How Not To Die In 2025: Advice From the Graveyard of Failed AI Startups

December is tech’s confessional booth. After 11 months of chest-thumping, bravado, and FOMO bait, this is the time we collectively downshift, trading in swagger for soul-searching—preferably over gingerbread cookies and eggnog spiked just enough to make honesty unavoidable.

I’ve spent a few festive moments with the ones who know: investors with eyes like spreadsheets, founders whose big ideas ended up on the business end of a burn rate. Between crumbs and confessions, they offered me their unvarnished, rum-infused truths.

This is my early Christmas present to you: how to keep your AI startup breathing in 2025.

Here’s hoping we’ll make it to next December still standing, still building, and, ideally, still sipping eggnog.

Kill Surveys And Focus Groups

You’ve heard it before: go on [insert platform of choice, mine’s Reddit], find relevant groups and communities to infiltrate, post some sort of white paper about your product, get feedback, tweak product, launch. The textbook way to validate any prototype. Well, that hardback should be set on flames.

People are one (or all) of three things: 1) nice 2) indifferent 3) inclined to sabotage when given anonymity. They will try to please you, flat out ignore you, or send you on a wild goose chase because they had a bad day at the office. Asking for feedback when users got nothing on the line is like expecting honest answers in a game of "Truth or Dare" at a frat party.

What you should do is spend a couple days hashing together an MVP bound together with a few scripts then plunking it on ProductHunt. If PH results show promise, flesh out the idea, then build a basic landing page for your next iteration, and run $100 worth of ads. This is what crowdfunding agencies do to gauge the potential success of projects that want to launch on Kickstarter or Indiegogo. But the key here is to use the $1 reservation funnel tactic. People can, and will, lie with their words—but they won’t with their cash.

Here’s how it works:

• Build landing page with overview of your product
• Create “Get Early Access” CTA that redirects to a payment page, where users need to place a $1 fully refundable deposit down to be granted VIP access when you roll out. The payment hiccup will deter a lot of people, but it ensures the people you do haul in can provide value.
• Collect emails along with the deposits
• Email these users with a link to enter a beta group (use whatever you want, though I like Discord)
• Use this beta group to get feedback, iterate, crowdsource feature ideas etc.
• 
  

Your AI Doesn’t Need to Be Smarter

The smartest technology in the world means nothing if it can’t speak the language of the people it’s designed to serve. An AI that’s 10% less accurate but 10x more intuitive will win more customers. Always. Optimize for adoption, not just precision. Might make your job a whole lot easier.

Take Shyp, for example—the Silicon Valley darling that promised to supercharge shipping with advanced AI logistics. While Shyp's backend was a technological symphony, the front-end user experience was a cacophony of confusion. To benefit from the algorithmic brilliance, users had to abandon everything they knew about sending packages. Instead of the familiar ritual of boxing it up and dropping it at the post office or scheduling a pickup, Shyp required you to navigate an intricate app, decipher their unique process, and adjust to their logic. This disconnect proved fatal, and in 2018 Shyp sank.

Your AI doesn't need to break into MENSA, but it needs to break into habits.

The goal isn't to make users marvel at your AI's intelligence—it's to make them forget there's AI involved at all.

Fail Fast on the Data Side

9 out of 10 advice blogs will tell you proprietary data is the only thing that’ll guarantee your AI longevity and competitive edge. Yeah…this is only applicable if you meet these two criteria: 1) your concept has been validated by the market 2) you’ve actually launched and got users.

Quantopian, once the hotshot of algorithmic trading, learned this lesson the hard way. They wanted to empower individual traders with sophisticated tools for creating and testing trading strategies, and decided to spend years and mucho dolares perfecting a proprietary data ecosystem worthy of Jain Street. But by the time their product was ready to scale, the market had moved on, and competitors offering simpler, faster solutions ate their lunch. Quantopian burned through its funding, only to shut down in 2020, leaving behind a cautionary dividend: data perfection is a dream; iteration is survival.

Just test your AI on the data that’s already out there—public APIs, scraped datasets, appendices from research papers, as long as you’re getting it legally anything goes. Let the results guide whether investing in custom data is even worth it. Besides, you can launch and keep building up that dataset with the proprietary data from your own users.

Don’t Tech Up Too Early

Would you wear a tuxedo to your own backyard barbecue? Didn’t think so. Same logic here, you don’t need fancy AI/DS development and federated learning when you’re not on TechCrunch’s radar.

Remember Aria Insights, that AI drone company with a name better suited to adtech? They doubled down on cutting-edge AI analytics that would “remove humans from unsafe situations”, betting everything on expensive, high-tech solutions before they had paying customers. Aria was so caught up in the vision they didn’t even realize the lack of omnipresent wifi would be an insurmountable bottleneck. Anyways, they gnawed through cash faster than their drones could take off, ultimately shuttering operations in 2019.

So, just build something ugly, basic, but functional—like a web interface or a simple app. Do all the heavy lifting manually: think of it as artisanal algorithm training, like craft brewing but with code. Recommendations? Hand curate them. Forecasting? Use a spreadsheet. Machine learning? Rain check.

Survive before you scale. Survive before you suit up.

Once you’ve got actual users and a proven concept, then treat yourself to some neural networks that early investors can brag about.

Stop Acting Like You’re Legit. You’re Not.

If your startup is a two man show with an office split between a studio apartment and the SBUX down the alley, DO NOT shoot yourself in the foot by insisting adherence to Fortune 500 “best practices”. All this does is hinder speed, limit ingenuity, and brew headaches.

For example: cut the “I need someone full-time” crap. You’re small, you’re risky, and though you might be the next Altman most people care more about making mortgage next month than paving the path of progress. Go on Fiverr or Upwork and just find someone to code whatever you need, when you need it. (I strongly recommend that you hand the same task to at least 3 freelancers. Odds are, one will deliver something decent. Stick with that one, then scale it up.) Doesn’t matter if they’re in a village in the Philippines. Go global-it might actually be cheaper.

Not Spending On Marketing Doesn’t Mean No Marketing

Paid acquisitions are BAD for early stage startups. When you start paying for installs, you’re inevitably dragged into the Tartarus pit of CTR/CPI/ROAS/CAC optimization, attention diverted from the real debacle you need to address: do you have a viable product?? Please, don’t touch paid until you’ve secured at least one round of funding.

Marketing might be missing from your OpEx, but you’ll be damned if it’s missing from your meeting agenda. Analogy: If you want to lose weight, you don’t need a paid gym pass, you just need to find a way to burn calories. But if you don’t get off your a**, that weight’s not budging. Same rule applies here; no marketing, no minions. YOU JUST DON’T NEED TO HAVE A BUDGET. There are so many free (or free-adjacent) methods to get people to try your product: try stalking LinkedIn, laying siege to industry events or trade shows, or squatting in Reddit/Quora/forums to start.

Doesn’t stop at UA. When you overlook marketing, you fall prey to some really amateur flops: not optimizing your site for SEO or mobile, trusting GPT to write your copy, churning out generic emails to leads, and you get the point. Not saying you need Jony Ive to make you a brand VI, but at least make it look like you care.

I was talking to a founder friend working on his third venture the other day, and here’s an anecdote for you: “In retrospect, we were asinine. We ignored marketing to the point we had a customer support email that no one checked. Once I couldn’t figure out why no one was using this new feature we’d spent months developing, went off on a whim and checked the inbox, then realized we had nearly a thousand emails from new sign ups complaining there was an issue with our landing page.“ Oops.

Too Much Focus Can Be Fatal

Let’s talk about DeepGlint, a Chinese AI computer vision startup founded in 2013 that Gates himself dubbed “very cool” a decade ago. The first AI stock on the Shanghai Stock Exchange STAR Market, the company that once reached peak market cap of over $1 billion in 2023 is now struggling to make ends meet, with its founder recently stepping down amid irreconcilable losses.

Did they have $$$? Lots. Brains? Ivy League, MIT, Stanford, Google, NVIDIA, Tencent… Most likely. What happened? They focused so much on their ideals they lost focus of why they existed: to solve real problems.

DeepGlint initially aimed to use its AI to monitor consumer behavior in brick-and-mortar stores, offering data analytics packages to store owners. But they were tone-deaf to China’s seismic shift toward e-commerce. The country’s brick-and-mortar retail was already stagnating in 2012—store openings by the top 100 chain enterprises had dropped to their slowest rate in a decade. DeepGlint somehow missed this trend entirely—perhaps so deep in code no one had time to read the news?

When the brick-and-mortar dream crumbled, DeepGlint pivoted to security and surveillance. Strike two: they were hell bent on programming their version of the ideal solution. Company insiders revealed that management had next to zero understanding of the sector, a lot of hubris, and a weird conviction they knew better than clients. Well, they ended up with 1 client contributing to 80% of their revenue.

Moral of the story: brains and bucks won’t get you billionaire status if your eyes are closed and your ears are shut.

Treat AI Models Like Fast Fashion

AI models have a shelf life that’s about as long as a box of Cheerios. Brilliant today, bargain-bin in Q3. Ensure you’re building your infrastructure in a modular manner, enabling you to swap and plug in newer models with minimal friction and maximum speed.

Take Zymergen, a biotech startup that raised over $1 billion to revolutionize material science with AI. Their Achilles' heel wasn’t ambition—it was rigidity. Instead of building a flexible architecture that could easily integrate updated models or emerging techniques, they locked themselves into immutable, first-generation AI systems that turned their cutting-edge promise into a clunky relic, ultimately leading to their collapse.

AI isn’t a one-and-done game. Models and algorithms aren’t monuments—they’re sandcastles, and the tide isn’t governed by the moon.

Don’t Forget About…AI

Stress does funny things to your head. You’re so busy building AI to solve everyone else’s problems that you forget the obvious: AI can solve yours too.

You think building progress requires fancy tools, but see if things can be done just with generative AI. With a hundred bucks and an API call, you could technically achieve something that would have cost millions to do in the BC (Before ChatGPT) era. For example: These guys explored a cure for a rare disease with $50.

Use GPT to save your breath. Don’t spend hours on Zoom telling a dev what you want. Have a 10 minute brainstorm with ChatGPT and lay out a clear spec. Share that with the dev, and spare yourself the back-and-forth. Get GPT to crank out 90% of the code so the humans just have to finish up the last 10% and do some tweaking. Not saying you should take mortal brainpower out of the equation, but save human ingenuity for the last mile.

Cutting corners isn’t cutting quality.

Every dollar you save and every hour you reclaim is fuel for the Route 20. Stop sweating over what AI can do for others and start asking what it can do for you. Your stress levels—and your startup—will thank you.

Final Thoughts: It Won’t Be Pretty

Nike’s principle #9—“it won’t be pretty”—needs to be your mantra, and your warning.

The path you’re on is ugly, unforgiving, and doesn’t care about your vision board.

This isn’t a game of aesthetics or the clean lines of “best practices.” It’s about not going extinct. Survival doesn’t look like a hoodied tech bro pacing on a spotlit stage. It looks like systems held together by freelancers in three time zones, a landing page bound by duct tape and prayers, and decisions you’ll barely remember making because you were too busy making sure your MVP could limp its way into the hands of your first paying user.

There’s no grade for style, only points for persistence. This is a knife fight in a dark alley, and the winner isn’t the one who looks best—it’s the one who’s still got a pulse.

Make it that far, and you won’t just be pretty: you’ll be god damn beautiful.