Ray Fleming’s Post

Is the AI hype heading for a crash? this MIT study article explores the risks of an AI bubble and its potential impact on Wall Street and Big Tech. A must-read for anyone in the tech and finance industries. https://lnkd.in/dtWGQCm2

🚨 The AI Bubble: Why 95% of GenAI Projects Are Failing Despite record-breaking $49B+ invested in 2025 alone, a new MIT study shows: 👉 95% of enterprise AI initiatives aren’t generating meaningful revenue. We may be witnessing a bubble bigger than the dot-com era. Here’s why 👇 📉 1. The Investment Surge 2022: $8.7B in GenAI VC funding 2024: $45B total 2025 (H1 alone): $49.2B 🤯 Big Tech is expected to spend $320B on AI infra this year ⚠️ 2. The Reality Gap Most AI pilots never move past POC ROI is unclear → productivity gains ≠ revenue gains Enterprises adopt AI for FOMO, not strategy 🔍 3. Why Projects Fail Lack of clear business use cases Data fragmentation & poor integration Overreliance on hype-driven “AI for everything” thinking Talent gap in applied AI (strategy + operations, not just coding) 💡 4. The Paradox We’re pouring billions into infrastructure… …but 95% of projects can’t show real business impact. That’s the same disconnect we saw in the dot-com bubble. Only this time, the stakes are higher. 🔥 The Big Question: Will AI become the foundation of the next economy… …or the biggest bubble of our lifetime? 💬 What do you think — are we in an AI revolution or an AI bubble?

The most effective AI founders don't pitch technology—they sell the moment when someone's life fundamentally changes. I walked into that conference room ready to impress. Twenty investors. Flawless slides. Every neural network explained down to the last parameter. Honestly? I was proud. Maybe even a little smug. Polite faces waiting for something "disruptive." I launched in. Slide after slide. Chart after chart. Then, the questions started. "What problem does this actually solve?" "How does this help a real customer?" There it was—the look. "You lost me ten minutes ago." That's the moment most technical founders dread. Not failing on the math, but failing on the meaning. What stung: I'd spent months perfecting the engine. I forgot to build a bridge so anyone could cross it. That night, it hit me. The question isn't "How do I explain my tech better?" It's—"How do I translate my world so others don't just understand, but care?" Most of us get stuck in the "translation trap." The deeper your expertise, the harder it gets to remember what it's like not to know. We explain what and how, but skip why it matters to humans. Investors aren't trying to understand your code. They're trying to de-risk decisions. If your story is as dense as your Stack Overflow history? That's backing someone brilliant but disconnected. (I was speaking machine. Not mission.) Here's the framework I wish I'd had: Step 1: Hunt for the human villain. Forget technical jargon. What pain does your tech crush? Maybe it's the doctor buried in admin, losing hours meant for patients. Step 2: Name the new superpower. Don't list features. Tell me the ability you unlock. "Our AI gives Dr. Lee instant insight—so she swaps paperwork for patient time." Step 3: Paint the "new world." What does it feel like when your tech exists? Work feels lighter. Lives change. None of this is "dumbing down." It's elevating purpose—giving people a bridge to cross. Most founders stop at one great story, then default back to jargon. Treat your human story like a translation layer between brilliance and world. Every investor update. Every meeting. Every post. This isn't a pitch-day costume. Human impact should be your company's heart. It's how magnetic brands get built—and deep-tech founders who master this become the standard. When it comes to technical-to-human translation as a founder, what's your biggest challenge right now? A) Finding analogies that don't oversimplify your tech B) Keeping investors engaged when explaining complex solutions C) Maintaining technical credibility while making concepts accessible Drop your answer in the comments—I'm collecting insights for founders. P.S. What was your most painful "lost in translation" moment with investors? Those stories teach us all. #AIFounders #PitchStrategy #TechTranslation #InvestorCommunication

The investment community is treating AI much like past industrial revolutions: a disruptive force that will create winners and losers, change how businesses operate, and reshape economic growth. For investors, in our opinion, understanding both the opportunities and the risks is essential. Learn more in our latest market commentary.   https://lnkd.in/eTnTmpxK

Today, AI is not just improving forecasting; it’s completely changing it.   - Speed & Scale: Machine learning algorithms process huge datasets in seconds, covering everything from market trends to macroeconomic indicators.   - Accuracy: AI models spot patterns that traditional methods miss, which helps reduce forecasting errors.   - Adaptability: Real-time data integration allows forecasts to change instantly based on market shifts.  For India’s quickly growing economy, this is a major shift. From NBFCs to fintech startups, AI-driven forecasting helps with smarter credit risk assessment, better investment strategies, and stronger business planning.  The future of finance will go to those who blend human judgment with AI intelligence. The real question is not whether we will use AI in forecasting; it’s how fast we can weave it into our decision-making processes. #AIinfinance #machinelearning #forecasting #financialplanning #fintech #creditriskassessment #AIinsights #businessstrategy

🚨 Historic consensus in VC might be a red flag for the industry For the first time in our annual survey, over 50% of VCs pointed to the same "hottest sector" AI. While this could signal genuine excitement about transformative technology, it raises a critical question: Are we witnessing dangerous groupthink? The VC paradox: To win big, you need to be both right AND non consensus. When everyone's looking in the same direction, who's finding the next breakthrough that others are missing? AI tools themselves might be amplifying this convergence. As more investors rely on similar AI systems for research and strategy, we may inadvertently be creating echo chambers that replace the diverse thinking that once came from individual analysts. At Decile Group, we're tackling this head on by: 🎯 Training our AI on unique community data, not generic public information 🔄 Building counterfactual generation into our tools 💡 Designing features that push managers to add their own contrarian analysis The best VCs have always thrived on being different. But in an AI driven world, maintaining that edge requires intentional design against convergence. Read the full analysis to see how we're building AI tools that enhance rather than replace independent thinking! - link in the comments 👇 💬 Tell us what strategies you use to maintain contrarian thinking in an increasingly AI assisted world!

AI is transforming how products are built and how capital is deployed 🚀. Its potential is unprecedented, but AI is not the product or the outcome. It’s the enabler that helps us create the right solutions to meaningful, human problems 💡. Focus on solving the most worthy problems, and the best opportunities will emerge.

I think Decile Hub deserves to be called "the" venture capital co-pilot, considering how it leverages AI to both streamline end-to-end operations and deliver differentiated recommendations over time. Simply by being an environment that encourages more active over passive use of LLMs. #VentureInstitute #Cohort5

Decile Group makes an interesting point about consensus and groupthink in VC in relation to AI. This post made me think of two things: the environmental cost of AI, and how sustainability and climate tech can be seen as contrarian investments to AI. Don’t get me wrong, AI is receiving some well-deserved hype because it is cutting-edge, solving problems in many markets, and is super interesting. However, one area I rarely see factored into the AI boom is its environmental cost: massive energy consumption, water use, and growing e-waste. By 2030, data centers could consume up to 6% of global electricity, which is more than some countries use. Major providers are already reporting huge spikes in water use. For example, training GPT-3 alone is estimated to have consumed ~700,000 liters of clean water, mostly for cooling. Contrarian thinking could be linked to supporting founders working on climate-resilient AI, which emphasize efficiency, transparency, and carbon-aware models. Additionally, if AI is steering everyone toward the same conclusions, focusing on other sectors of sustainability is where contrarian thinkers can find both alpha and long-term resilience. Areas like decarbonization, circular economy, alternative materials, and resilient infrastructure could provide some large opportunities for investors. These opportunities are underfunded now, but hopefully they won’t be for long as VCs recognize how imperative it is directly focus on environmental sustainability. Further reading on these stats can be found here: https://lnkd.in/e9UKAERt https://lnkd.in/exuQ_UuX