The AI bubble: Are we repeating the dot-com mistake?

I recently read an article on the Federal Reserve’s website about AI and its ramifications on US Economy. Here is my take away from it. AI is no longer hype. It’s scale. → ChatGPT went from 500M users in March to 800M in October → Nearly 1 in 2 American professionals now use AI at work → Entire industries from customer service to drug discovery are being rewritten But what stood out most wasn’t the buzz. It was how Vice Chair Philip Jefferson framed AI’s impact using the Fed’s dual mandate: 1. Maximum employment 2. Price stability Here’s where it gets interesting... AI doesn’t just replace jobs, it redefines them. Yes, automation will disrupt certain roles. But the productivity boost is real. → In one study, AI increased support agent efficiency by 14% → For less experienced workers, the gains were even greater This means two things: AI could create economic slack by replacing some workers. But if we redeploy talent effectively, we unlock exponential growth. And while productivity might ease inflation by lowering costs, the infrastructure needed to power AI (data centers, compute, energy) could just as easily add upward pressure. Translation? AI will change everything, but not in predictable ways. As investors, especially those focused on exits, this presents both risk and upside: ✅ Early adopters will see cost and speed advantages ✅ Sectoral winners (e.g., logistics, biotech, professional services) will emerge ✅ But we need to watch for mispricing due to inflated growth assumptions or over-indexing on tech-led margin expansion We’re already seeing founders pivot models to be "AI-powered" without clarity on how it materially changes unit economics. That’s noise. I’m focused on signal. Here's what I’m asking myself: → Is AI truly improving productivity or just automating noise? → Can the company defend its moat once others adopt similar tools? → Will the labor it displaces be redeployed to higher-value work, or lost? In a world of structural change, we can’t rely on old assumptions. We need sharper due diligence, deeper operating insight, and a real POV on how tech drives ROI, not just valuation hype. If you're a founder, investor, or operator thinking about how AI fits into your scaling or exit strategy, let’s connect. The world is shifting. Those who understand how to scale amidst change will define the next decade. What’s one way AI has actually improved your team’s productivity, not just your pitch deck? Drop it below. 👇 Read the article here: https://lnkd.in/gs2VbVvq Tagging Dina Ellis Rochkind - Paul Hastings Counsel Govt. Affairs and Arjun Shrivastava, curious to hear your take on how AI is showing up in your world. #PrivateEquity #AIProductivity #ExitDrivenInvesting #GenerativeAI

With all the press surrounding a potential “AI bubble,” this is an interesting article. It highlights that many AI firms and start-ups already have real business models and revenue behind them. It is still early days, so much of what we see is experimentation as companies figure out what works. As with any new industry, some businesses will succeed and many will fail. It is an exciting moment. The technology continues to improve quickly, and every major technology evolution moves through similar stages before long-term winners emerge. What do you think? Are we about to see the bubble burst? If so, what would that mean?

AI a bubble and a breakthrough(bonanza). Today's constant fretting about whether frothy tech stocks are in a bubble is rooted in fears that AI may not live up to the hype and that business returns will therefore never justify the blistering investment we're seeing on advanced chips as well as the infrastructure and energy needed to support AI's vast processing needs. If the technology endures and ultimately transforms the world, some of the priciest stocks from the bubble years can still be left high and dry. The companies may not go bankrupt, but over-eager valuations can mean their share prices take years to recover - if they ever do. It's impossible not to compare today's valuation frenzy to the 1990s dotcom bubble in tech, media and telecom stocks that burst spectacularly in early 2000. https://lnkd.in/eR__gaJD. Not everybody uses 'The innovator's dilema' correct. The "Good Enough" Revolution The late Harvard professor Clayton Christensen called this phenomenon the "innovator's dilemma." His theory of disruptive innovation doesn't describe new technologies that are immediately better than existing ones. In fact, it’s the opposite. Disruptive innovations almost always start out as inferior when measured by the performance metrics that matter to the most demanding customers. They don’t win by stealing the incumbent's best clients. Instead, they take root by creating new markets of people who were previously non-consumers or by serving "overserved" customers at the low end of the market with a simpler, cheaper product. For this initial audience, the new technology is, plain and simply, "good enough." book:The Innovator's Dilemma https://lnkd.in/epSxfhZs

Many people are debating whether “AI is a bubble.” My thinking: yes and no. A few days ago, I rewatched The Big Short — and it reminded me why I had already sold my Nasdaq positions. The market feels euphoric, and valuations often move faster than reality can deliver. But let me be clear: I am convinced of the long-term, transformative impact of AI on every dimension of business and society. We just need to distinguish between the financial market cycle and the technology curve. I think markets often move faster than reality can deliver. Even if the financial bubble bursts — and it probably will — the underlying transformation will continue to compound quietly: • Productivity gains from AI will reshape how organizations create value. • Entire workflows and professions will be redefined around new human–machine collaboration. • Competitive advantage will shift from owning data to orchestrating intelligence. The market may correct. The technology will mature. #AI #Leadership #DigitalTransformation #AITransformation

Everyone’s laughing at the “AI companies investing in each other” carousel, but they’re missing the real story. The bubble is real. The circular money flows are slightly absurd. And yes, the economics look a bit like a late-90s dot-com rerun. But here’s the nuance everyone conveniently forgets: when the dot-com bubble popped, the 𝘪𝘯𝘵𝘦𝘳𝘯𝘦𝘵 didn’t die - the hype machine did. And out of that wreckage came Google (1998), eBay (1995), Amazon (1994). The bubble burst didn’t kill the technology; it reshaped the industry and crowned new market leaders. We’re heading toward the same pressure test in AI. Mega-models consume billions, attract headlines, and still fail to show sustainable economics - especially when Bain is projecting the industry will need $2 𝘁𝗿𝗶𝗹𝗹𝗶𝗼𝗻 𝗶𝗻 𝗮𝗻𝗻𝘂𝗮𝗹 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 𝗯𝘆 2030 just to power future compute demand. Meanwhile, 95% of AI pilot projects can’t produce meaningful results, according to MIT. That’s not a tech failure - that’s a product and expectations failure. And while every startup pitches their “ChatGPT-but-for-X,” smaller domain-specific systems are quietly winning. Austin, Texas already uses an on-premise local model for building permits: faster, cheaper, and not hostage to a remote model’s pricing whims. 2025 should’ve been the year of exponential model jumps - instead, it exposed diminishing returns. Scale alone isn’t saving anyone. This creates a brutal career filter. If your value is “I know how to prompt AI,” you’re replaceable. If your company 𝘪𝘴 AI, with no path to revenue, you’re exposed. But if you can wield AI without depending on it - validate, integrate, architect around failure modes, and fall back to non-AI logic when needed - you become the person companies keep when the bubble air hisses out. 𝗪𝗵𝗮𝘁 𝘀𝗲𝘁𝘀 𝘆𝗼𝘂 𝗮𝗽𝗮𝗿𝘁 𝗶𝘀𝗻’𝘁 𝗵𝗼𝘄 𝘄𝗲𝗹𝗹 𝘆𝗼𝘂 𝗽𝗿𝗼𝗺𝗽𝘁 𝗮𝗻 𝗟𝗟𝗠 - 𝗶𝘁’𝘀 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘁𝗵𝗮𝘁 𝘀𝘂𝗿𝘃𝗶𝘃𝗲 𝗮 10𝘅 𝗔𝗣𝗜 𝗽𝗿𝗶𝗰𝗲 𝗵𝗶𝗸𝗲 𝗼𝗿 𝗮 𝘀𝘂𝗱𝗱𝗲𝗻 𝗺𝗼𝗱𝗲𝗹 𝘄𝗶𝘁𝗵𝗱𝗿𝗮𝘄𝗮𝗹. The next Google-class winners are already being built by people who treat AI as a tool, not an identity. 𝗧𝗵𝗲 𝗯𝘂𝗯𝗯𝗹𝗲 𝘄𝗶𝗹𝗹 𝗯𝘂𝗿𝘀𝘁. 𝗧𝗵𝗲 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆 𝘄𝗼𝗻’𝘁. Are you building something that survives the correction - or something that depends on the bubble staying inflated?

𝗔𝗿𝗲 𝗪𝗲 𝗶𝗻 𝗮𝗻 𝗔𝗜 𝗕𝘂𝗯𝗯𝗹𝗲? 𝗔 𝗣𝗹𝗮𝗶𝗻-𝗘𝗻𝗴𝗹𝗶𝘀𝗵 𝗧𝗮𝗸𝗲 𝗳𝗼𝗿 𝟮𝟬𝟮𝟱 The surge in 𝘢𝘳𝘵𝘪𝘧𝘪𝘤𝘪𝘢𝘭 𝘪𝘯𝘵𝘦𝘭𝘭𝘪𝘨𝘦𝘯𝘤𝘦 (AI) investment has sparked a fierce debate: Are we living through an "AI bubble"? Here’s what’s at stake and why it matters, 𝘦𝘷𝘦𝘯 𝘪𝘧 𝘺𝘰𝘶’𝘳𝘦 𝘯𝘰𝘵 𝘢 𝘵𝘦𝘤𝘩 𝘰𝘳 𝘧𝘪𝘯𝘢𝘯𝘤𝘦 𝘦𝘹𝘱𝘦𝘳𝘵. 𝘞𝘩𝘢𝘵’𝘴 𝘢𝘯 𝘈𝘐 𝘉𝘶𝘣𝘣𝘭𝘦? A financial "𝘣𝘶𝘣𝘣𝘭𝘦" happens when investors pour money into a sector based on hype rather than actual business results, pushing prices so high that they disconnect from what companies are truly earning. We've seen this before, think the dot-com crash and the 2008 financial meltdown. 𝘞𝘩𝘢𝘵’𝘴 𝘏𝘢𝘱𝘱𝘦𝘯𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘈𝘐? Over $375 billion will be spent on AI globally in 2025, with projections surpassing $500 billion by 2026. More than 1,300 AI startups are now valued above $100 million, and nearly 500 are “unicorns” worth over $1 billion. Major tech giants (Amazon, Microsoft, Meta) are plowing massive funds into data centers and chip deals, racing to be AI leaders. 𝘕𝘷𝘪𝘥𝘪𝘢, 𝘢 𝘵𝘰𝘱 𝘈𝘐 𝘤𝘩𝘪𝘱𝘮𝘢𝘬𝘦𝘳, 𝘳𝘦𝘢𝘤𝘩𝘦𝘥 𝘢 $5 𝘵𝘳𝘪𝘭𝘭𝘪𝘰𝘯 𝘷𝘢𝘭𝘶𝘢𝘵𝘪𝘰𝘯, 𝘢𝘭𝘮𝘰𝘴𝘵 𝘢𝘴 𝘭𝘢𝘳𝘨𝘦 𝘢𝘴 𝘢𝘭𝘭 𝘰𝘧 𝘊𝘢𝘯𝘢𝘥𝘢’𝘴 𝘦𝘤𝘰𝘯𝘰𝘮𝘺 𝘤𝘰𝘮𝘣𝘪𝘯𝘦𝘥. 𝗥𝗲𝘁𝘂𝗿𝗻𝘀 𝗹𝗮𝗴 𝗯𝗲𝗵𝗶𝗻𝗱 𝗶𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁: OpenAI racked up nearly $1 trillion in deals and a $500 billion data center project, but forecasts just $13 billion in revenue. The sector's biggest wins (like ChatGPT) lose money on most uses, thanks to ultra-high computing costs. 𝗖𝗶𝗿𝗰𝘂𝗹𝗮𝗿 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗻𝗴: Industry leaders trade billions back and forth, like Nvidia investing in OpenAI and OpenAI buying Nvidia’s chips, sometimes inflating each other’s valuations. 𝗛𝘆𝗽𝗲 𝗼𝘃𝗲𝗿 𝗿𝗲𝗮𝗹𝗶𝘁𝘆: Current AI applications often fail to deliver on promises (𝘈𝘐 𝘵𝘩𝘦𝘳𝘢𝘱𝘪𝘦𝘴, 𝘵𝘶𝘵𝘰𝘳𝘴, 𝘰𝘳 𝘴𝘦𝘢𝘳𝘤𝘩 𝘣𝘰𝘵𝘴 𝘢𝘭𝘭 𝘴𝘦𝘦 𝘮𝘪𝘹𝘦𝘥 𝘰𝘳 𝘶𝘯𝘥𝘦𝘳𝘸𝘩𝘦𝘭𝘮𝘪𝘯𝘨 𝘳𝘦𝘴𝘶𝘭𝘵𝘴). 𝘞𝘩𝘢𝘵 𝘋𝘰 𝘌𝘹𝘱𝘦𝘳𝘵𝘴 𝘚𝘢𝘺? Some analysts say it’s a classic bubble, sky-high spending and valuations are outpacing profits and real-world impact. Others argue this investment is key for future breakthroughs, though not all bets will pay off. Even CEOs like 𝗦𝗮𝗺 𝗔𝗹𝘁𝗺𝗮𝗻 (OpenAI) and 𝗝𝗮𝗺𝗶𝗲 𝗗𝗶𝗺𝗼𝗻 (JPMorgan) warn that much of today’s capital may end up lost in failed projects, but genuine innovation will endure. 𝘞𝘩𝘢𝘵 𝘊𝘰𝘶𝘭𝘥 𝘏𝘢𝘱𝘱𝘦𝘯 𝘕𝘦𝘹𝘵? If the bubble pops, we could see layoffs, failed startups, and stock price drops, much like the dot-com bust. But underneath, truly valuable uses for AI will survive and grow, just as internet giants did after the 2000s crash. The AI story in 2025 is a mix of bold promise, wild hype, and rising risk. Whether you build, invest in, or just use these tools, knowing the difference will matter more than ever. #AI #ArtificialIntelligence #AIBubble #TechTrends #Finance #2025

Although economists suggest current AI spending outweighs AI-related profits, pragmatic channel partners who stay the course can still thrive. Artificial intelligence has sparked one of the biggest spending waves in tech history. Venture capitalists have poured nearly $200 billion into AI this year, while data-center investment has tripled since 2022 — levels not seen since the dot-com era. Economists Jared Bernstein and Ryan Cummings warn that valuations may be outpacing realistic profits and fueling what they call the “AI bubble.” Yet whether that bubble pops next quarter or five years from now, partners shouldn’t panic.

Signs of an AI Bubble Experts are pointing to several red flags that suggest the AI market may be overheating: * Extreme Valuations & Concentration: The market's gains are highly concentrated in a few "Magnificent 7 " megacap tech stocks, which have accounted for nearly half of the S&P 500 's recent gains . This kind of narrow #leadership often signals a bubble. * Unproven Returns on Massive Spending: Big #Tech companies are spending hundreds of billions on AI infrastructure. These valuations depend on a rapid and high return on investment, which is far from guaranteed . As one expert noted, "Valuations are not pricing in a slow adoption story here" . * The "Easy Money" Has Been Made: According to Ted Mortonson, a managing #director at Baird, "The easy money has been made" in the AI trade . This suggests future gains will be harder to come by and the risk of a sharp correction is growing. 🛡️ How to Protect Your Portfolio You don't have to avoid #AI entirely, but a smart, cautious approach is key. Here are 3 #strategies from #financial experts: 1. Reduce Big Tech Exposure: Consider trimming your holdings in the biggest AI winner #stocks, especially if their stock price moves below its 50 day moving average. This helps you lock in gains and reduce risk . 2. Follow the Free Cash Flow: Invest in companies with strong, reliable cash flow. This provides a safety net if expected AI revenues don't materialize. Beaten down enterprise software stocks and certain semiconductor companies are potential opportunities . 3. Consider Diversifying into Small Caps: #US large cap growth stocks are very expensive. Small cap stocks are trading at a discount and could outperform, especially if interest rates fall, which would reduce their borrowing costs . SO The AI revolution is real, but its #financial markets may be in a #bubble. Protecting your wealth now means focusing on companies with solid finances and a diversified portfolio, rather than chasing the most hyped stocks. #Investing #StockMarket #FinanceTips #PortfolioManagement #InvestingStrategy #HR #CEO #CTO #Webdevelopers #Men #entrepreneur #CMO

The market is shaking again, but it’s not panic📉 What stands out right now: First, the tech-hype cycle is getting a reality check. Private investment into generative AI reached $33.9 billion in 2024, representing an 18.7% increase from 2023. Yet despite the surge, analysts caution that 30% of enterprise generative AI projects are expected to stall in 2025 because of weak data, unclear business value, and risk control gaps. In short: yes, the AI market is huge, global estimates project the AI market size may reach $3.68 trillion by 2034. This means companies and investors aren’t necessarily abandoning AI, but they are shifting from speculation toward execution and tangible results. Second, in parallel, uncertainty in government and policy is once again casting a shadow. For example, a recent analysis finds that each week of a U.S. federal government shutdown could reduce GDP by about 0.1 percentage points. A White House-memo estimates the cost of a protracted shutdown could reach $15 billion per week, potentially leading to tens of thousands of lost jobs and reduced consumer spending. While markets may remain resilient in the short term, for now, the real risk for businesses lies in how slower policy flows and delayed data may disrupt expansion plans, hiring decisions, and capital deployment. What it means for business owners: • If you’ve been riding the AI wave or considering tech expansion, now’s the time to ask: “Is this growth sustainable and supported by clear ROI?” • If your business relies on stable government contracts, vendor payments or regulatory approvals, the timing and certainty of your cash flow may matter more than ever. What do you think are investors shifting wisely from hype to substance or are we just in the calm before the next big wave? Share your opinion💡

"The entire AI project is clouded in an immediate, tangible fear: what if investors come to realize that the stupendous productivity gains that Silicon Valley assures us will more than justify plowing hundreds of billions of dollars into ever more powerful AI agents won’t, in fact, materialize? Trillions of dollars in stock wealth could go poof in days..." https://lnkd.in/gh39Pfdf