down. A model might spit out catchy headlines but completely miss the brand’s tone. A chatbot might sound smart in a demo but fail with real customer queries. And more interesting, different models behave differently. Stanford’s 2024 AI Index Report highlights that startups relying on a single GenAI system often hit performance ceilings. Using multiple models in tandem can considerably improve accuracy, but it’s a strategy few founders take into account.
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Mistake 4. High cost at scale

Scaling costs can get out of hand. Generating content isn’t free, and when startups start processing massive amounts of text, images, or video, expenses enter the stratosphere. A single AI-generated image costs significantly more in compute than a text query, and large-scale models drain resources fast.

The numbers don’t lie. Developing a generative AI product can cost anywhere from $5 million to $20 million, depending on model complexity, data processing, and infrastructure. And that’s before factoring in ongoing cloud expenses. According to Epoch AI, the compute cost of training frontier AI models has been doubling every 10 months, putting even deep-pocketed startups under pressure.
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While GenAI unlocks exciting capabilities, many founders forget that cheaper, traditional methods often work fine. Basic automation and rule-based systems can handle tasks that don’t need AI.

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Mistake 5. User frustration

Too many startups rush to integrate AI-powered chatbots, search tools, and assistants without real-world testing. As a result, a frustrating, clunky experience that drives users away.

AI often misreads intent, gives robotic answers, or rambles on when a short response would do. And yet, founders rarely test how real users interact with their AI. They just think it’ll “work.” But when customers get useless responses, they lose trust and patience. A July 2024 Gartner survey found that 64% of customers would prefer companies that didn't use AI for customer service at all. That’s not just bad UX, that’s lost revenue.

Mistake 6. The illusion of stability

Startups roll out GenAI features expecting consistency, but instead, they get unpredictability. Ask the AI the same thing twice, and you might get two different answers. Sometimes the differences are subtle, sometimes they are complete contradictions. 

Yet many founders don’t check for this. They test AI like traditional software, hoping outputs will be stable. But they are not. Stanford research shows that large language models work with neutral topics well but give wildly different answers on anything remotely complex or controversial.

GenAI is not static. It shifts with phrasing, context, and even unnoticed model updates. Ignore this, and your AI will not just be wrong. It will be unpredictably wrong.
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Mistake 7. Legal issues

Just because something is online doesn’t mean it is free to use. Many founders think they can feed any dataset into GenAI without consequences. But is it really?

Take the wave of lawsuits against AI companies in 2024. The New York Times sued OpenAI and Microsoft for using its articles without permission. Getty Images went after Stability AI for allegedly training on copyrighted photos. These cases are not just headlines. They are shaping how AI businesses operate.

Ignoring legal risks can cost millions. Data privacy laws are tightening worldwide, making compliance a moving target. As regulations change, startups flying blind risk everything, including products, funding, and reputation.
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Mistake 8. Overlooking bias until it’s too late

AI bias is a big problem that startups routinely ignore until it fails. Train a model on biased data, and it will reflect those biases, whether that means racial, gender, or cultural distortions.

Researchers at Anthropic tried reducing bias in AI responses but found that doing so sometimes made the model less capable overall. Meanwhile, Google now conducts fairness reviews before launching AI-driven products, recognizing that unchecked bias can damage trust and invite regulatory scrutiny.

The trade-off between fairness and accuracy is messy, and there’s no universal solution. Adversarial training and algorithmic audits show promise, but startups rarely invest in them early. Instead, they wait until users or regulators call them out.

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Mistake 9. Security as an afterthought

Security often takes a backseat until something breaks. In January 2025, Chinese AI startup DeepSeek faced a "large-scale" cyberattack that forced the company to limit new user registrations. This attack coincided with DeepSeek's AI assistant becoming the top-rated free app on the U.S. Apple App Store, leading to significant website outages. 

This isn’t new. AI models have been tricked into leaking internal logic, private prompts, and even personal user information. And many founders don’t check where their AI tools send or store data. Some free API versions quietly reserve rights to user inputs for future training. The fine print? Most don’t even read it.
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Mistake 10. Blind trust in AI results

Startups deploy AI-powered assistants and copilots but rarely question the accuracy of their outputs. AI hallucinations, confident but incorrect responses, happen between 3% and 10% of the time, depending on the model. GPT-4’s hallucination rate is around 3%, while Google’s PaLM 2 reportedly reaches 27%.

Yet many founders don’t validate results before shipping AI-driven products. They think the model "mostly works" and let users figure out the mistakes. In high-stakes industries like healthcare or finance, even a small error rate can lead to serious consequences. And users won’t forgive it.
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Wrapping up

Is GenAI a revolution? Absolutely! Is it helpful for many industries? Without a doubt. But is it a magic bullet for everyone? Not really. Before you incorporate GenAI into your products, take a step back and think about your users first. Will it solve their problems? If the answer is yes, then move on. Otherwise resist following hype blindly.