TechCrunch’s StrictlyVC evening in Los Angeles late last week brought together two of the more straight-talking investors working in AI right now. They were as entertaining as they were illuminating. And thank God for that. Because in a market that’s moving faster than a teenager’s attention span, we need someone to cut through the bullshit.
“Everyone is lying to you”
The first speaker, a managing partner at a top-tier venture firm, didn’t bother with pleasantries. “Everyone is lying to you,” she said, scanning the room. “Startups, analysts, even your own gut. The only thing you can trust is the data — but even that can be faked if you look at the wrong metrics.” She wasn’t joking. Her firm now runs every pitch through a proprietary model that cross-references founder claims with public filings, hiring data, and social media sentiment. “If someone tells me they have 10,000 users, I want to see their churn rate, their engagement per user, and how many of those users actually pay. Anything less is a bedtime story.”
Her co-panelist, a former engineer turned angel investor, nodded grimly. He told a story about a startup that claimed “explosive growth” in its Series A deck. “I dug into their GitHub. Turns out their ‘explosive growth’ was a bunch of bots pinging their API. Real users? Maybe 200. They’d raised $5 million on a lie.” The room went quiet. Then he shrugged: “That’s the game now. You either do the homework or you lose your shirt.”
“The only thing you can trust is the data — but even that can be faked if you look at the wrong metrics.”
Speed kills — unless you have a system
Everyone talks about speed in AI investing. Deals close in days, not weeks. But both investors agreed: speed without discipline is just gambling. “I see these young VCs bragging about making decisions in 72 hours,” said the managing partner. “I’m like, congratulations, you just bought a lottery ticket. We take three weeks minimum, and we still miss things.”
Her firm uses what she calls a “ladder of conviction” — a four-step process that starts with a cold read of the deck and ends with a full technical audit. “If at any point we find a crack, we pause. We don’t accelerate. The market will still be there next month.” She paused, then added: “Most of the time, the crack is in the founder’s story. They’re selling a vision that doesn’t match reality. And the faster you move, the easier it is to miss that.”
The angel investor chimed in with his own rule: “I never invest in a company where the founder can’t explain their unit economics in one sentence. If they start talking about ‘ecosystems’ or ‘synergies,’ I’m out. That’s code for ‘I don’t know how we make money.’”
The AI hype cycle is real — and dangerous
Both investors were brutally honest about the AI hype cycle. “We’re in the peak of inflated expectations,” said the managing partner. “Every other startup claims to be ‘AI-first.’ Most of them are just using GPT wrappers and calling it innovation.” She warned that the coming correction will be brutal. “A lot of these companies will die. The ones that survive will have real moats — proprietary data, unique hardware, or a distribution advantage. Not just a pretty demo.”
The angel investor was even blunter. “I’ve seen at least 50 startups this year that are basically the same product: a chatbot with a different skin. They’re all racing to the bottom on price. That’s not a business. That’s a hobby.” He advised retail investors to avoid the hype. “If you’re buying AI stocks right now, you’re not investing. You’re speculating. The winners won’t be clear for another three to five years. And by then, most of the current darlings will be dead.”
“If you’re buying AI stocks right now, you’re not investing. You’re speculating.”
What they’re actually betting on
So what do the pros put their money on? The managing partner said she’s focused on infrastructure — the pipes that power AI, not the apps. “We’re betting on compute, data centers, and specialized chips. That’s where the real value is. The application layer is too crowded and too easy to copy.” She also likes companies that solve the “last mile” problem — getting AI out of the lab and into real-world workflows. “Anyone can build a model. The hard part is getting a hospital or a factory to actually use it.”
The angel investor prefers vertical plays. “I’m investing in AI for agriculture, for logistics, for legal. These are industries with deep, messy problems that generic AI can’t solve. You need domain expertise and a willingness to get your hands dirty.” He gave an example: a startup that uses computer vision to detect crop diseases in real-time. “That’s a product with a clear ROI. Farmers will pay for it because it saves them money. Compare that to some consumer chatbot that’s trying to get you to subscribe for $10 a month. Which one do you think has a future?”
The takeaway for normal people
The evening ended with a Q&A. Someone asked: “What should the average person do with their money right now?” The managing partner’s answer was short. “Nothing dramatic. Don’t chase the AI hype. Keep your cash in index funds, maybe allocate a small percentage to venture debt if you’re feeling bold. But the biggest mistake you can make is thinking you need to act fast. You don’t.”
The angel investor agreed. “The best investment you can make right now is in your own education. Learn how these technologies actually work. The people who understand the fundamentals will be the ones who profit. Everyone else will just be noise.”
And with that, they walked off stage. No handshakes, no platitudes. Just two people who’ve seen enough cycles to know that the only thing worse than missing out is getting burned.
