This year, AI had that moment. And the stage wasn’t a keynote. It was the Super Bowl.

The Super Bowl is not a developer conference. No one is there to debate architectures. No one is comparing benchmarks or arguing about model performance. The Super Bowl is where America goes to be entertained and to argue about commercials.

And this year, AI wasn’t a background capability quietly powering someone else’s message. It was the headline.

Super Bowl LX had an unusually high concentration of AI-themed ads. Not just one or two nods to “AI-powered” features, but a meaningful cluster. Roughly 15 of 66 commercials were classified as featuring AI in some way either as the product, the plot, or the production method itself.

That last category is important.

Some brands didn’t just advertise AI. They used AI to make the ad and made that fact the story. SVEDKA’s spot was positioned as entirely AI-generated. The message wasn’t subtle: look what this technology can create now. AI wasn’t just being sold. It was part of the creative toolchain.

But what made the night especially fascinating at least if you live in product and engineering was watching the category try to solve the same strategic problem in real time: How do you make a foundational technology feel normal? You could see the different answers play out.

OpenAI framed Codex as leverage for builders. AI as a continuation of human curiosity and making. Anthropic went sharper. It leaned into satire and essentially said, “Ads are coming to AI… but not to Claude,” turning monetization into a trust battleground. When two AI companies decide the Super Bowl is the place to litigate ethics, you know the stakes are high.

Google went emotional and domestic with Gemini’s “New Home” framing. AI as co-creator in a family moment. Amazon did something clever: it played directly into the fear people already have “what if the assistant turns evil?” and then defused it with humor around Alexa+. Meta and Oakley positioned AI glasses as performance gear, blending utility with lifestyle. Salesforce injected participatory energy, using game mechanics to make AI feel interactive rather than abstract.

Then there were the category expanders.

Ring showed computer vision as a neighborhood hero helping find lost dogs. Genspark leaned into the “AI autopilots your work so you can take Monday off” fantasy, which is either delightful or deeply provocative depending on how your week is going. ai.com went minimalist and cryptic, selling inevitability more than functionality.

Across all of them, you could see the same instinct: move AI from abstraction to application. From “intelligence” to “usefulness.” That’s smart. Because usefulness is how trust begins.

But here’s the harder question.

Did these ads actually make Americans like AI more? The clearest evidence we have is behavioral. EDO’s outcomes ranking showed ai.com driving 9X the engagement of the median Super Bowl ad, measured through immediate actions like search and site visits. That is a real signal. It reflects what people did, not what critics said. But attention is not trust. People search for things they love. They also search for things they don’t understand. Or things that worry them. Or things they think are overhyped. Outcomes tell you that you created a moment. They don’t tell you whether you created confidence.

Most of the ads tried to reduce anxiety by showing tangible value. Build a home. Analyze a game. Find a dog. Automate a workflow. The strategy was clear: make AI feel concrete, helpful, human-adjacent. AI is a general-purpose technology that touches employment, privacy, creativity, information flow, and power structures.

Super Bowl LX made AI unavoidable. It put AI at the center of the cultural conversation. It even put AI inside the creative process itself. But liking AI will not be won in a 30-second spot. It will be won in the first real workflow where it either helps or doesn’t.

I read less because it started to feel less useful.

Somewhere along the way, staying “on top of the news” stopped feeling like staying informed. More noise than signal. More interpretation than information. I still skim headlines. I still want to know what’s happening. But the daily habit that’s mostly gone.

When I talk to friends and colleagues, many say the same thing. We consume less, not more, even as access explodes. That is not a content problem. It’s a trust signal. And not a good one.

What changed?

It’s tempting to say the problem is bias. That’s the popular answer. But I don’t think bias is the real issue.

Bias has always existed in news. Perspective is unavoidable. Editors choose what to cover. Writers choose what to emphasize. Readers interpret through their own lenses. None of that is new, and none of it is inherently bad.

What is new is opacity.

Today, it’s increasingly hard to tell where facts end and framing begins. Two articles can report the same event accurately and still leave readers with completely different conclusions. Not because one is lying, but because word choice does the work. What’s emphasized matters. What’s omitted matters even more.

All of it shapes perception.

Historically, editors balanced it through judgment, norms, and time. Today, speed, algorithms, and engagement economics overwhelm both. Humans are still in the loop, but the loop is moving faster than they can reasonably manage. And over time, that erosion adds up.

Why reading the news feels exhausting now

Here’s what I’ve noticed in myself. Almost every article seems to want me to feel something before it wants me to understand something. Anger. Fear. Outrage. Vindication. Even hope is packaged with urgency.

That emotional tax compounds. Over time, you start asking a simple question: is this making me smarter, or just more reactive?

Here’s the uncomfortable truth. Machines already shape how we consume news. Algorithms decide what surfaces. Headlines are optimized for clicks. Feeds reinforce patterns of engagement. Whether we like it or not, AI already influences information flow. So the question isn’t whether AI belongs in news. It already does.

The real question is whether AI can help clarify rather than amplify. Can it separate facts from framing? Can it expose structure without enforcing ideology?

What AI is actually good at here

AI does not understand truth the way humans do. That’s important to say clearly. But it is exceptionally good at recognizing patterns in language and coverage. It can identify emotionally loaded terms. It can measure sentiment toward specific people or groups. It can track who is quoted, how often, and in what role. It can compare how different outlets frame the same event. And it can do this consistently, repeatedly, and at scale.

For example, a system can observe that coverage of a policy consistently highlights economic risk but rarely discusses social outcomes. Or that official voices dominate while community voices are missing. Or that language subtly shifts depending on who is in power. These are observable signals. Surfacing them is where AI adds real value.

A useful system should be able to say why an article leans a certain way. It should point to language choices, source selection, framing decisions, and omissions. It should show its work.

Once you can detect and explain framing, the next question is inevitable. Should AI do more than that? There are reasonable options, each with tradeoffs.

One approach is language normalization. The article remains intact, but the system highlights emotionally charged phrasing and suggests neutral alternatives while preserving factual meaning.

Another is contextual augmentation. The article stays exactly as written, but readers can expand background, historical context, or commonly cited counterpoints.

A third is comparative framing. Readers can see how the same story is covered across outlets, or view a synthesized summary that highlights differences in emphasis.

This is not a silver bullet. Models learn from existing language, which reflects social and cultural imbalances. Without continuous auditing, systems designed to expose bias can quietly introduce new distortions while claiming objectivity.

The question I keep coming back to

What if the goal of news wasn’t just to inform or persuade, but to help people think more clearly? What if, instead of asking “do I agree with this,” readers could first ask, “how was this constructed?” And maybe, if we get it right, reading the news can feel useful again - not because it tells us what to feel, but because it helps us see more clearly.

The calendar looks like an administrative tool. It is where invites go, where teams negotiate slots, where assistants protect open space. But it is something much more important.

It is the operating system of leadership. It decides where attention goes. It decides whether judgment is sharp or fragmented. It decides whether a leader is shaping the future of the product or reacting to the noise of the present.

The problem is that most calendars are built on inertia. They are filled with commitments that seemed harmless in the moment. A thirty-minute sync here. A progress update call there. A status update that “just FYI” One by one, they accumulate until the calendar is no longer a reflection of strategy. It is a reflection of drift. Leaders describe this as being “slammed,” but what they are really describing is a loss of control over their operating system.

Some leaders break this cycle by resetting. One leader I worked for makes it a 6 month ritual to wipe her calendar clean. She deletes every meeting—yes, every single one—and then rebuilds it from scratch. What gets added back must earn its place. If her focus for the quarter is adoption metrics for a new feature, her time flows into customer research reviews, funnel deep dives, and design discussions. If the focus shifts to scaling the product team, the calendar fills with hiring loops, onboarding sessions, and mentoring. The rhythm looks different each time, but the principle is the same: start from zero and rebuild around what matters now. The act is uncomfortable at first. It surfaces just how much of a typical calendar is built around habit rather than intention. But the discipline pays off, because it ensures time matches priorities.

Other leaders anchor their calendars around leverage. Jeff Bezos used to say that he scheduled “high-IQ decisions” in the morning, when his energy was sharpest, and refused to make major calls late in the day. Bill Gates famously took “Think Weeks” to immerse himself in reading and reflection, often coming back with insights that shaped Microsoft’s strategy. These weren’t productivity tricks. They were acknowledgments that judgment is a scarce resource, and that the calendar must be designed to protect it. Product leaders face the same truth. Your calendar should reflect where your judgment creates the most leverage. Sometimes that is in roadmap reviews where hard trade-offs are made. Other times it is in direct customer conversations that reset your intuition. Occasionally, it is in stepping away from the day-to-day entirely to think about the next platform shift. When the calendar fragments into endless back-to-backs, the quality of that judgment collapses. we all know that once focus is interrupted, it can take more many minutes to recover. A scattered schedule is not just exhausting—it is structurally incapable of producing deep insight. The most effective leaders design their calendars around focus.

The influence of a product leader’s calendar extends far beyond their own productivity. Every meeting you attend multiplies. It spawns prep work, cascades of follow-ups, and shadow meetings. A single one-hour review with ten attendees costs the team ten hours. Stretch that over a quarter and you’ve consumed weeks of execution time. That’s why canceling a low-value meeting is not an act of selfishness—it is an act of leadership.

That is the cultural power of a calendar. Teams watch how their leaders spend time, and they infer what matters. If your week is consumed by sprint reviews, the message is that short-term execution trumps long-term vision. If you carve out regular time with customers, the message is that strategy starts with user insight. If you visibly protect time for writing, teams internalize that clarity of thought matters more than speed of reaction.

The calendar, in other words, is more than a personal tool. It is a broadcast system. It tells your teams what you value without a single slide or speech. It shows them what matters, week by week, far more than any cultural manifesto ever could.

For product leaders, the stakes are even higher. Because the job is about judgment—about choosing where to place bets, what to ship, what to cut, and how to sequence—the calendar is the scaffolding that supports those choices. If you don’t design it, it will be designed for you. And if it is designed for you, it will always tilt toward the urgent over the important.

The discipline, then, is not about finding more time. It is about asking harder questions. What are the few things only I can do for this product? Where does my judgment create leverage, not just presence? What will I stop doing so I can create space for what matters?

Leaders who answer those questions honestly end up with calendars that look very different from the default. Their weeks are not jammed with everything. They are anchored around the handful of things that move the product forward. The rest falls away.

This is why some product leaders feel perpetually trapped in their calendars, while others use theirs as a source of clarity. It is not about effort. Both groups work hard. The difference lies in design. Some surrender their calendar to drift. Others treat it as their most important system.

The truth is simple: product leaders don’t lack time. They lack calendars that reflect their priorities. When they fix that, everything else changes. The roadmap sharpens. The team feels less scattered. Customers feel heard. Leadership becomes intentional again. Because in the end, the calendar isn’t just where you spend your time. It is how you spend time.

They spotted Jenga boxes arranged like numbers. They noticed that one row of tiles broke the symmetry of the rest — every shape was a square except one with rectangles, which turned out to be the key clue. The kids didn’t deliberate or analyze. They tested, guessed, failed, and tried again. Meanwhile, I stood back, realizing I would have needed hints at every turn.

That moment lingered with me. It made me wonder — when do we stop seeing what’s in front of us?

The Brain’s Transition from Explorer to Executor

Children approach the world as an open network of possibilities. Adults see it as a system of known rules. Somewhere along the way, we trade exploration for efficiency.

Psychologists call this functional fixedness: the tendency to see an object only in the way we’ve used it before. Once we learn that a Jenga block is for building towers, we stop asking if it could represent a number or a clue. Experience narrows our field of view.

In neuroscience, this shift has a clear biological explanation. Children’s brains are built for exploration. Their neural connections are more distributed and less specialized, meaning that when they face a new challenge, multiple regions of the brain activate together. Studies from MIT have shown that children display broader neural activation during problem-solving tasks — their brains “light up” across hemispheres, not just in one focused region. Adults, in contrast, show more modular, efficient activation patterns. We get faster, but less flexible.

This is the result of neural pruning — a process where the brain strengthens connections that are used frequently and trims away the rest. It’s an evolutionary advantage. It helps us master complex, repeatable tasks and adapt to stable environments. But it comes with a cost: we lose the ability to notice the unexpected.

A study published in ScienceDirect demonstrated that when exploring complex environments, children outperform adults precisely because they are less constrained by prior knowledge. Adults tend to exploit what they know. Children explore what they don’t.

The Bias of Experience

Another classic example of this trap is the Einstellung effect, first identified by psychologist Abraham Luchins in the 1940s. Once people learn a familiar way to solve a problem, they reuse that same method automatically — even when a simpler or better solution exists. The more expertise we build, the more we rely on it.

For executives, this pattern appears everywhere. A pricing problem becomes a “optimization” problem because that’s how it was solved last time. A slowdown in engagement becomes a “messaging” problem because we’ve seen it framed that way before. Experience sharpens instinct, but it also hardens boundaries.

The human brain is designed to filter distractions. But sometimes, the filter hides the signal.

There’s an irony here. As adults, we are surrounded by patterns — but we only see the ones that fit our domains. Product leaders see user behavior loops. CFOs see financial correlations. CTOs see system dependencies. We are experts at pattern recognition inside our professional sandbox. But outside it, we stop looking.

It’s not that we lose the ability to connect dots. We just build stronger neural pathways for the dots that matter to our work. That’s why innovation so often comes from the edges — from people who connect fields that don’t usually intersect.

The economist Richard Florida once described creativity as “the ability to make connections between seemingly unrelated things.” The most transformative innovations — from Jobs’ fusion of design and engineering to Netflix’s use of machine learning in entertainment — emerged because someone crossed a boundary most others never saw.

The Escape Room as a Mirror

In that escape room, the kids weren’t optimizing for speed or logic. They were simply playing. They were open to being wrong. Their curiosity wasn’t burdened by reputation or productivity.

Watching them, I realized curiosity isn’t the opposite of expertise. It’s what keeps expertise alive. It’s the willingness to ask “what if” even when you already know “what works.”

Executives often operate in environments that reward predictability and precision. But creativity lives in the moments when we suspend that precision.

In technology and business, the equivalent of “functional fixedness” shows up as organizational inertia. Teams become locked into familiar success patterns — the quarterly metric, the proven framework, the familiar KPI. Over time, the same focus that drives efficiency limits the range of discovery.

In many ways, leadership is the art of creating room for pattern rediscovery. The best teams I’ve seen combine the speed of execution with the looseness of play. They leave time for the unexpected.

Seeing Again

Every once in a while, moments like that escape room remind us what our filters hide. They challenge the bias of experience and make us see that pattern recognition is not just a cognitive skill — it’s an act of curiosity.

Children remind us that discovery begins where certainty ends. Their minds aren’t yet optimized; they are exploratory systems in motion. They remind us that curiosity isn’t about knowing less — it’s about being willing to be wrong more often.

Perhaps the real test of growing up, or of leadership, isn’t how efficiently we solve problems, but whether we can still see the invisible.

For product leaders, answering that question begins with proving whether AI features work for customers. That means moving beyond launch metrics and focusing on adoption, trust, and operational performance. Features that are technically sophisticated but unused quickly become expensive experiments.

Adoption is the first signal. If a personalization feature goes live, the right question is whether customers use it consistently, not whether it was technically hard to build. Spotify’s Discover Weekly playlist is a clear example. The company did not measure success by the number of playlists generated but by how often listeners returned. Repeat usage and satisfaction data showed that personalized playlists were delivering value. That loyalty translated into Spotify holding more than 30% of the global music streaming market. The lesson is simple: measure whether AI is becoming part of daily behavior.

Trust is just as critical. A chatbot that answers correctly 85% of the time but hallucinates 5% of the time risks undermining confidence in the entire product. In consumer settings, a handful of bad interactions can undo months of effort. Latency is another area where trust is fragile. Google found that users disengage when autocomplete suggestions take longer than 200 milliseconds. Product leaders should therefore monitor metrics like error rates, hallucination frequency, and response times. These details rarely appear in boardroom presentations, but they decide whether customers stay.

Operational efficiency is another domain where metrics matter. IBM reported that enterprises using AI in customer service reduced handling times by 40% while raising satisfaction scores. For product leaders, the operational question is whether AI features cut effort for customers and staff alike. Some organizations are adopting a new benchmark, the Levelized Cost of AI (LCOAI), which calculates the cost per useful AI output across a model’s lifecycle. By tracking this, product teams can compare whether in-house models or third-party APIs deliver better efficiency.

Metrics must also extend to risk and compliance. Fairness and transparency are operational realities. Product leaders should embed fairness checks into model validation pipelines and monitor demographic performance gaps. They should be able to report whether all deployed models passed bias audits, whether explainability coverage meets regulatory thresholds, and whether any high-severity AI incidents occurred. A compliance failure can halt launches or create reputational damage as quickly as a product defect.

The challenge is avoiding vanity metrics. Reporting that a model reached 92% precision or handled a million chatbot conversations may look impressive but does little if it does not translate into customer retention, revenue lift, or margin improvement.

Some companies now use shared scorecards to make the connection explicit. A recommendation team may note internally that accuracy improved by 10%. The same data can be translated into a 7% increase in average order value and a 5% uplift in retention among users exposed to recommendations. Balanced scorecards that blend financial outcomes, customer satisfaction, operational efficiency, and innovation throughput give organizations visibility at both the feature level and the strategic level.

The companies that succeed are those where product leaders treat model accuracy, adoption rates, and trust as inputs, and measure retention, revenue, efficiency, and compliance as outputs. They cut away vanity statistics and instead focus on metrics that prove value—incremental revenue, churn reduction, margin improvement, and trust maintained at scale.

We talk endlessly about compute capacity, model architecture, and data availability. But all of that presumes something more fundamental: that the lights stay on, the voltage stays steady, and the grid can deliver. Without that, AI’s theoretical capacity is just that—theoretical.

As I’ve read more about this, it’s become clear that we’re in the middle of a new industrial convergence: the digital and the electrical systems are fusing into a single strategic map. This map has five main pillars—grid infrastructure, clean baseload generation, storage and resilience, compute efficiency, and architectural design. Together, they form what I call The New Energy–AI Industrial Complex. The companies that understand this ecosystem—and secure their place within it—will define the next decade of AI leadership.

The Arteries of the AI Age: Grid Infrastructure

The grid is the circulatory system of the AI economy. No matter how powerful the heart, nothing flows without clear, unobstructed arteries. In this case, those arteries are high-voltage transmission lines, substations, and underground connections—and they are under unprecedented strain.

The scale of this challenge is staggering. The U.S. Department of Energy and Lawrence Berkeley National Laboratory now project that electricity usage from data centers will at least double, potentially triple, by 2028. This is a dramatic shift from the flat demand the grid has seen since 2007, with AI alone expected to account for 30–40% of all new electricity demand through 2030.

In July 2024, Northern Virginia—home to the largest cluster of data centers on Earth—narrowly avoided disaster. A transmission fault briefly took 60 data centers offline, pushing them to diesel backup. The sudden drop in demand risked a cascading outage across the entire grid. Dominion Energy now faces over 40 GW in pending data center load requests, roughly equal to California’s peak summer demand.

New high-voltage transmission lines in the U.S. take 7–10 years to build, bogged down in permitting, lawsuits, and environmental review. In contrast, China deploys ultra-high-voltage lines in a fraction of that time, connecting its renewable-rich western provinces to industrial hubs in the east—a speed advantage with clear implications for its AI ambitions.

Quanta Services (PWR) is one of the few companies with the capacity and expertise to execute large-scale builds like the 550-mile SunZia Transmission and Wind Project, which took 17 years to approve. These timelines illustrate the gap between AI’s urgency and infrastructure’s reality.

The Steady Hand: Clean Baseload Generation

AI’s demand curve is unlike almost any other—flat, relentless, and intolerant of interruptions. That’s why baseload generation—power available every hour of every day—is the unglamorous but essential foundation.

Big tech is no longer waiting for the grid to solve this. Amazon’s move to colocate with a Pennsylvania nuclear plant locks in guaranteed megawatts, independent of grid congestion. In Texas, some AI campuses are installing on-site natural gas turbines—trading carbon intensity for control. The choice of baseload source is a climate inflection point. Meeting demand with gas or coal will raise emissions. Meeting it with nuclear, hydro, or geothermal keeps AI growth aligned with decarbonization goals.

NuScale, TerraPower, and Westinghouse are leading the charge in small modular reactors (SMRs)—50–300 MW nuclear units that can be sited next to data centers. Standardized designs make them easier to permit and finance. TerraPower’s advanced load-following capabilities allow for seamless integration with intermittent renewables. In May 2025, the Nuclear Regulatory Commission approved NuScale’s SMR design—an important precedent for the sector.

Buying Time: Storage & Resilience

Even the best generation and transmission plans can fail. Storage buys you time—time to ride out a transmission fault, a renewable lull, or a price spike in the energy market. And it’s not just about minutes or hours anymore. Long-duration storage is becoming critical. Form Energy’s iron-air batteries, with up to 100 hours of discharge, could keep a facility running through multi-day outages. Tesla’s Megapacks already help data centers shave peak demand charges while serving as emergency backup.

Policy is enabling this market. FERC Order 841 opened wholesale markets to storage, unlocking an estimated 50 GW of potential capacity in the U.S. globally, countries like Australia, South Korea, and Germany are moving faster, deploying large-scale batteries and experimenting with hydrogen storage.

The first company to crack cost-effective, long-duration storage will hold a strategic choke point in the AI power stack.

Making Every Watt Count: Compute Efficiency

In a power-constrained world, efficiency is a growth multiplier. Every performance-per-watt gain extends the life of existing infrastructure, acting as a “virtual power plant.”

NVIDIA’s latest GPUs deliver significantly more compute per watt than their predecessors—effectively adding capacity without new grid connections. AMD’s MI300 accelerators and Cerebras’s wafer-scale processors pursue similar gains. Cerebras’s architecture, which eliminates chip-to-chip communication delays, can slash power use for certain AI workloads.

Efficiency is also a climate lever. Every watt saved reduces strain on grids and the need for carbon-intensive generation. The EU’s Energy Efficiency Directive will force transparency on data center energy use per workload; while U.S. rules lag, investor scrutiny is rising.

Chinese players like Biren and Huawei are also targeting efficiency as a differentiator. Closing the performance-per-watt gap could let them compete globally on both cost and carbon intensity.

Design as Strategy: Architectural Innovation

Data center design is where the upstream investments—in generation, storage, and efficiency—either compound or dissipate. Digital Realty has achieved a PUE below 1.2 in its Northern European modular builds by integrating rooftop solar, on-site batteries, and liquid cooling. Equinix is experimenting with seawater cooling in Singapore and hydrogen fuel cells in the UK, while reaching 96% renewable energy coverage globally.

Cooling remains a critical front. It can account for 30–40% of a data center’s total energy draw. Moving to liquid immersion cooling and locating in cooler climates can dramatically cut that load. In water-scarce regions like the U.S. Southwest, operators are being forced toward air-based or closed-loop systems.

The pillars of the New Energy–AI Industrial Complex are deeply interdependent. An SMR breakthrough could unlock new geographies for AI campuses. A leap in chip efficiency could delay the need for new transmission. An architectural innovation could change the economics of site selection.

AI strategy is now inseparable from energy strategy. Whether you own, lease, or partner for capacity, your moat may increasingly be measured in megawatts, not patents.

Product managers today operate in a landscape of abundance—abundant data, abundant opportunities, abundant noise. Everyone has an idea. Every PM wants “just one more” experiment. The surge of generative AI has only amplified this frenzy. Yet, the job of a great product manager—especially in this era—is not to chase every one of them. It’s to know which ones actually needs tending.

The Flood of “Ideas”

Every team—from sales to operations to customer care—has a wishlist. Some of those ideas stem from real customer pain. Many are driven by internal incentives. Add AI to the mix, and now everyone wants an LLM-powered chatbot, a co-pilot for their teams, a real-time trend predictor, and a generative campaign builder by next quarter.

The PM’s job is not to say yes to every idea. It’s to interrogate them.

• What problem does this solve?

• Whose problem is it, really?

• What does success look like—and is that success even measurable?

Too many roadmaps crumble under the weight of “important-sounding problems.” The result? You deliver features that look innovative, but don’t move the business. Or worse, features that move nothing at all.

Generative AI Is Not the Strategy

It’s tempting to treat generative AI as the strategy. But it’s just a tool. The real question is: What is your business trying to become? You don’t need a GPT wrapper on every customer touchpoint just because you can build one.

A product manager working in AI should start not with the model, but with the moment.

What are the moments in a customer’s journey where uncertainty blocks action?

What decisions does a partner team member make a hundred times a day that software can simplify?

What context does an algorithm lack that a generative layer could fill in?

Start there. Because the moment is where impact lives.

The Discipline of “No”

Saying “yes” is cheap. Saying “no” is costly. But that’s where product judgment lives. You’ll be asked to support a prototype that has an executive sponsor. You’ll get pressure to prioritize “that one thing Legal really wants.” You’ll face suggestions that “won’t take much engineering time.” It is your job to say no when the idea doesn’t tie to a customer journey, a business driver, or a measurable outcome.

A good product manager filters. A great one teaches others to filter.

Most importantly, Ask Better Questions

Before greenlighting any project, ask:

• Are we solving a priority problem? Not a theoretical one, but one felt by customers.

• Do we have the foundation? AI is expensive, both in infra and failure. Do we have the data, the integration points, and the organizational will?

• Is it measurable? Can we isolate the impact? Will we even know if it worked?

• Does it scale? If the pilot works, can we roll it out across categories, platforms—or will it die in a corner?

If the answer to most of these is no, don’t do it. At least, not now.

Zoom Out, Then Zoom In

Generative AI has opened up a new design space for everyone. We can reimagine everything from how customers interact with our business across every thoughpoint. But don’t start with the tech. Start with the value.

Zoom out. What is the business trying to become in 3 years?

Zoom in. What is the smallest meaningful step we can take today to test a wedge into that future?

That’s the rhythm. That’s the job.

I had the opportunity to speak at Walmart’s Product Summit 2025, followed by an engaging listening session with some of the sharpest product thinkers and leaders across the company. This is the talent I get to work with—and learn from—every day, a privilege I don’t take lightly.

Below are a few snippets from the talking points I shared with the audience.

Product leadership is always about building with intention. It’s deliberate evolution—anchored in purpose, strengthened by culture, and propelled by innovation.

Start with clarity. Clarity of mission. Clarity of who we serve. Clarity on how teams align to that mission. From day one, ground yourself in first principles: start with the customer, build what matters, and never drift from the what and the why.

Treat culture as your operating system. That’s the foundation for scale. Hire not just for resume, but for value alignment. Talent might get you started. Culture builds momentum which builds scale.

High-trust, high-velocity organizations don’t happen by accident. You build them. Through intent. Through repetition. Through systems that let teams move fast, learn fast, and stay focused on real problems. Make it safe to challenge assumptions. Make it normal to speak up early. Make it expected to iterate quickly.

Now, as we’re stepping into a new phase—where AI isn’t just influencing how we build. It’s not just about x% productivity wins. It’s transforming what our customers expect and need. This shift isn’t theoretical. It’s happening now.

Now what will matter most is speed—speed of iteration, speed of insight, and speed of decision-making. That’s where product roles are evolving fast.

The fundamentals of product leadership haven’t changed. A sharp understanding of customer needs. The ability to tell a clear story. The judgment to make hard calls in ambiguity. The responsibility to think beyond the next sprint and design for long-term impact. That’s the constant.

But the tools and the tempo are different. Imagine a product manager who used to spend three days reading customer feedback across tickets, reviews, and survey data. That work now takes thirty minutes. But more importantly, that time saved means more cycles to learn, experiment, and refine.

It’s not just 10X faster. It’s 10X more adaptive.

That’s the new expectation.

The surface area of what one product can influence has expanded. So has the pace at which we can test, fail, and improve. As this accelerates, leaders need to invest in leverage—a word that now means much more than efficiency.

Leverage is about compounding impact. It’s about making decisions, building tools, and fostering relationships that scale beyond any one product or team. Instead of solving a problem once, we ask: can this solve for many? Can we build once, but learn across ten different use cases? Design for tomorrow’s extensibility.

As product leaders, we’re stewards of both purpose and pace.

AI will redefine how we work. But it’s not a replacement for product judgment or customer understanding. It won’t replace customer empathy. It’s an accelerant. And when paired with strong culture, real empowerment, and a bias for action, it unlocks something powerful. It will amplify all of it.

This moment demands that we build intentionally. Be curious. Think systemically. Lead with trust. And scale with focus.

And that’s how we build something that lasts.

“It’s now a baseline expectation that everyone at Shopify uses AI in their workflow.”

He made another rule. Before any team adds headcount, the manager must prove that AI can’t do the job. Shopify is shifting from AI-enabled to AI-native. And it’s doing so by resetting the company’s expectations.

Shopify isn’t a slow-moving company. But scaling a product is not the same as scaling a mindset. Lütke’s AI mandate is a direct attempt to reshape the culture—not just through values, but through metrics. AI fluency will now be considered in performance reviews. Peer feedback will reflect it. Hiring decisions will route through it.

This is not easy to execute. New expectations risk becoming shallow rituals. Teams might adopt AI tools superficially—asking ChatGPT to write recaps or using code assistants for boilerplate—but stop short of integrating AI into real decision loops. And in roles like enterprise sales or legal, where nuance and trust drive outcomes, AI may not be a substitute at all.

Execution, not ambition, will define whether this works.

Still, this shift is not just defensible—it’s overdue. Across sectors, AI is already reshaping how customers discover, buy, support, and engage. AI is no longer a competitive advantage. It’s becoming the infrastructure layer of the modern company. If your customers are AI-native—and your competitors are AI-native—your company can’t afford to operate on pre-AI workflows. This memo is simply a reflection of this new normal.

This decision is also financially sound. Lütke is not cutting costs blindly. He’s raising the bar for resource deployment. Capital—especially human capital—should follow a clear logic: use automation where you can, hire when you must. That’s smart governance. Investors want productivity without unnecessary sprawl. And employees who learn to work effectively with AI today will lead the workforce tomorrow.

But again, success depends on how this is rolled out. If the policy outpaces the tooling, adoption will feel forced. If the shift is top-down only, middle management may become the blocker. And if experimentation is penalized rather than rewarded, the cultural reset will stall.

The leadership playbook now requires both: cost discipline and capability building.

Shopify’s bet is that a company of 10,000+ people can operate with that level of clarity. If it works, it could become the new standard for modern tech organizations.

Clear expectations. Leaner teams. Faster loops. Higher standards. Love it.

Grove wrote his book when he led Intel through fierce competition in the semiconductor space. His experiences taught him that sudden shifts in market conditions or technology can threaten a company’s existence if leaders fail to adjust. He called these moments “strategic inflection points.” They demand swift but thoughtful action. Although Grove focused on the microprocessor business, his lessons apply to the present wave of AI technologies. Machine learning and deep learning methods can revolutionize operations, upend traditional business models, and force executives to rethink core strategies, even the ones that have worked for decades.

AI’s reach today extends into every major sector. In finance, it powers algorithmic trading, fraud detection, and risk management systems that learn from real-time data. In healthcare, AI-assisted diagnostics and personalized treatment plans may change how patients receive care. In manufacturing, AI will drive predictive maintenance, where sensors alert technicians to looming failures before they halt a production line. In logistics, AI optimizes routes, reduces fuel use, and shortens delivery times. Grove’s call to stay paranoid is not about fear—it is about awareness. Success can breed complacency, and complacency leads to a lack of attention when the winds of change begin to blow.

Leaders often fail to spot a strategic inflection point because early signals may look minor or confusing. AI took years to move from academic circles to real-world deployments. But now, developments come so quickly that we are seeing everyday breakthroughs. And these can reshape entire markets in months. For instance, improvements in all kinds of chatbots, document analysis tools, and virtual assistants has me hooked. These tools always start out looking like neat demos, but soon they alter how companies interact with customers, handle sales inquiries, and process large datasets.

When Grove advised leaders to remain paranoid, he meant they should stay attuned to how subtle changes can gather momentum until they disrupt the foundation of a business. This paranoia is constructive. It prompts decision-makers to test new technologies early, question routine assumptions, and stay vigilant about emerging competitors. Leaders should watch for signals within their organizations as well. Teams that experiment with small AI projects can reveal whether a new tool is a gimmick or a game-changer. Data scientists and engineers can often spot opportunities to automate processes or derive insights faster. They may notice patterns the rest of the company misses.

Grove famously shifted Intel from memory chips to microprocessors. That pivot saved the company from fierce Japanese competition in the 1980s. In a similar way, AI might force a hospital network to invest in AI-driven patient triage, or a financial services firm to adopt machine learning models that predict market fluctuations. The painful part of these pivots is that they require leaders to abandon comfortable routines. As Grove wrote, “Success breeds complacency. Complacency breeds failure.” The message rings true for AI: it is not enough to sit on a positive track record. One must keep pushing to see whether the latest advances can strengthen or undermine the current business.

Implementing AI can look different across industries, but the principles remain the same. In finance, advanced analytics can help underwrite loans, forecast market risks, or automate compliance checks. In healthcare, new AI algorithms might read medical scans or predict disease progression. Each application reveals hidden challenges. Data privacy, cybersecurity, and bias in AI systems are just a few. Leaders cannot ignore these concerns. They must balance the urge to innovate with an understanding of regulatory requirements and ethical implications.

Grove believed that tech executives must be willing to take risks when they detect a strategic inflection point. In AI, that can mean launching pilot programs to see how well a tool handles real-world data. If the pilot succeeds, scaling becomes a priority. If it fails, leaders gather lessons and get better. Grove’s approach encourages constant experimentation. It also requires candid discussions at high levels. Executives need to ask whether the entire business model will remain viable if a competitor uses AI more effectively. This kind of conversation can be uncomfortable. Yet Grove’s enduring wisdom is that ignoring major shifts only postpones the day of reckoning.

Culture plays a critical role here. Grove saw that teams often resist big changes. Leaders need to foster an environment where managers and employees feel safe challenging old methods. When AI solutions emerge, people may be tempted to cling to manual processes. Constructive paranoia pushes them to ask if those manual processes might soon lose value. That is why Grove stressed open communication and debate. In today’s AI context, a culture of experimentation and questioning can help surface new ideas. It can also reveal where AI might fail. Spotting failures early is essential. It limits costs and guides leaders toward more promising opportunities.

Another dimension is talent. AI demands skills in data science, software engineering, and product management. Grove’s perspective suggests hiring people who not only have the right technical background but also share the company’s sense of urgency. In times of strategic upheaval, these hires can help drive change. They can point out which processes are ripe for automation or where AI algorithms might yield the highest impact. Partnering with outside experts can help, but Grove would argue that businesses need to cultivate paranoia from within, across all levels of the organization.

Keeping a watchful eye on competitors remains vital. Grove noted that change often arrives from unexpected directions. An automotive manufacturer might suddenly face competition from a tech giant that builds AI-powered vehicles (we all know this story). A streaming service might contend with a new startup that leverages cutting-edge recommendation systems. Vigilance means analyzing these moves and deciding if they signal a larger shift. It also means examining your own strategy. If rivals adopt AI to reduce costs or offer superior products, can you respond quickly, or are you already behind?

Grove also warned leaders to avoid reacting too late. After a strategic inflection point becomes obvious to everyone, the window for an easy pivot may have closed. Early detection is key. By investing in R&D, setting up internal incubators, or collaborating with research on AI , a company can spot trends before they explode into full market disruptions. Early adopters often gain a head start, refining their AI models and building a data-driven culture. Latecomers might spend years catching up—and may never fully recover their market position.

Throughout Only the Paranoid Survive, Grove underscores that the key lies in embracing the necessity for change before it becomes a crisis. In the AI era, that means understanding that machine learning, deep learning, computer vision, and natural language processing can shake up even the most stable enterprises. Being paranoid in a constructive way means continuously assessing whether your business model remains sound, whether your processes need modernizing, and whether your team has the right blend of urgency and expertise.

For leaders, Grove’s message is a directive to stay humble before disruptive technology. AI can empower organizations to achieve new heights, or it can erode a company’s market position if leaders cling to old ways. The difference lies in how quickly they acknowledge AI’s rise and adapt to it. Strategic inflection points are seldom gentle. They demand bold leadership, a culture that tolerates risk, and a willingness to challenge the status quo. As Grove would remind us, the greatest threat often comes when you believe all is going well. By staying vigilant, open to change, and ready to move, you ensure that “paranoid” thinking is the engine that drives reinvention, growth, and long-term relevance in a world shaped by AI.

This constant shifting isn’t unique—it’s the reality of being an executive. The real question isn’t how to avoid it but how to master it.

Context switching drains you

Neuroscientists call this the “switching cost.” Every time you jump from one cognitive mode to another, your brain has to reorient, consuming mental energy. Studies show that frequent task-switching can reduce productivity by up to 40% (American Psychological Association). The problem isn’t just lost time—it’s the cognitive drain. Your brain doesn’t seamlessly shift between strategy, crisis management, and execution. Each requires a different mental framework. Strategic thinking relies on abstraction and foresight. Crisis response demands rapid decision-making. People management needs patience and emotional intelligence.

Constantly toggling between these modes depletes your focus and increases stress.

But some manage this chaos better than others. Here’s my toolkit.

#1: Structure your day around energy, not just time

It’s not just about scheduling—it’s about sequencing. Align your calendar with your cognitive strengths: For me - it revolves around

• Mornings for deep work: Strategic discussions, complex decisions, and long-term planning.

• Midday for tactical execution: Shorter, action-driven meetings and operational updates.

• Afternoons for people-focused work: One-on-ones, coaching, and team discussions.

Does not always happen, but personally, the days I am able to batch my work into cognitive “zones”, I experience lower decision fatigue and higher clarity.

#2. Use mental hooks to switch context faster

Before switching topics, I often use a quick framework to reset. I even write these down, and read them before the meeting.

• For strategy: e.g. What’s the long-term impact? What second-order effects matter?

• For crises: e.g. What’s the immediate action? What information is missing?

• For people issues: e.g. What’s the real problem? How do I help them feel heard and supported?

Think of this as shifting gears in a car. A quick mental hook prepares your brain before you dive into the next discussion. Break everything down to first principles, removing unnecessary complexity before making decisions.

#3. Create a buffer between meetings

Moving straight from one meeting to the next leaves no time to reset. Instead, build a short transition ritual. I am a fan of 5 minute gaps - end 5 mins early, or start 5 mins late. Write down action items, and key takeaways from the last meeting. Writing is key for me. It makes it real for me. Quickly think through the main goal of the next one. Take a deep breath - controlled breathing improves cognitive function under stress. This moment of clarity before major meetings helps ensure focus and efficiency.

#4. Delegate and the right way

Not everything needs your attention. That’s true. If you’re in every decision, you’re a bottleneck. That’s also true. Delegate tasks to who are closer to the work. Empower teams to handle crises with predefined playbooks. Set clear decision rights so escalations only reach you when truly necessary. Automate routine decisions (pre-planned meeting agendas, standardized approval processes). I write them down, and revise them once a quarter or so. But for every other time, standardization helps. I am also starting to offload low-priority tasks to AI.

Books & Resources on Managing Context Switching

1. "Deep Work" – Cal Newport: On minimizing distractions and building focus.

2. "The Effective Executive" – Peter Drucker: Drucker’s framework on prioritization and decision-making.

3. "Essentialism" – Greg McKeown: How to do less but better, reducing decision fatigue.

4. Harvard Business Review Articles on Decision Fatigue & Time Management: Search for articles on reducing context-switching and improving decision-making.

Context switching isn’t going away. The best don’t resist it—they build systems to handle it better.

Some industries grow in parallel, but others are locked in a fate where they either rise together—or collapse together. IMO, AI and energy belong to the latter. AI demands more and more power; the energy sector scrambles to supply it. If energy lags, AI slows. If AI grows faster than energy infrastructure can handle, data centers become bottlenecks.

It’s easy to see these relationships in hindsight. Smartphones and semiconductors. Cloud computing and broadband expansion. Social media and digital advertising. But the real insight—the one that creates fortunes—is recognizing these dependencies before they become obvious.

Twelve months ago, it was clear AI would need more energy. What I didn’t imagine was the sheer scale of the demand surge (and the gap it’ll create).

AI’s Power Consumption: A Five-Year Surge in One Year

AI’s hunger for power has accelerated beyond every forecast. In 2024, data centers, AI workloads, and cryptocurrency mining consumed about 460 terawatt-hours (TWh) of electricity—nearly 2% of global electricity demand (International Energy Agency, 2024). By 2026, AI’s power needs alone could surge by 550%, reaching 286 TWh, and by 2030, the number could hit 652 TWh—more than the entire country of Japan consumes today (Forbes, 2024).

The chart below illustrates just how fast AI's electricity needs are expected to rise:

The Data Center Expansion Race is only accelerating: Tech giants are racing to build out infrastructure. Microsoft plans to spend $80 billion to scale its AI-ready data centers across North America. Google has placed orders for small modular nuclear reactors (SMRs) to power its AI-driven infrastructure (The Guardian, 2024). Amazon and Meta are investing heavily in natural gas plants to ensure power stability.

Why is this happening? AI models like GPT-4, Gemini, and Claude have made massive computational leaps. Training these models requires thousands of high-performance GPUs, each consuming 300-700 watts per chip under full load. A single AI model training cycle can use as much energy as 100,000 U.S. homes do in a month. The rise of real-time AI applications—like ChatGPT-powered search engines, AI video processing, and self-driving car simulations—has made energy costs a business-critical factor for tech companies.

The Energy Industry’s Response: Scrambling to Keep Up: This AI-driven demand has forced an unprecedented transformation in the energy sector.

Nuclear energy, once considered outdated, is making a return. Microsoft has signed deals to reopen Three Mile Island’s nuclear plant, tapping into stable, carbon-free energy. Google is betting on small nuclear reactors, securing contracts to build AI-dedicated power sources.

Natural gas, often seen as a transitional fuel, is seeing renewed investments. Microsoft has partnered with utility providers in Wisconsin to build gas-powered AI data centers. Meta and Amazon are backing large-scale gas plants in Louisiana and Mississippi (Business Insider, 2025).

Renewable energy remains in focus, but challenges persist. Shell is using AI to optimize power grids, reducing energy waste and improving efficiency in industrial processes. But solar and wind projects alone cannot yet meet AI’s demand spikes—especially as AI workloads require continuous, always-on power rather than intermittent energy sources.

The Financial Shift: AI is Making Utilities Hot Again: This is where the fun begins. For years, the stock market viewed utilities as boring, low-growth investments. AI has flipped that narrative. Hedge funds that once chased high-growth tech stocks are now moving into power generation companies. Vistra and Constellation Energy—historically slow-growth utility stocks—have become top performers in the S&P 500 .

Power companies are now AI enablers, making them some of the most valuable assets in the tech supply chain.

The Next Big AI-Driven Resource Battles

AI and energy are just one part of the equation. What’s the next big industrial connection that will shape the future?

• AI and Water: Data centers need enormous amounts of water for cooling. With each AI data center consuming millions of gallons per day, the industry is already putting pressure on global water supplies. Google’s data centers consumed 4.3 billion gallons of water in 2023, a 20% increase over 2022. Microsoft’s AI training pushed its water consumption up by 34% in a single year (Nature, 2024). Major AI hubs like Phoenix, Dallas, and Las Vegas are in drought-prone areas, making water supply a long-term constraint. Prediction: The water-tech industry will boom as companies scramble for better cooling solutions, desalination, and closed-loop water recycling systems.

• AI and Rare Earth Metals: The AI hardware boom is fueling a global rush for rare earth elements like lithium, cobalt, and neodymium—materials essential for GPUs, superconductors, and quantum computing. A single Nvidia H100 AI GPU requires 30+ rare materials, many of which come from China or politically unstable regions. The price of lithium has surged by 500% over the last five years, driven by demand from AI and EVs. The U.S. and EU are investing billions in rare earth mining to reduce dependence on China, setting up a geopolitical battle over mineral supply chains. Prediction: Expect massive investments in rare earth mining, alternative chip materials, and AI-designed resource extraction to counteract shortages.

Seeing the Future Before the Market Does: The AI-energy link was obvious in hindsight. The real challenge—the real goldmine—is predicting the next great industrial dependency before it plays out. AI and energy are now locked together. The next wave of AI-driven industries is forming. If you see the right pairings before the world does, you win big. And if you’re wrong? The market will remind you.

Adaptability is a necessity in business. Industries shift, technology disrupts, and companies that fail to evolve disappear. Pivot or Die by Gary Shapiro sets out to make this case. Shapiro, CEO of the Consumer Technology Association and the force behind CES, uses his book to argue that leaders must pivot or risk extinction.

The premise is strong (and somewhat obvious in the world today). Pivot or Die is an easy read on a short flight. The execution, however, feels weak. The book is packed with personal anecdotes and case studies, but it lacks a structured approach to decision-making. The stories, while engaging, often feel disconnected, making it hard to walk away with a clear playbook.

Pivot or Become Obsolete

Shapiro emphasizes that companies must pivot when market conditions change. He categorizes pivots into four types:

1. Startups Pivot to Survive – A company launches with one idea but realizes it must shift to succeed. Amazon started with books but expanded into e-commerce. This is well-documented, little new insight.

2. Businesses Forced to Pivot – External shocks like regulatory changes or pandemics force companies to rethink their models. Book mentions how COVID-19 pushed businesses online, but the discussion lacks depth. IMO - here the real challenge isn’t recognizing the need to pivot—it’s knowing how to execute successfully.

3. Failure Drives a Pivot – Learning from mistakes is critical. Book cites Netflix’s shift from DVDs to streaming, a well-known example. It doesn’t explore the internal decision-making that made this pivot work, which would have been more valuable.

4. Success Requires a Pivot – Even thriving companies must evolve. Apple moved beyond computers to consumer devices. The book acknowledges this but doesn’t break down how successful businesses decide when to pivot or which bets to make.

The author is a strong storyteller. He shares personal experiences and business cases that keep the book moving. If you enjoy business stories, this makes for an easy read.

The biggest weakness is its lack of depth. The book reinforces the idea that pivots are necessary, but it doesn’t provide a clear framework for how leaders should approach them. My all time two favorite books - Andy Grove’s Only the Paranoid Survive and Eric Ries’ The Lean Startup offer more structured approaches with clear methodologies.

Shapiro’s stories, while engaging, feel disconnected. There’s no strong throughline, making it difficult to extract actionable takeaways. The book is also light on data. While case studies are useful, they need to be backed by more research to make a compelling argument.

Pivot or Die is a good read if you want a collection of business stories about change and adaptation. But if you're looking for a deeper exploration of how to identify and execute a pivot, it falls short. It’s entertaining but not necessarily instructive.

If you are looking for a approach to strategic pivots, books like The Innovator’s Dilemma by Clayton Christensen or Measure What Matters by John Doerr provide clearer frameworks backed by data.

If you’ve read Pivot or Die, I’d be curious to hear your take. Did you find it useful?

AI, with its breathtaking pace of innovation, is driving seismic shifts across sectors. But beneath the surface, its rise is placing immense pressure on the energy industry, especially electricity generation. This isn’t merely about AI’s transformative potential—it’s a story of how technological revolutions ripple through downstream industries, creating opportunities, challenges, and profound economic and environmental consequences.

For informational purposes only: this blog explores these themes.

AI’s energy consumption is staggering. Training a single large AI model like OpenAI’s GPT-4 requires hundreds of megawatt-hours (MWh) of electricity—the equivalent of what an average U.S. household consumes over several decades. Inference, or running these trained models, consumes just as much energy, particularly as AI applications become ubiquitous across industries. A study by MIT found that the energy required to run large models at scale rivals that of small data centers.

This voracious energy demand has already made data centers one of the largest energy consumers globally. In 2021, they accounted for 1-1.5% of total global electricity consumption. Projections by the International Energy Agency suggest this figure could soar to 8% by 2030.

As these hubs of computation expand to accommodate AI workloads, energy providers that can meet this demand are seeing opportunities for growth. Companies like NextEra Energy, the leader in wind and solar energy, are particularly well-positioned to benefit from this trend. Their ability to scale renewable energy projects to meet AI’s demand makes them critical players in this ecosystem.

Energy Under Pressure

AI’s energy demands are straining power grids in unexpected ways. In North America, the North American Electric Reliability Corporation (NERC) has warned that the confluence of AI-driven data center expansion and the electrification of vehicles and homes risks destabilizing grids during peak demand. Ireland’s grid is under similar strain, with data centers consuming more electricity than all urban households combined—a reality that forced the government to halt new developments near Dublin until 2028 !!

This pressure is driving transformation in the energy sector. Renewable energy companies like Enphase Energy, which provides solar solutions, and Ormat Technologies, specializing in geothermal energy, are emerging as vital partners to AI-dependent industries. Their focus on delivering consistent and sustainable energy is not only meeting current demands but also attracting significant investor interest.

Energy storage innovators, such as Tesla with its Megapack batteries, are stepping in to stabilize grids overwhelmed by AI’s 24/7 power needs. These grid-scale solutions ensure that intermittent renewable energy sources like wind and solar can meet the continuous demand generated by AI applications.

At the same time, traditional energy companies are under pressure to adapt. Utilities that embrace AI-driven grid management solutions are positioning themselves for long-term success. For instance, partnerships with AI startups to predict energy demand and optimize grid operations are becoming increasingly common. Schneider Electric’s acquisition of AVEVA underscores the growing importance of energy optimization technologies in this new era.

The search for stable, clean energy sources to power AI is driving renewed interest in nuclear power and accelerating investments in renewables. Nuclear startups like Oklo, backed by Sam Altman, are developing small modular reactors (SMRs) designed to meet the energy demands of data centers. These reactors offer a reliable, carbon-free energy source that aligns with the sustainability goals of major AI-driven corporations. Meanwhile, Microsoft’s exploration of nuclear power, including the potential reopening of the Three Mile Island plant, signals growing corporate interest in this technology.

Renewable energy providers continue to play a critical role. Companies like Ørsted, a leader in offshore wind, are working on large-scale projects to provide clean energy to AI-driven industries. At the same time, emerging renewable technologies like enhanced geothermal systems (EGS) are being explored as potential solutions for meeting base-load energy demands. These advancements position the renewable energy sector as a cornerstone of AI’s future.

The interplay between AI and energy is reshaping the energy industry in profound ways. Renewable-rich nations like Norway, with its hydropower, and Chile, with its vast solar resources, are emerging as global leaders in AI energy provisioning. Meanwhile, energy companies that invest in sustainable, scalable solutions are attracting attention from both investors and corporations reliant on AI.

The surge in AI-driven energy demand is also transforming financial markets. Companies at the intersection of AI and energy, like NextEra Energy and Tesla, have seen their valuations soar as investors recognize their critical role in powering the future. Similarly, firms that develop energy-efficient infrastructure, from battery storage to smart grids, are poised to grow alongside AI’s expansion.

The future of AI depends on our ability to meet its energy needs sustainably. This requires collaboration between companies, governments, and energy innovators. By investing in renewable energy, exploring alternative energy sources like nuclear, and building advanced storage solutions, we can ensure the AI revolution drives progress without compromising the planet.

AI’s promise is immense—from transforming industries to solving global challenges—but its future hinges on energy systems that are up to the task. As this ecosystem accelerates, here are the energy companies I will want to watch:

• NextEra Energy: A leader in renewable energy production, focusing on scaling wind and solar projects to meet the demands of AI-driven industries sustainably.

• Tesla: Pioneering grid-scale battery storage solutions with its Megapack systems, crucial for stabilizing renewable energy supply as AI operates around the clock.

• Oklo: Innovating in small modular nuclear reactors designed to power energy-intensive data centers and AI ecosystems reliably.

• Enphase Energy: Delivering advanced solar energy solutions, including microinverters and storage systems, to align with AI’s increasing energy demands.

• Ormat Technologies: Developing enhanced geothermal systems (EGS) as a sustainable and consistent energy source for AI-driven economies.

• Ørsted: Driving offshore wind energy projects to scale renewable power generation for data centers and industrial AI applications.

• Schneider Electric: Expanding its suite of energy management technologies to optimize and automate energy use for AI-intensive infrastructure.

These companies are reshaping the energy landscape to align with the rapid growth of AI, positioning themselves as key players in a future defined by both innovation and sustainability.

The Federal Reserve's manipulation of the federal funds rate is crucial in shaping economic conditions. This rate, which dictates short-term borrowing costs for banks, is the Fed's primary tool for controlling inflation and guiding economic growth. An increase in this rate generally aims to temper an overheated economy by making borrowing more expensive. Conversely, a decrease is intended to spur economic activity by making credit cheaper.

Impact on Commodity Markets

Exchange Rate Fluctuations: Interest rates significantly impact exchange rates, which in turn affect commodity prices. When the Fed raises rates, the dollar typically strengthens due to higher yields attracting foreign investment. A stronger dollar makes dollar-denominated commodities, such as coffee, cheaper. For instance, if the dollar appreciates, coffee beans imported from Colombia might become less expensive, potentially lowering prices at your local café. Conversely, a rate cut can weaken the dollar, making imported coffee more expensive and potentially raising prices for your favorite brew.

Fun Fact: Coffee is one of the most traded commodities globally, with the International Coffee Organization estimating that coffee accounts for 15% of global trade. Thus, U.S. interest rates can influence coffee prices well beyond domestic borders.

Cost of Capital: Interest rates also affect the cost of capital for businesses, including those in the coffee industry. Higher rates increase borrowing costs for coffee shops and roasters. For example, if a coffee shop needs a loan for equipment upgrades, higher interest rates mean greater interest payments. To counterbalance these increased costs, coffee shops might raise prices on their beverages. Conversely, during periods of low interest rates, businesses can invest in advanced technologies, enhancing the quality and variety of coffee.

Fun Tidbit: The coffee industry has embraced innovations like robotic baristas and AI-driven inventory systems, partly funded by loans with historically low interest rates. These technologies can improve the consistency and quality of your coffee.

Consumer Behavior and Economic Conditions

Economic conditions influence consumer behavior and, consequently, coffee consumption patterns. High interest rates can reduce disposable income, leading consumers to cut back on discretionary spending, including premium coffee drinks. This reduction in revenue might prompt coffee shops to increase prices or reduce promotions.

Surprising Fact: During the 2008 financial crisis, coffee sales actually increased. Consumers turned to affordable luxuries like coffee as a small indulgence in a time of economic uncertainty.

Inflation and Supply Chain Dynamics

When inflation is high and the Fed raises rates to address it, the costs for coffee producers can rise. Increased operational costs may be passed on to consumers, leading to higher coffee prices. This scenario can be further intensified if global coffee prices are also rising due to inflationary pressures in coffee-producing regions.

Fun Fact: Brazil, the largest coffee producer, experienced a severe drought in 2021 that significantly impacted global coffee prices. Such weather-related shocks can exacerbate the effects of inflation and interest rate changes on coffee prices.

The coffee supply chain is also sensitive to macroeconomic conditions influenced by interest rates. Higher interest rates can elevate financing costs for logistics companies transporting coffee beans. These increased transportation costs can, in turn, lead to higher consumer prices for coffee.

Fun Tidbit: The global coffee supply chain is so extensive that a single bean can travel over 10,000 miles from farm to cup. Changes in interest rates and global trade policies can significantly impact the cost of this journey.

The coffee industry’s investment in technology is another factor impacted by interest rates. Advances in coffee roasting technology and automation, financed through loans, have improved the consistency and quality of coffee production. High interest rates increase the cost of such investments, potentially affecting production costs and coffee prices.

Surprising Fact: The coffee industry is increasingly adopting blockchain technology to improve traceability and transparency in coffee supply chains. This innovation helps ensure fair trade practices and high-quality standards but requires substantial investment, which can be affected by interest rates.

The next time you savor a cup of coffee, consider the complex economic forces influencing its price and quality. From the strength of the dollar to the cost of capital and inflationary pressures, understanding these connections adds depth to your appreciation of your daily brew. This intersection of monetary policy and coffee exemplifies how macroeconomic decisions can impact even the simplest pleasures in life.

The $2 trillion+ question is if the current dominance of Nvidia is facing a challenge. Intel, Meta, Microsoft, Google, and Amazon are throwing their hats into the ring.

Intel's Gaudi 3 chip, recently unveiled, is potentially a game-changer (don’t know). It's talked about as a 50% speed increase over Nvidia's offering for training AI models. AND Gaudi 3 promises a 1.7 times better performance for training LLMs. Then, unlike Nvidia's platform, which relies on proprietary architecture, Gaudi 3 can work across different software ecosystems. This is an interesting move, trying to win over smaller players who might not have the financial resources to invest in Nvidia's specialized platform.

Meta’s custom-designed Meta Training and Inference Accelerator (MTIA) might not be the sheer powerhouse some competitors offer, but it gives them a crucial advantage: independence. By developing their own chips, Meta reduces their reliance on Nvidia, giving them more control over their AI infrastructure.

Then, Amazon and Microsoft are joining the fray. AWS is leveraging their expertise in custom silicon (like their Graviton and Trainium chips) to develop even more specialized offerings for AI workloads within their cloud platform. This could lead to more competitive pricing and a wider range of options for cloud-based AI development. Highly recommend reading its coverage in the AMZN’s latest shareholder letter. Microsoft Azure unveiled their custom AI chip, codenamed Maia. Developed in collaboration with OpenAI, Maia is designed specifically for LLMs, a key area of focus for every enterprise out there. These cloud giants will further intensify the competition, ultimately benefiting developers and businesses with a wider array of choices and potentially more competitive pricing. They will sell services, and potentially dis-intermediate the chip makers changing the market dynamics.

This competition is fantastic news for the future of AI. It promises a wider range of powerful and accessible chip options, ultimately accelerating innovation in the field. The demand for efficient and powerful AI chips will only grow. Here's why:

• Wider Range of Powerful and Accessible Options: No longer will developers and businesses be limited to a single player's offerings. With multiple companies in the game, we can expect a variety of chips catering to different needs and budgets. This democratizes access to powerful AI hardware, allowing smaller players and startups to innovate without needing massive financial resources.

• Accelerated Innovation: Competition breeds creativity. As companies like Intel, Meta, Microsoft, and Google all vie for a slice of the AI chip pie, we can expect them to push the boundaries of performance and efficiency. This translates to faster training times, lower power consumption, and ultimately, more powerful AI models.

• Focus on Specific Needs: With several players in the market, there's room for specialization. We'll likely see chips designed specifically for tasks like LLMs, image recognition, or edge computing. This targeted approach allows for more optimized hardware, leading to better performance and efficiency in specific applications.

• Lower Costs: Competition often leads to price wars, which is fantastic news for developers and businesses. As companies fight for market share, we can expect the cost of AI chips to become more competitive. This opens the door for wider adoption of AI technology across various industries.

• A Booming Startup Ecosystem: With more affordable and accessible AI chips, startups will have the resources to experiment and develop groundbreaking applications. This fosters a vibrant ecosystem of innovation, leading to exciting new advancements in AI. The entry of Amazon and Microsoft into the fray adds another layer of disruption. They offer entire AI services built on their custom hardware. This could further democratize access to AI for businesses of all sizes, removing the need for extensive in-house infrastructure.

The competition in AI chips is not just about who becomes the top dog. It's about fostering an environment of innovation, affordability, and accessibility. This ultimately benefits the entire AI industry, accelerating progress and paving the way for a future powered by intelligent machines.

I feel the seeds of Q-commerce in India were sown during the COVID-19 lockdowns. Restricted movement and a surge in online shopping with all the free time created a gap – people needed immediate access to essentials, and often fast. This demand gap was filled by startups specializing in quick delivery of groceries, medicines, and daily necessities. Players like Blinkit, Zepto, Swiggy, Instamart, and Dunzo (and am sure I missed many) capitalized on this opportunity, offering lightning-fast delivery times, sometimes as quick as 10-20 minutes.

I met and spoke with a few friends in this industry who have been instrumental in shaping Q-commerce in India at few of the companies mentioned above. Our discussion primarily centered around what's driving the exponential growth of Q-commerce in India. Interestingly, I haven't observed a similar trend back home in the US. Here's a summary of our conversations:

• Consumer behavior is evolving in India, and evolving fast: Today's (not just affluent, but the HUUUGE middle class) consumers prioritize convenience and speed. Who wants to wait around all day for a delivery, that DNA is not there anymore. A significant percentage of Indian online shoppers are even willing to pay a premium for faster delivery. Q-commerce has found a good fit with their busy lifestyles.

• Urbanization density: India's urbanization is happening faster than ever before. Densely populated urban areas create a favorable environment for rapid delivery models. Over 35% of India's population is expected to reside in urban areas by 2030 (McKinsey Global Institute, 2021). I don’t think this is a good thing, with the infrastructure in some of these metros. But that aside, this concentration fuels the growth of Q-commerce – the closer the customers, the faster the deliveries! It also opens up avenues for optimizing delivery routes and utilizing technology like smart route planning algorithms. So much innovation headroom.

• Venture Capital fueling the Q-comm boom (obviously): VCs are injecting significant $$$ into the sector. Q-commerce startups are all burning money to grow, seems familiar, and to an extent deja vu.

• Beyond Essentials: Maintaining super-fast delivery times while ensuring profitability is a major challenge. While initially focused on essentials, platforms are now extending their offerings to include a wider range of products. They are exploring higher-margin product categories. Cosmetics, electronics, and other non-essential items are finding their way onto these platforms, catering to a wider range of consumer needs. It's a one-stop shop for everything from toothpaste to that new phone you've been eyeing.

• Local businesses are onboard: Partnerships with local stores is key for this to scale. India is still decentralized in terms of physical commerce. Small local nearby shops are the fabric of commerce. So this partnership is a win-win for everyone – consumers get their products quickly, and local stores gain a wider customer base.

• Also touted as a job creation engine: The growth is creating employment opportunities across various sectors. From logistics and delivery to tech, there's a growing need for skilled professionals to keep this speedy delivery machine running. There is good support from local residents and cities for this.

• and then the reach to the belly of the country - smaller cities: Q-comm isn't just for big cities anymore. Companies like Swiggy Instamart are already expanding their reach, aiming to tap into the demand in smaller towns and cities. This will make the convenience of Q-commerce accessible to an even wider population.

Very excited and curious on how this pans out in the next few years, as profitability and sustainability (environmental impact) pressures on top of evolving customer demands will continue to shape this industry.

The Power of Portfolio Thinking

Imagine a well-diversified investment portfolio. You wouldn't put all your money in a single stock, no matter how promising it seems. Similarly, a product roadmap shouldn't be a one-trick pony. Instead, it should be a collection of initiatives, each categorized by its potential impact and risk profile. Here's where the "buckets" come in:

• Innovate (20%): These are your moonshots, those ambitious ideas with the potential for high rewards but also high risk. This bucket houses disruptive features, entirely new product lines, or ventures into unexplored markets. Think of it as the "blue sky" section of your roadmap.

• Invest (60%): This is the bread and butter of your portfolio. These bets focus on core functionalities, user experience improvements, and features that directly address user needs. They aim for steady, reliable growth and optimization.

• Fix the Product Debt (20%): Every product accumulates technical debt over time. This bucket tackles performance bottlenecks, usability issues, and legacy code that hinders progress. Here, the focus is on improving efficiency and maintaining a healthy foundation.

These percentages are just a starting point, and the ideal allocation will vary depending on your company's stage, industry, and risk appetite. However, the framework of portfolio thinking can dramatically improve your decision-making process.

Making Smart Bets in Each Bucket

Innovate Bucket: Aim for the Stars

• Embrace experimentation: Encourage a culture of experimentation where small, fail-fast tests are encouraged. Consider hackathons, prototypes, or A/B tests to validate your moonshot ideas.

• Focus on user problems: Even disruptive ideas should solve real user needs. Conduct user research to identify unmet needs and pain points that your innovation can address.

• Be prepared to pivot: Innovation thrives on flexibility. Be prepared to adapt your initial concept based on user feedback and market trends.

Invest Bucket: Grow What Works

• Metrics Matter: Focus on metrics that measure user engagement, retention, and growth. Prioritize initiatives that demonstrably improve these metrics.

• Listen to your users: Gather user feedback through surveys, user interviews, and usability testing. This feedback will guide your investment decisions and ensure your efforts are user-centric.

• Data-Driven Decisions: Leverage data analytics to understand user behavior and identify potential areas for improvement. Invest in features that address these needs.

Fix the Product Debt Bucket: Build a Solid Foundation

• Prioritize technical health: Identify and prioritize critical fixes to performance issues, bugs, and security vulnerabilities.

• Future-proof your product: Regularly evaluate your technology stack and consider refactoring outdated code to ensure long-term maintainability.

• Transparency with stakeholders: Communicate the importance of product debt reduction efforts to key stakeholders. Explain how these investments contribute to overall product health and future growth.

Navigating the Portfolio: A Continuous Cycle

The beauty of this framework lies in its flexibility. Your portfolio isn't a static document; it's a living document that evolves alongside your product and market. Here's how to manage it dynamically:

• Regular Portfolio Review: Schedule regular portfolio reviews to assess the performance of your bets. Consider metrics, user feedback, and market trends to evaluate each initiative.

• Be Willing to Cut Losses: Don't be afraid to sunset failing innovations or investments. Shifting resources to more promising initiatives can be crucial for long-term success.

• Balance is Key: Maintain a healthy portfolio balance across the innovate, invest, and fix buckets. Strive for continuous growth while maintaining a stable platform.

The Portfolio Manager: More Than a Feature Factory

By adopting a portfolio mindset, you become more than just a feature factory. You become a strategic leader who allocates resources wisely, mitigates risk, and propels your product towards long-term success. This framework empowers you to champion user needs, drive innovation, and ensure a robust foundation for your product.

Remember: Your portfolio is a powerful tool. Use it strategically.

A Blueprint for Modern Leadership

Grove's foundational work emphasizes the critical importance of vigilance and adaptability amidst rapid business changes. He introduces the concept of "strategic inflection points," transformative moments requiring decisive responses to technological advancements, market shifts, or competitive pressures. Central to Grove's philosophy is the cultivation of a culture characterized by continuous learning, agility, and proactive innovation, urging leaders to remain vigilant against the pitfalls of complacency.

GenAI: A Modern Strategic Inflection Point

The advent of GenAI resonates deeply with Grove's principles, heralding transformative changes across all sectors, from travel, to retail, to healthcare, to finance, to entertainment. For instance, GenAI's revolutionary impact on healthcare is redefining diagnostic protocols and treatment paradigms, mirroring Grove's emphasis on adaptive strategies in the face of disruptive innovation. Similarly, GenAI-powered advancements in finance compel institutions to recalibrate investment strategies and risk assessment methodologies, aligning with Grove's advocacy for agility and foresight. The convergence of Grove's insights with GenAI's transformative potential underscores the imperative for leaders to embrace innovation, anticipate challenges, and harness the opportunities presented by disruptive technologies.

Navigating the GenAI Landscape

In the GenAI era, product managers and leaders must cultivate a proactive, adaptive mindset, drawing inspiration from Grove's enduring principles. To thrive amidst uncertainty, leaders MUST :

1. Challenge Assumptions: Foster a culture that encourages exploration of uncharted territories, enabling teams to envision groundbreaking innovations and capitalize on GenAI's disruptive potential.

2. Embrace Change: Remain attuned to emerging technologies and market trends, vigilantly monitoring competitors' innovations, and maintaining readiness to pivot strategies in response to evolving dynamics.

3. Cultivate a Culture of Vigilance: Lead by example, instilling a mindset of continuous questioning, experimentation, and learning, thereby fostering an environment conducive to innovation and growth.

By integrating these, you transform "paranoia" into a powerful catalyst for progress, ensuring organizational resilience, agility, and innovation in the face of GenAI-driven disruption.

Andrew Grove's "Only the Paranoid Survive" continues to offer invaluable guidance for leaders navigating the complexities of today's rapidly evolving landscape, particularly with the emergence of GenAI. By embracing Grove's philosophy and principles, leaders can champion a culture characterized by adaptability, foresight, and innovation, harnessing the transformative potential of GenAI to drive unprecedented growth, success, and competitive advantage.