Data Center Dynamics: AI and Energy Demand
Energy-hungry data centers are on the rise. Power demand driven by artificial intelligence has been met by an increase in power purchase agreements (PPAs) for low-carbon energy. Meanwhile, DeepSeek has reduced demand through more efficient computations. So what is driving decision making at tech companies that work in the AI and data center space? At the 2025 BloombergNEF Summit San Francisco, Mark Daly, BNEF's head of technology and innovation, moderated a panel titled 'Data Center Dynamics.' This episode brings listeners that panel, which featured Steven Carlini, chief advocate of data centers and AI at Schneider Electric; Will Conkling, head of data center energy for the Americas and EMEA at Google; Kleber Costa, chief commercial officer at AES Corporation; and Darwesh Singh, founder and CEO at Bolt Graphics.
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Yahoo
2 hours ago
- Yahoo
Veteran Wall Street firm makes surprise call on tech stocks
Veteran Wall Street firm makes surprise call on tech stocks originally appeared on TheStreet. It's been nothing short of a gut punch for tech stocks this year. AI swings, sticky interest rates, and trade-war drama have weighed down tech's rebound so far. 💵💰💰💵 As if tech didn't have enough problems, the Israel-Iran flare-up and U.S. airstrikes have added another dynamic. However, despite the nervous energy on Friday, the markets seem relatively unfazed on Monday. One veteran Wall Street firm in particular feels the U.S. airstrikes on Iran might've helped calm the waters for tech, not shake them. After two strong years, tech stocks took a hammering in 2025. It kicked off in January when Chinese AI firm DeepSeek dropped its R1 model, with some of the biggest AI stocks shedding billions in days. By mid-March, you had both the S&P 500 and Nasdaq sliding into correction territory, losing north of 10% from their many had predicted, investors finally buckled under the weight of lofty AI multiples and sticky interest rates. As if things weren't bad enough, Trump's trade moves had chipmakers staring down fresh tariffs and more global supply headaches. This month, we saw the Israel-Iran conflict taking center stage with back-and-forth airstrikes adding to the geopolitical angst. But then the U.S. finally stepped in with targeted hits on Iran's nuclear sites, and weirdly, markets liked the move. More Tech and AI Stock News: Circle stock is one of the market's biggest winners this week OpenAI's Altman slams Mark Zuckerberg, ignites drama Google plans major AI shift after Meta's surprising $14 billion move For tech stocks, which have been starving for clarity, that's exactly the kind of signal they need to start clawing back some of those big gains. Wedbush feels the market's likely to shrug off the Iran strikes, with tech stocks likely to rebound with the overhang clearing out. The veteran Wall Street firm feels most investors believed the U.S. strike was inevitable, a 'when, not if' kind of move. In their view, a weaker Iran with no nukes takes away a major threat from the Middle East and Israel. This could be bullish for stocks, especially tech, as investors process the news. Also, the firm expects cybersecurity stocks to push higher following the news. Wedbush mentioned the likes of Palo Alto Networks, CyberArk, CrowdStrike, Zscaler, and Checkpoint to get a boost, as investors expect cyberattacks from Iran. Over the past week, U.S. officials have anticipated low-grade cyberattacks from Iran, including DDoS hits and hacktivist a Politico report says pro-Iranian groups have already been meddling with utility and banking systems, forcing some countries to lock things down early. Hence, with cyber threats heating up, next-gen defense tech is getting its moment. Smart firewalls, cloud filters, AI tools, and zero-trust setups offered by the cybersecurity bellwethers mentioned above have helped keep hackers at bay. Wedbush also encouraged investors to buy the dips in tech winners and AI leaders like Nvidia () , Palantir () , Microsoft () , and Tesla. Nvidia has undoubtedly been at the front of the AI pack. Every major AI model, from GPT-style LLMs to rec engines, runs on its GPUs. With its powerful H100s for training and Blackwell chips for inference (applying trained data), they've built a position that's virtually impossible to match. Palantir is another major AI play, with its full-stack software becoming ubiquitous for governments and big business. The company handles everything from data crunching to model control, racking up double-digit top-line growth, raising its guidance, and remaining firmly in the green. Moreover, the recent conflict buzz has lit up Palantir's sales, with its clientele running AI straight into their data stacks. And despite the market sluggishness, Palantir stock is at the top of the heap, posting an 82% gain year-to-date. In contrast, AI stalwart Nvidia is up just 6.6%, following an incredible rally over the last couple of years. Microsoft has also impressed, racking up a nifty 14% gain, led by booming Azure AI deployments and stronger enterprise Wall Street firm makes surprise call on tech stocks first appeared on TheStreet on Jun 24, 2025 This story was originally reported by TheStreet on Jun 24, 2025, where it first appeared.
Harvard Business Review
3 hours ago
- Harvard Business Review
The AI Revolution Won't Happen Overnight
If you believe the frenzied hype, AI is about to tie our shoes, run our businesses, and solve world hunger. McKinsey predicts it will add $17.1–$25.6 trillion to the global economy annually. It's a seductive vision. It's also a hallucination. As a business-first CIO with nearly three decades of experience turning emerging tech into business value, I've seen this movie before. It rarely ends the way the trailer promises. We've spent 75 years asking whether machines can think. Maybe the better question now is whether we can. Yes, AI is powerful. Yes, it will change how we live and work. But the transformation will be slower, messier, and far less lucrative in the short term than the hype suggests. Companies are collectively pouring billions of dollars into AI without clear ROI. Open-source models like Meta and Deep Seek are rapidly eroding the competitive advantage of other big tech companies' foundation models (e.g.,Gemini, ChatGPT). And the business model for gen AI is full of potential—but missing a clear path to sustainable revenue. AI's transformational impact will come, but it won't be the instant revolution we're being sold. We're getting six fundamental things wrong about how AI will create value and how long it will take. AI's real impact will take much longer than we think. In 1987, economist Robert Solow famously quipped, 'You can see the computer age everywhere but in the productivity statistics.' Decades later, AI is the latest iteration of this paradox. Despite billions in investment, measurable efficiency gains remain elusive. So far, the Federal Reserve Bank of Kansas City found that AI's impact on productivity has been modest compared to previous technology-driven shifts. This isn't a failure of AI—it's a failure of expectations. Generative AIs like large language models are a general purpose technology (GPT). (Though the 'GPT' in ChatGPT stands for something else.) We've seen many GPTs before—the printing press, electricity, the internet—and they all follow the same pattern. In each case, it took decades before their transformative potential really hit the economy. Electricity revolutionized manufacturing, but it took 40 years before factory design caught up. The internet existed in the 1970s, but it wasn't until the 2000s that it rewrote business models. There are compelling reasons to think that AI will follow the same slow but inevitable trajectory. For example, MIT economist and Nobel laureate Daron Acemoglu argues that only 5% of tasks will be profitably automated in the next decade, adding just 1% to the U.S. GDP—a far cry from the seismic shift many expect. The challenge, he argues, is that for most organizations, the costs of disruption, retraining, integration, and computing will outweigh the returns for most tasks. Moreover, we've already picked the low-hanging fruit of digital transformation—automating operational work, digitizing information, moving customers online, and migrating core infrastructure to the cloud. These early wins delivered efficiency gains. But each new leap delivers diminishing returns, making it harder for AI—or any technology—to drive economy-wide productivity gains. Despite breakthrough technologies like smartphones, social media, and cloud computing, U.S. total factor productivity (TFP) growth has been sluggish for five decades. From 1974 to 2024 TFP growth was less than half the rate of the post-war boom. AI might boost personal productivity, but it won't deliver productivity gains at scale anytime soon—if at all. A study by the National Bureau of Economic Research recently demonstrated the difference of adoption and intensity. They showed that while 40% of U.S. adults used generative AI, most people used it infrequently. That infrequent use translated to 1–5% of total work time. When combined with the users' estimated time savings, this resulted in <1% of a productivity gain. That doesn't mean AI is useless. It just means its value won't come from sweeping, instant disruption, but from targeted, deliberate integration. Betting on a short timeline and quick ROI risks wasted capital, failed automation, and unnecessary workforce disruption. Instead, companies should focus on the long game: build the right systems, train your team, and figure out how to make AI work for your business. We're being wildly optimistic about enterprise AI adoption. When ChatGPT launched, AI felt like magic—an overnight revolution. Earnings calls were flooded with AI mentions. Venture capital shifted into overdrive. Headlines promised AI's transformation would be instant and all-encompassing. We've seen this kind of overheated hypecycle before — with the early personal computers, dot-com bubble, the blockchain boom, and even the very early days of cloud computing—and we'll likely make this mistake again. We misjudge technological change because of three cognitive biases. The planning fallacy makes us underestimate how long transformation takes. Optimism bias convinces us adoption will be smooth and easy. Recency bias leads us to believe AI's viral consumer adoption will translate seamlessly into the enterprise. For all of the concern about AI's biases, we tend to overlook our own, and this might be especially true in enterprise adoption. Enterprise AI isn't plug-and-play. It collides with outdated systems, regulatory roadblocks, risk-averse corporate cultures, AI talent shortages, and procurement bottlenecks. The barriers aren't technical, they're systemic. It took us 100 years to add wheels to luggage, don't underestimate the forces that balance the pace of technology diffusion. IBM Watson Health is a cautionary tale. IBM promised to 'outthink cancer,' betting big that AI would transform healthcare. But by 2022, Watson was sold for parts, its potential crushed by messy, fragmented medical data, regulatory red tape, and real-world complexity. Hospitals found it unreliable. Doctors found it impractical. Ethical concerns mounted. Watson didn't fail because of AI—it failed because IBM underestimated how difficult real-world implementation would be. AI will transform industries, just not at Silicon Valley speed. It will happen on enterprise time: longer, slower, and with far more friction than most expect. Companies that fall victim to bias and ignore these realities will waste resources, overpromise results, and erode trust. The winners in AI won't be the ones making the boldest claims. They'll be the ones with the patience to build real, lasting change. The market is overestimating the value of AI companies. Investors are making a critical error around AI: They're treating AI companies like high-growth, asset-light software firms, when in reality they're capital-intensive, high-cost, and infrastructure heavy. AI-heavy tech stocks have traded at a 20–40% premium, assuming future profits that haven't materialized. For executives, this disconnect isn't just a market misread—it's an execution trap. Inflated valuations set unrealistic expectations that trickle down into the enterprise: pressure to move fast, to pilot something flashy, to be seen 'doing AI.' The result? Rushed rollouts, misaligned priorities, and investments in the magic rather than margin performance. In a market priced for miracles, the real advantage lies in restraint—leaders who prioritize integration over spectacle and long-term value over short-term visibility. Consider OpenAI. It's chasing a $300 billion valuation —double Facebook at IPO and eight times Google at IPO (adjusted for inflation). Investors are pricing it like a cloud software company with expanding margins. But AI isn't SaaS. OpenAI's costs don't shrink with scale, they rise with demand. Every query has a price. Every customer adds costs. OpenAI itself expected a $5 billion loss on $3.7 billion in revenue in 2024. The problem is that the infrastructure demands of AI are staggering. Meta, Alphabet, Amazon, and Microsoft plan to spend a combined $300 billion this year. Analysis of cash flow statements and public statements show their AI-related capital expenditures have increased 40–60% in just two years. Microsoft alone is spending $80 billion this year. By 2028, Microsoft's compute needs could rival an entire country's electricity demand. This infrastructure build has created an estimated $125 billion annual revenue gap to fill. Competition is further squeezing AI's margins. Open-source models like LLaMA, Mistral, and DeepSeek-V3 are rapidly eating into market share. Meta's LLaMA 3 already reaches over a billion users across Instagram, WhatsApp, and Facebook—at zero cost to consumers. Meanwhile, OpenAI pays for every user and lacks a built-in distribution ecosystem. AI is commoditizing faster than any previous technology cycle, a reality even OpenAI's board chair has acknowledged. For industry leaders, the implications are real and immediate. Many are making high-stakes investment decisions based on tools built by companies whose AI business models may not be sustainable. If those partners face cost overruns, slowed R&D, or collapse altogether, it could leave enterprise roadmaps stranded mid-implementation. The risk isn't just financial—it's operational. The real winners in AI won't be those chasing sky-high valuations. They'll be the companies embedding AI where it creates durable, economic advantage—places where it speeds up business decision cycles, improves decision quality, or reimagines products—all with measurable ROIs. AI's transformation will be a test of leadership stamina, not speculation. The real money isn't in the models. Even if AI model companies turn a profit, they won't be able to defend their advantage. AI's biggest breakthroughs—like neural networks and attention mechanisms—are just math, and math can't be patented. That's the critical difference between invention and innovation. Invention delivers the breakthrough—the transformer architecture, the novel algorithm. But innovation at scale requires more: distribution, margin, and market fit. The real test of AI isn't whether we can build something new. It's whether we can embed it deeply enough into business systems to generate durable, measurable value. And that's exactly why models, no matter how advanced, won't hold the moat. Open-source collaboration and government-backed research will continue to push AI toward commoditization. Once AI is cheap and everywhere, no one will own it. The real value isn't in building AI—it's in using it. It's in applications, not models. AI is already moving to 'the edge,' shifting from the cloud to personal devices where users don't need to pay for access. Apple Intelligence, though early in the market, is embedded into iPhones. Some Meta LLaMA models run on laptops. This is the same trajectory cloud computing followed. Investors first bet on infrastructure—AWS, Azure, Google Cloud. But over time, the winners weren't the cloud 'infrastructure' providers. They were the application companies embedding cloud into business workflows. By 2030, Goldman Sachs expects cloud infrastructure to be a $580 billion market, while cloud applications will be more than double that at $1.38 trillion. It stands to reason that AI will follow the same pattern. Apps move AI from theory to reality, from the lab to the customer. Turning a model into a real business solution is an engineering challenge far beyond just running a model with chat on top. The companies solving complex, industry-specific problems with custom AI architectures are the ones who will create the most lasting value. This shift is already beginning as we see AI agents cropping up across industries. Harvey is an AI lawyer. Glean is an AI work assistant. Factory is an AI software engineer. Abridge is an AI medical scribe. AI's real value is in transforming human-dependent services into scalable, always-on applications. And that's exactly where enterprise companies should focus—not on building models, but on applying them with precision. The opportunity isn't to create the next GPT. It's to embed AI into the backbone of the business—product design, operations, compliance, HR, finance—where small changes will add up. Too many enterprises assume foundation models will deliver value out of the box. But without serious investment in the hard stuff—applications, integration, data infrastructure, workflow redesign and change management—AI remains a flashy prototype: impressive in demos, but ineffective at scale. Ironically, the companies that win will be the ones that make AI boring: seamlessly embedded, consistently reliable, and quietly transformative where the real work happens. We're over indexing on startups. The market hype is fixated on AI startups, but big incumbents have the real advantage in the enterprise. AI isn't about disruption, it's about distribution. Look at Microsoft Teams. Microsoft didn't build the best video conferencing tool—Zoom did. But Microsoft won in the enterprise by bundling Teams into Office 365. Businesses didn't pick Teams because it was better; they picked it because it was already there. The same playbook is unfolding in AI. Startups may push innovation forward, but incumbents control enterprise budgets, IT integration, and distribution. Microsoft, Google, and Salesforce don't need the best AI models—they just need good enough AI, seamlessly embedded into their existing enterprise stack. That's how AI adoption happens—whoever owns the enterprise and consumer workflow wins. This is why AI isn't another e-commerce disruption story. In the late 1990s, online upstarts like PayPal, Amazon, and eBay toppled brick-and-mortar giants because the internet leveled the playing field. But AI is different. It's not low-cost, high-speed disruption. It's capital-intensive, infrastructure-heavy, and favors scale. And Big Tech already owns the data, compute power, and enterprise relationships. That last point is critical. Proprietary, real-time enterprise data is the last true moat in AI. Today's AI models are trained on 300 trillion tokens of publicly available text—but that data is running out. Epoch AI estimates that between 2026 and 2032, developers will hit a wall—there won't be enough high-quality public training data left. Large incumbents have the edge—but it's not automatic. They sit on the distribution rails, the enterprise relationships, and the proprietary data that startups can only dream of. But advantage without action is inertia. Now is the time to double down: integrate AI into existing systems, leverage data as a strategic asset, and partner where it adds speed or specificity. This isn't about chasing the next big thing—it's about making the last big thing work at scale. We're obsessed with generative AI but it's not the future. We're fixated on generative AI, but the future lies beyond chat-based models. Today's AI excels at summarizing reports and drafting emails but struggles with real-world complexity. It lacks situational awareness, complex reasoning, and the ability to synthesize multiple types of changing information in real-time. That's why AI adoption lags in fields like medicine and logistics—where decisions require more than historical text. A chatbot can draft a contract, but it can't diagnose every patient or optimize a failing supply chain. The next evolution is Multimodal AI and Compound AI systems—technologies that process multiple types of input and work together like human cognition. A self-driving car doesn't rely on a single data source; it integrates LiDAR, radar, GPS, and live sensors to navigate. AI will need to do the same, layering models that analyze vision, sound, text, and real-time data. Compound AI systems take this further, combining multiple models to create intelligence that learns, plans, and acts autonomously. Today, AI operates in silos—one model generates text, another detects fraud. Future AI will orchestrate these capabilities like an ensemble of specialists working together. This is a signal for companies to plan ahead. The current generation of AI tools can offer some wins—but those wins are relatively narrow. Leaders should avoid overinvesting in single-purpose solutions and start building toward infrastructure that can support integrated, multimodal systems. That means investing in data architecture, workflow flexibility, and AI governance that can evolve as the technology does. The future of AI isn't about building a better chatbot. It's about designing systems that see, hear, analyze, and act in concert—at scale, and in sync with the complexity of the real world. Can we think smartly about machines? In 1950, Alan Turing posed the now-famous question: ' Can machines think? ' Seventy-five years later, we're evaluating AI on how well it reasons, predicts, and generates. Maybe it's time to turn that same lens on ourselves. Right now, we're collectively hallucinating our way into bad bets, misplaced priorities, and unrealistic timelines. Companies are treating AI as if it's a silver bullet, throwing billions at models while neglecting the harder work of integration, infrastructure, and real business value. Ultimately, the market will determine which companies and sectors capture AI's value. But one thing is certain: AI's ubiquity will erode its exclusivity. Its impact won't be in who owns it, but in how we use it. Turing's original question is still relevant. But today, the more important one is: 'Can we think smartly about machines?' For enterprise leaders, that means shifting the focus from potential to performance. It means asking fewer questions about what AI might do—and more about what it's actually doing in your business. It means building for endurance, not headlines—investing in architecture, talent, and systems that can turn today's tools into tomorrow's competitive advantage.
Miami Herald
4 hours ago
- Miami Herald
Veteran Wall Street firm makes surprise call on tech stocks
It's been nothing short of a gut punch for tech stocks this year. AI swings, sticky interest rates, and trade-war drama have weighed down tech's rebound so far. Don't miss the move: Subscribe to TheStreet's free daily newsletter As if tech didn't have enough problems, the Israel-Iran flare-up and U.S. airstrikes have added another dynamic. However, despite the nervous energy on Friday, the markets seem relatively unfazed on Monday. One veteran Wall Street firm in particular feels the U.S. airstrikes on Iran might've helped calm the waters for tech, not shake them. Image source:After two strong years, tech stocks took a hammering in 2025. It kicked off in January when Chinese AI firm DeepSeek dropped its R1 model, with some of the biggest AI stocks shedding billions in days. By mid-March, you had both the S&P 500 and Nasdaq sliding into correction territory, losing north of 10% from their peaks. Related: Struggling EV semiconductor company files for bankruptcy As many had predicted, investors finally buckled under the weight of lofty AI multiples and sticky interest rates. As if things weren't bad enough, Trump's trade moves had chipmakers staring down fresh tariffs and more global supply headaches. This month, we saw the Israel-Iran conflict taking center stage with back-and-forth airstrikes adding to the geopolitical angst. But then the U.S. finally stepped in with targeted hits on Iran's nuclear sites, and weirdly, markets liked the move. More Tech and AI Stock News: Circle stock is one of the market's biggest winners this weekOpenAI's Altman slams Mark Zuckerberg, ignites dramaGoogle plans major AI shift after Meta's surprising $14 billion move For tech stocks, which have been starving for clarity, that's exactly the kind of signal they need to start clawing back some of those big gains. Wedbush feels the market's likely to shrug off the Iran strikes, with tech stocks likely to rebound with the overhang clearing out. The veteran Wall Street firm feels most investors believed the U.S. strike was inevitable, a "when, not if" kind of move. In their view, a weaker Iran with no nukes takes away a major threat from the Middle East and Israel. This could be bullish for stocks, especially tech, as investors process the news. Also, the firm expects cybersecurity stocks to push higher following the news. Wedbush mentioned the likes of Palo Alto Networks, CyberArk, CrowdStrike, Zscaler, and Checkpoint to get a boost, as investors expect cyberattacks from Iran. Over the past week, U.S. officials have anticipated low-grade cyberattacks from Iran, including DDoS hits and hacktivist noise. Related: Tesla's robotaxi finally launches, but there's a twist Meanwhile, a Politico report says pro-Iranian groups have already been meddling with utility and banking systems, forcing some countries to lock things down early. Hence, with cyber threats heating up, next-gen defense tech is getting its moment. Smart firewalls, cloud filters, AI tools, and zero-trust setups offered by the cybersecurity bellwethers mentioned above have helped keep hackers at bay. Wedbush also encouraged investors to buy the dips in tech winners and AI leaders like Nvidia (NVDA) , Palantir (PLTR) , Microsoft (MSFT) , and Tesla. Nvidia has undoubtedly been at the front of the AI pack. Every major AI model, from GPT-style LLMs to rec engines, runs on its GPUs. With its powerful H100s for training and Blackwell chips for inference (applying trained data), they've built a position that's virtually impossible to match. Palantir is another major AI play, with its full-stack software becoming ubiquitous for governments and big business. The company handles everything from data crunching to model control, racking up double-digit top-line growth, raising its guidance, and remaining firmly in the green. Moreover, the recent conflict buzz has lit up Palantir's sales, with its clientele running AI straight into their data stacks. And despite the market sluggishness, Palantir stock is at the top of the heap, posting an 82% gain year-to-date. In contrast, AI stalwart Nvidia is up just 6.6%, following an incredible rally over the last couple of years. Microsoft has also impressed, racking up a nifty 14% gain, led by booming Azure AI deployments and stronger enterprise spending. Related: Veteran Tesla analyst makes boldest robotaxi call yet The Arena Media Brands, LLC THESTREET is a registered trademark of TheStreet, Inc.
