Builder.ai's $450M collapse: A cautionary tale for AI founders

Charlie Munger’s Inversion: Seeing the Coming AI Chaos Before It Hits Charlie Munger often said: “Invert, always invert.” If you want to succeed, first figure out how to fail. Right now, the business world is drowning in AI hype. Every vendor promises transformation, every startup claims disruption. But let’s invert: What if the real story of AI is not just the breakthroughs, but the breakdowns? Deepfakes fueling industrial-scale fraud. Hallucinations embedded into decision systems making mistakes at speed. Compliance teams buried under a flood of AI-enabled SaaS tools they can’t govern. The unintended consequences of embedding GenAI into every business system will be chaos before it is clarity. And yet—inside that chaos lies the real opportunity. The leaders who prepare for AI’s second-order effects, not just its promises, will be the ones who shape the next decade. This is the lens I’m bringing to my work with technology and financial leaders: not chasing the hype, but preparing for the risks, so AI becomes a competitive advantage instead of an uncontrolled liability. If this resonates, let’s connect. The conversation about AI’s unintended consequences is just beginning.

Is AI in a bubble? A rigorous new analysis says no, but there are risks we should watch closely. Given the hundreds of billions being spent on data centers, and the hundreds of billions at which AI startups are valued, it’s easy to think AI is in a bubble. On the surface, this feels like the dot-com bubble, which I lived through. If we compare the current AI boom with past bubbles, however, and create gauges for key bubble components, it becomes clear that AI is different: ◾️ Most money being spent on data centers isn’t borrowed from banks and citizens, which created systemic risk in past bubbles. Instead, it’s largely funded by huge, profitable companies. ◾️ While the capex-to-revenue ratio is higher than in past bubbles (roughly 6X today vs. 2X for railroads and <4X for telecoms), rapid revenue growth offsets this. Generative AI revenues, currently estimated at $60 billion or more, are at least doubling every year. Some estimates project 4–5X growth for model developers next year. ◾️ While valuations seem high, they’re nowhere near dot-com territory. The Nasdaq P/E is about 32 today versus 72 at the 2000 peak. Even fast-growing companies like OpenAI and Anthropic are valued at revenue multiples of ~40X and ~36X, respectively. High, but far below the triple-digit levels seen in 2000. So, it’s fair to say AI is heating up, and we should watch for signs of a bubble. But it doesn’t seem like we’re in one now. We should be vigilant, however. Specifically, we should track whether AI infrastructure build-outs exceed 2% of GDP, whether revenue growth slows, whether valuations push into lofty P/E ranges (50–60+), or whether funding shifts from hyperscaler cash flows toward riskier debt and securitization. But as of right now? No, we’re not in a bubble. We’re in the midst of an unprecedented technological revolution. Yes, it requires high capital expenditures. Yes, it's rewarding companies at the center with high (but not historically high) valuations. But all of this is paired with extraordinary revenue growth driven by the technology's impact and potential. This is, of course, a distilled summary. Definitely take a look at the full article, linked in the comments.

https://lnkd.in/d6tZf7-Y VC investor Orlando Bravo warns: The AI craze may pose a bubble risk. He noted that while AI technology holds enormous potential, market valuations and investment enthusiasm may be outpacing the actual pace of adoption. Rational assessment and long-term value will be key to navigating these cycles. #AI #VentureCapital #Investment #TechTrends #OrlandoBravo

A new buzzword is hanging over businesses as they rush into AI - CNBC: The report outlined that the debt could manifest as security risks, poor data quality, low impact AI agents which will waste time and resources for ...

"'AI debt' could manifest as security risks, poor data quality, low impact AI agents which will waste time and resources for human employees, and a management skills gap..." https://lnkd.in/gQh8Eirz

❓ AI Bubble Debate: Is It Real or Hype? Few days ago, JPMorgan CEO Jamie Dimon sparked debate by warning that parts of AI look bubbly, even though the technology itself is real and valuable. He says AI will create lasting winners and real profits, but too much money is rushing in, so many projects and companies will likely fail along the way. Arguments from experts that it can be a bubble: ✔️ Too dependent on a few giants: A small group of AI-linked giants powered most of 2025’s market gains, so any slowdown in data center building or earnings could hit markets hard. ✔️Big spending before results: Tech giants are pouring hundreds of billions into chips and data centers, raising worry that capacity is being built faster than revenue arrives. ✔️Caution from leaders: High-profile voices, including Sam Altman and major bank CEOs, warn parts of AI are overheated and could face pullbacks. ✔️Little proof of broad productivity yet: Despite huge AI investment, overall productivity gains look modest so far, suggesting expectations may be ahead of real-world impact. ✔️Startup froth: The riskiest excess is in young companies without strong business models, where valuations can be driven more by hype than by solid economics. Arguments that value is real: ✔️Real revenue is growing: Major AI companies are reporting growth driven by enterprise customers, not just demos or hype. ✔️Built on real infrastructure: This wave is grounded in physical assets - chips, data centers, networks - so it’s less about vibes and more about capacity that can power future services. ✔️Scale in context: Some research says today’s AI spend, while big, is still reasonable versus past tech buildouts relative to the economy, leaving room to grow before true excess. ✔️Long-run productivity upside: AI looks like a general-purpose technology that can automate work and lift margins over time, which can justify heavy early investment. ✔️Mixed, not manic: Even skeptics note parts of AI may be overhyped while the overall trend likely endures, pointing to a selective shakeout rather than a total bust.

The world's most powerful financial institutions just issued a stark warning AI valuations echo the dot-com bubble The Bank of England and the IMF see a "sharp market correction" on the horizon. The data paints a worrying picture: → Historic Concentration: Just 5 companies now make up 30% of the S&P 500's value. → The "Rhyme" with History: Market leaders acknowledge current patterns echo previous bubbles. → The ROI Reality Check: A recent MIT study found 95% of organizations see zero return on their generative AI investments. This is not a prediction of if AI will transform the world. It is a warning that the financial market has gotten ahead of the operational reality. For a COO, this changes the game. Our role shifts from chasing hype to demanding substance. The question is no longer "How can we use AI?" The critical question now is: "Where does AI drive tangible, measurable value in our operations?" The companies that survive the coming shakeout will be those who built operational moats, not just placed speculative bets. They will have: - Clear ROI frameworks for every tech investment. - Efficiency gains that directly impact the bottom line. - Sustainable business models that do not rely on circular financing. This is a moment for operational discipline over blind optimism

The AI Litmus Test: 5 Strategic Questions Every CEO Must Answer Before Automating Core Operations The buzz around AI is deafening, but successful adoption isn't about the technology—it's about strategic logic. Many companies rush into automation pilots only to face integration failures, soaring costs, or new risks. Before you invest another dollar, every CEO must pass the Dream LogicX AI Litmus Test by answering these five non-negotiable questions: - Strategic Alignment: Does this initiative directly support our top 3 corporate objectives for the next three years? Avoid AI for AI’s sake. - Quantifiable ROI: What is our calculated "Cost of AI Inaction," and what's the guaranteed return on investment (ROI) if we succeed? Demand rigorous financials, not just promise. - Human Loop Blueprint: How will this automation impact our workforce? Have we defined the new skills required and the human oversight necessary to maintain accountability and trust? - Data Integrity & Risk: Do we have robust governance to manage data security, bias, and privacy? AI is only as good as the data you feed it. - Scaling & Agility: Is our existing technology architecture and organizational structure flexible enough to support continuous AI evolution and rapid scaling? Failing to answer even one of these questions is a fundamental failure of business logic. AI should be a strategic asset, not a technological liability. Ready to apply the AI Litmus Test to your roadmap? Contact Dream LogicX for a strategic readiness assessment and a clear path to value.

AI isn’t a magic wand—it’s a mirror. If your business logic is broken, automation will only amplify the cracks. These 5 questions aren’t optional—they’re survival-grade.”

The AI Value Gap Is widening and organizations, are at a crossroad! Through my work with financial institutions, it’s clear that there’s a big difference in how organizations are building their future with AI. Some are still testing the waters, while others are embedding AI into the core of their business unlocking real, measurable impact. Boston Consulting Group's recent article highlights this aswell: Only 5% of companies are actually “future built” for AI and the possibility to achieving significantly higher revenue growth and cost efficiency. 📖 Read the article here: https://lnkd.in/dZvst_Pk At SAS, we’ve never treated AI, Data and Analytics as a side project, and want our customers to be part of the 5 %. Next year SAS have 50 years anniversary as a company that helped institutions turn data into decisions, whether it’s in risk, compliance, fraud, or customer intelligence. In SAS we have written a report called From Algorithms to Impact: Banking’s AI Future, exploring how banks can move from pilots to performance especially as agentic and generative AI reshape the landscape. 📖 Read more on the future of AI in Banking here: https://lnkd.in/d95VytGx 💡 The real question isn’t whether you’re using AI. It’s whether you’re unlocking its full potential. If you’re seeing some of the same trends or curious about how we at SAS are unlocking real value trough AI and Analytics drop me a message.