A Unicorn, or a Horse in a Party Hat?

Unicorn startups that turn into the next Google are about as rare as actual unicorns.

But cautionary tales about wannabe unicorns?

Just ask Hollywood. The fraud-to-TV pipeline is booming. The fall of Theranos has been retold in The Dropout starring Amanda Seyfried. WeWorks got WeCrashed, with Jared Leto and Anne Hathaway playing its cofounders. And the meteoric, highly publicized fall of Sam Bankman-Fried’s FTX is fueling competing projects at Netflix and Prime.

Forget Marvel; fraud has its own cinematic universe, with villains wearing Patagonia vests. And sadly, there’s no shortage of material.

Just look at the implosion of social media app IRL, where 95% of its ‘users’ were bots. Founder Abraham Shafi has recently been accused of defrauding investors out of $170 million, some of which funded a luxury wedding in Hawaii and art classes.

In recent months, we’ve also seen the collapse of Builder.ai, once touted as one of the world’s hottest AI startups. Its CEO Sachin Dev Duggal, self-titled ‘chief wizard,’ raised more than $500 million from investors on promises to revolutionize app development with AI. Turns out that in addition to inflating revenue figures, Builder.ai’s AI chatbot ‘Natasha’ was powered by 700 human engineers. That’s the equivalent of using digital sweatshops.

Here’s a helpful tip: Never trust a man who unironically calls himself a wizard.

Why do we keep falling for it?

In hindsight, the lies told by these companies seem so bold-faced. How could investors have ever fallen for them? And yet, they do. Time and time again.

A unicorn is commonly considered a startup valued (on paper, at least) at a billion dollars. Valuation in this context is typically based on a combination of slide decks, investor hype, and FOMO.

That number is rarely (if ever) a true reflection of solid financials (as more traditional measures are based). It can easily be inflated by a market that’s overly eager to find the next big thing. This is why some of the most exciting companies getting your attention are also at risk of being overhyped.

Sadly, the frenzy around unicorn spotting is far from over. The number of unicorn startups has swelled to over 1,500 as of last month, compared to around 140 in 2015.

But some experts argue that the unicorn boom, which peaked in 2021, is now showing signs of fading.

Historically, startups raising $100 million or more before going public have actually performed worse after listing. Why? Because too much easy money early on creates bad habits: weak governance, sloppy cost discipline, and a culture that prizes growth over sustainability.

Only a small fraction of unicorns are actually profitable. Most rely on a steady drip of investor capital to keep the lights on, and with funding drying up, many face a looming reckoning.

Enter: The AI Gold Rush.

The foibles of unicorn hunting are clearly seen in the latest investor obsession: Generative AI.

Investors are throwing their cash at anything with AI attached to it (sound familiar?). In 2024, about 40% of the capital raised by US VC funds was allocated to funds focusing on AI. Seed valuations for startups in AI were 42% higher than those that weren’t.

With numbers like these, it’s easy to see why some startups are tempted to exaggerate their AI capabilities. (And by the way, you should resist the urge of padding your résumé on LinkedIn with ‘fluent in Python’ if all you’ve done is watch a single YouTube tutorial).

Builder.ai is just the latest chapter in the rise of ‘AI-washing,’ where companies are promoting their services as using AI to attract funding.

But AI isn’t a magic horn.

The use of AI in business is pretty commonplace today. According to Stanford University’s 2025 AI Index Report, 78% of organizations reported using AI in 2024, up from 55% in 2023. At the same time, recent reports have also found that 95% of organizations see no measurable return on their investment in these technologies.

That’s because many businesses are using AI tools to create low-effort work, affectionately called ‘AI slop.’

You’ve almost certainly come across a TikTok video with an eerie voice narration that sounds just shy of being human, or a  LinkedIn essay on B2B sales that reads like an unedited ChatGPT answer. Just last month, a survey published in Harvard Business Review found that 40% of US employees have received workslop in the past month. Its authors argue that AI slop actually transforms into a productivity tax in the workplace.

The same skepticism also applies to some of the US’s biggest companies, where executives will talk a big AI game in earnings calls but in reality can’t guarantee that AI will boost profits (or that it will even be adopted by employees beyond pilot programs).

So, what does this all mean? AI promises to be transformational for a business, but only when it’s used as more than a corporate buzzword.

Here’s how you weed out the horses wearing AI party hats.

For starters, put aside any soft marketing language like “powered by AI.” You need to question what models, algorithms, or code libraries the product in question uses and push for real answers.

Make sure you assess how intuitive the product actually is, outside of well-oiled demos.

Verify how easily the product is integrated into existing processes. Is it easy to set up, or is a lot of engineering manpower needed to get it up and running? In other words, is there real AI wizardry involved, or is the whole show actually powered by interns?

The moral of the story here? Just because something looks, sounds, and trots like a unicorn doesn’t mean it’s not just a horse dressed up in a fancy party hat.

Due diligence and good old-fashioned skepticism are more critical than ever in any investor’s tool kit.

©2025. DailyDAC^TM, LLC d/b/a/ Financial Poise^TM. This article is subject to the disclaimers found here.

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