Informed i’s Weekly Business Insights

In China, large reactors are coming together at a stunning pace. The country has nearly doubled its nuclear fleet since 2016, reaching nearly 60 gigawatts of total power capacity. The new facilities are nearly all gigawatt-scale pressurized-water reactors.

Meanwhile, the US has built just two reactors in that time—Unit 3 and Unit 4 at Plant Vogtle in Georgia. Smaller reactors are attracting a lot of excitement and investment, though. A microreactor developer just saw its reactor reach criticality in a new Department of Energy pilot program.

The world is racing to meet rising electricity demand, and many countries are interested in energy sources, like nuclear power, that don’t come with greenhouse-gas emissions. The key question: Which of these strategies will really pay off in terms of getting electrons on the grid quickly?

Today, the US and France are known as leaders in the nuclear industry. The US has the world’s largest fleet, with France coming in second. France is heavily dependent on nuclear for its grid—about two-thirds of the country’s power comes from nuclear reactors.

But they have hardly added any new reactors to their fleets in recent years. The US can point only to Vogtle, and France connected its latest reactor to the grid in December 2024—the first in over 20 years. 

It’s incredibly difficult to build the massive projects that dominate the nuclear industry today. Up-front investment can run well into the billions, so investors need to wait decades to break even. Designs are complex and can often change during the regulatory process, tacking on cost and time.  Many are hoping that the key to turning things around in these countries could be smaller reactors.

But look to the other side of the globe, and others are sticking with the established blueprint: China is absolutely churning out large nuclear reactors. Construction started on six new reactors there in 2025, and two more got underway in the first five months of 2026. The country is on course to overtake both the US and the European Union in installed nuclear capacity by 2030.

The speed here is staggering. As of 2024, the average time to build a new reactor in China came in at between five and seven years. The global average is about nine years, and the two most recent reactors in the US took about 15 years.

One key to this speed is standardization: China has set up a uniform project management system to design, license, and build new reactors. They’re built in batches of six or more to take advantage of economies of scale.

That’s not to say China is exclusively focused on big reactors: the country is also expected to see its first operational small modular reactor, the Linglong-1, start sending power to the grid this year.

But looking ahead, it’ll be interesting to see if smaller reactors can help the West keep building new nuclear power. At the moment, with China’s quick progress, it’s looking as if bigger might just be better.

Every year, companies invest thousands of hours in building their budgets, only to find within months that the plan no longer matches reality. Demand shifts, input costs change, competitors reposition, and new technologies reset productivity baselines. Yet the budget locks capital into allocations based largely on last year’s choices.

Summing up the problem, a former e-commerce CFO said, “If you stick to a rigid budget, you often find that by the time it’s approved, the world has already moved on, and you’re left trying to catch up.”

The uncomfortable truth is that the traditional annual budget process is not just inefficient; in today’s environment, it is strategically corrosive. In addition to being backward-looking, it reinforces incrementalism and can make dynamic reallocation politically and operationally difficult.

However, a handful of companies are outperforming their peers on growth, resilience, and total shareholder returns, and they’re contributing to their success not by budgeting better, but by budgeting differently. They treat the budget not just as a way to control spending, but as a road map for executing strategy. They have evolved the budgeting process in four ways: they base budgets on strategic choices and value-creation potential, they shift 10 to 20 percent of capital year over year toward higher-return opportunities, they replace single-scenario budgeting with scenario-based planning, and they use AI and machine learning to turn operational data into forward-looking insights.

Providing further evidence of how fundamentally companies are rethinking budgeting, a small number have gone even further by eliminating traditional budgets altogether in favor of approaches built around targets, real-time reporting, automated forecasting, and more dynamic performance management. While still the exception, these approaches underscore the extent to which the role of budgeting is being reconsidered.

Changing the budgeting process can be hard. Dynamic reallocation of capital may challenge entrenched interests, and gated funding forces executives to make difficult trade-offs. Scenario-based planning can expose strategic fragility and transparency into how KPIs impact the profit-and-loss statement may eliminate comfortable ambiguity. Effective performance management remains a critical enabler of both traditional and modern budgeting processes. It requires executives to cascade accountability throughout the organization and to establish business-review cadences that focus less on explaining performance gaps and more on what actions will be taken to address them.

But the cost of inaction is rising. The static annual budget can act as a brake on strategy, reinforce incrementalism, and lock capital into legacy priorities. These are big risks in a world where capital markets reward agility.

To set a new budgeting approach in motion, CFOs can start with: hard-wiring value creation into capital allocation; implementing a system of rolling scenario reviews and explicit contingency governance; and reallocating capital both year over year and in-year. Finally, CFOs can ensure that they and their teams have the AI and machine learning capabilities needed to industrialize driver-based planning. A final step, beyond the scope of this article but essential to consider, is redesigning incentives to reward adaptability, so that business units don’t complacently expect budget entitlements.

Asia’s boardrooms are full of AI ambition. Over the past three years, companies have launched pilots, tested AI assistants, and explored use cases across nearly every function. Business leaders are no longer asking whether AI works, but instead whether it’s materially changing the economics of their organizations.

The next phase of enterprise AI will expose an uncomfortable truth. Companies won’t struggle because they don’t have access to powerful models. Instead, they will struggle because they treat AI as a tool to bolt onto old ways of working. McKinsey’s latest global survey argues that the companies with the strongest bottom-line impact aren’t simply deploying more AI. They are redesigning workflows, governance, and decision-making around it.

In Asia, businesses are under pressure to do more while facing tighter margins and less tolerance for delay. AI may now be an operating necessity for many organizations—but necessity alone won’t lead to transformation.

The first wave of enterprise AI has largely focused on assistance. Today’s employees are surrounded by dashboards and beset by emails, yet struggle to get the right insight at the right moment. This is one area where AI has proved immediately useful. It surfaces relevant context and flags anomalies, helping employees act more quickly. When AI helps a finance team detect anomalies before they escalate or enables customer service teams to resolve issues faster, it shows that AI’s value isn’t just theoretical.

The second stage is automation, where AI begins to alter the economics of how work gets done. Traditional automation worked when tasks were repetitive and rules were clear. AI can now expand that range by handling variable and more unstructured tasks with far less manual intervention than before.

The real payoff will be the removal of friction. When approvals move faster, organizations become faster and more efficient. Over time, that can reshape how the business scales.

The third, and ultimately most strategic, benefit is augmentation, where AI begins to expand what the organization can realistically do. It allows companies to coordinate decisions at a scale that would have been difficult to manage manually. AI won’t just improving existing processes, but also make new operating models possible.

Singapore offers a useful glimpse of what that looks like in practice. SMRT, Singapore’s leading public transportation provider, and Oracle are piloting JARVIS, an AI-enabled platform designed to bring together maintenance and operations data, identify potential issues earlier, and help engineering teams intervene before disruptions occur. SMRT’s rail network supports more than two million passenger journeys a day, which makes the operational stakes obvious. This is a strong example of AI in action where value is created by solving real-world challenges; AI creates value when companies use it used to act before problems become obvious.

That is why the next chapter of AI will be written by companies integrating it into their processes, rather than treating it as a standalone tool.

The question isn’t whether AI belongs in the enterprise. It does. Instead, the question is whether the organization is prepared to redesign work so AI can deliver value.

Business bottlenecks are a good place to start. Leaders should ask where delays, errors, poor handoffs, duplicated work, or slow decisions are costing the organization money and trust—then they should ask how they need to change to allow AI to remove that friction.

Companies need to also trust AI to take the lead. If AI produces an insight but companies still need manual approval to act on it, value will leak away. It’s the workflow, not the model, that determines whether the transformation succeeds or fails.

Finally, companies need to treat governance as something that enables value. A company that can’t trust its AI won’t use it in consequential decisions.

This July will mark five years since Andy Jassy took over the chief executive officer role from Amazon’s founder. At the corporate offices in Seattle, the workforce has grown accustomed to his brand of rigorous oversight and ongoing exhortations to act as if they were at Jeff Bezos’ startup, not a $2.9 trillion behemoth. He recently placed a series of staggeringly expensive bets on artificial intelligence, audacious even by the standards of Silicon Valley’s ongoing trillion-dollar AI bacchanalia. In February he agreed to invest as much as $50 billion in OpenAI in a deal that commits the rising startup to relying in part on Amazon’s data centers and custom-designed microchips. Then in April he expanded a similar partnership with its archrival, Anthropic—a $13 billion investment, with an option for an additional $20 billion. To Jassy’s critics, that spending was the price of Amazon’s late jump into the current AI wave. He wasn’t bluffing, though: Jassy spooked investors by vowing to spend $200 billion this year on big-ticket items including warehouse robots, a far-out effort to launch satellites into space, and in particular more AI data centers, AI chips and networking equipment. “I don’t think the world has ever seen a technology get this much adoption and grow this quickly, at least in my lifetime,” Jassy tells Bloomberg Businessweek.

Jassy runs the fifth-largest company in the world by market value. He takes pains, earnestly and often awkwardly, to project an everyman image, in part by constantly advertising his lifelong passion for music and sports.  He also studiously strives to embody one of Amazon’s 16 leadership principles, frugality, as if to compensate for Bezos’ public evolution into an icon of overindulgence. “He’s customer obsessed, product obsessed and, frankly, Amazon obsessed,” says his Harvard classmate Gina Raimondo, the former US secretary of Commerce. “It’s really the only place he’s ever worked.”

The company is much different from the online bookseller Jassy joined in 1997. Amazon today is a corporate turducken: an advertising business and logistics company stuffed inside an e-commerce marketplace, trussed to a cloud computing powerhouse, and garnished with Alexa, Whole Foods Market and Prime Video. The behemoth infuriates critics, who lambaste its rough treatment of drivers and warehouse workers and its willingness to bully rivals. On the other hand, ordering from Amazon and seeing a cardboard box sitting on your doorstep the next day is one of the few reliable luxuries of modern life, made possible only because of the company’s ruthless ability to root out inefficiencies, adopt cutting-edge robotics and hide all the messiness from our delicate eyes.

Jassy took over in mid-2021 and had to correct course from some of the excesses of the late-Bezos era, including a spurt of overhiring to meet pandemic demand. He laid off roughly 60,000 corporate personnel, forced employees back to the office and shuttered dozens of projects, such as the cashierless Go stores, Amazon Fresh supermarkets and a telehealth service. Employees grumbled, but his personal style contrasted favorably with his former boss’s. He was ever-present, probing but never argumentative. (Jassy for years has urged colleagues to not be afraid to ask dumb questions.) He also has the agreeable habit of thanking everyone at the beginning and end of meetings and sending holiday cards to his senior execs. Friends vow that it’s all authentic and not at all humility theater for a leery public that’s become profoundly skeptical of technology overlords.

Then came the age of generative AI, inaugurated by OpenAI’s ChatGPT in late 2022, which posed perhaps the first existential challenge to Jassy’s own baby and Amazon’s profit engine, Amazon Web Services. With AWS, Amazon invented cloud computing and went on to build a client list that included the biggest names in corporate America. It remains the market leader, though Microsoft Corp. and Alphabet Inc. have collectively booked $600 billion more in future business than Amazon since ChatGPT came on the scene. Would companies and governments move their data off Amazon’s servers to access the newest large language models? Would shoppers, for that matter, start browsing and buying with knowledgeable chatbots such as ChatGPT and Google’s Gemini, instead of enduring the ad-riddled search results on Amazon.com?

The answers to those questions, and how Jassy navigates the AI era, will matter more than anything else in determining whether Amazon continues to thrive in its fourth decade and how posterity comes to view Bezos’ successor.

When a rule gets broken at work, a leader’s first instinct is to act fast. The business response feels obvious: punish the offender and move on. But it’s rarely that simple.

In a new paper that reviews and synthesizes more than 250 studies across four decades, Michael J. Gill, an associate professor of organization studies at the University of Oxford’s Saïd Business School, shows that people break rules for very different reasons. Some do it for personal gain; others to help customers or colleagues. And sometimes the environment pushes people over the line.

The Ethics & Compliance Initiative’s “2023 Global Business Ethics Survey” reported that 65% of employees around the world said they had observed misconduct at work, up from 60% in 2020.

Gill started studying the topic after hearing the same question from companies: Why does this keep happening? His work divides misconduct into four types: self-interested, or meant to help oneself; prosocial, or meant to help others; corrupted, or driven by pressure; and edified, or encouraged in the name of doing the right thing. Most organizations treat all of them the same way, which is a problem because breaking rules can be beneficial.

Before punishing rule breakers, leaders should ask two critical questions: Was the behavior constructive or destructive? Was it driven by individual choice or the situation? “If your first move is to punish someone, you may be solving the immediate issue but missing the real cause. And that can hurt the business in the long term,” Gill says. “A better approach is to be less reactive and more curious.” He recommends several best practices for resolving rule breaking at work: 

Understand what happened.  Before jumping to conclusions, talk to the people involved. Ask why the policy was violated—and do so in a way that isn’t confrontational and that gets to the reality of the situation.  That’s why tone matters.

Separate intent from impact.  Not all misconduct is created equal. Yet when people believe their intent doesn’t matter, they either stop using judgment or start hiding what they’re doing. And when every misstep is treated as self-interest or corruption, they learn to play it safe or stay quiet. The result is less initiative, less transparency, and fewer opportunities to fix problems early.  But if employees see that good intentions are recognized—even when outcomes fall short—they are more likely to take smart risks and speak up when something feels off.

Pay attention to patterns.  One-off incidents can be misleading; patterns are where the insights are. If a rule keeps getting broken, that usually points to something bigger, such as a policy that is unrealistic, unclear, or in conflict with what employees are rewarded for doing. Repeated violations can be a form of feedback about where the system doesn’t line up with the work.

Uncover what you’re not being told.  Many instances of observed misconduct never make it up the chain. People may not report it, and managers may not escalate it. That’s why leaders need other ways to understand what’s happening, such as establishing anonymous reporting channels, spending more time seeing how work gets done, or simply asking broader questions about the company’s policies and how restrictive they are.

Fix the causes of rule breaking.  If you execute the earlier steps well, you’ll identify root causes that may be driving misconduct, including bad incentives, unclear rules, or pressure that’s pushing people in the wrong direction. Now it’s time to find a systemic fix for the problem. Focusing only on the individual might fix the behavior in the short term but won’t always solve what’s driving it.

Most professionals think of LinkedIn as an external platform. But when used strategically, it can help you build influence and demonstrate leadership inside your organization as well. It’s a personal branding tool that can signal to your boss and other senior leaders in your company that you are a leader. Leadership isn’t a title, it’s a reputation. LinkedIn gives individual contributors an unprecedented opportunity to build a leadership reputation long before their title catches up. Here’s how to use your LinkedIn profile and activity to make your leadership potential visible and memorable.

Make Your LinkedIn Profile Show You Are A Leader.  Most professionals think of their profile as an e-version of their resume or CV. Leaders think of it as a way to showcase impact, demonstrate leadership behaviors, and build trust.

Leadership Leaves Digital Footprints.  People often form opinions about your leadership potential before they ever work with you. Your profile, comments, posts, and interactions create digital footprints that reveal how you think, what you value, and how you show up. When people review your activity, they’re not just evaluating your expertise, they’re evaluating your leadership.

Use Your LinkedIn Activity To Show That You Are A Leader.  Your LinkedIn activity reveals how you think, what you value, and how you show up. When someone looks through your posts and comments, they’ll get a feel for how you engage on the platform. Make it clear that you are a leader by highlighting key skills such as acknowledging others, sharing thought leadership, expressing a point of view, and being generous.  Here’s how to demonstrate you are a leader through your LinkedIn activity:  Be Generous With Recognition; Share Ideas and Insights, Not Just Accomplishments; Curate Industry Trends; Demonstrate Curiosity; Build a Reputation Around a Topic; Write About Lessons, Not Successes; Show Cross-Functional Thinking; Engage Thoughtfully With Others; Publish Original Frameworks; Show that You Are a Change Agent; And Build Relationships with Leaders.

Founders who survive bad years don’t outspend their problems. They outthink them.  According to the author, if he ha to start over today with nothing in the bank, here are the seven plays he would run in this order. 

Write a seven-sentence plan.  Most founders he meets have a marketing strategy that’s too long to remember. According to the author his fits on one index card. He answered seven questions in order:  What is the goal of my marketing?  What’s the number-one problem my business solves?   What benefits do I provide?  Who am I marketing to, specifically?  Which guerrilla marketing tactics will I use?  What percentage of projected revenue will I commit in year one?  How will I measure success?    If you can’t say it in seven sentences, you don’t have a plan yet.

Build a 90-day calendar.  According to the author the plan gave him direction. The calendar created motion. One row per tactic, with seven columns: date, tactic, message, budget, tracking, notes, and grade. A campaign earned an A if it made money, and an F if it didn’t. Marketing is a slow burn, so he revisited grades 60 to 90 days later, once the leads actually closed.

Build your Dream 100.  Other businesses already had his ideal customers in their database. His job was to build the relationships. He picked 100 companies that served the same buyer he did but didn’t compete with him, and he committed to spending six months getting to know them: two coffees, one LinkedIn comment, one introduction at a time.  The mistake most founders make is asking for referrals before they have earned the relationship. The Dream 100 works because it turns cold-calling into warm, authority-building outreach.

Make them the star of your podcast or webinar.  He invited his Dream 100 onto a webinar series he hosted, one by one. Each session was about them; he made them the star. After every recording, he sent a thank-you gift with a handwritten card.  In 2008, that single play produced nearly $2 million in referred business from nine relationships. The cost was a USB microphone and a stack of stationery.

Turn one episode into a year of content.  Each session became his content engine. He sliced every recording into clips, quote graphics, and audiograms for LinkedIn, Facebook, and YouTube. Today, the same work takes minutes with CapCut, Descript, and Opus Clip.  His stealth move: For a full year after each session aired, he posted a fresh clip every other week and tagged the guest. That kept him in front of their network for free, and gave them content their audience expected them to share. By month 12, social was driving steady inbound, and the only cost was editing time.

Send lumpy mail.  Lumpy mail is an odd-shaped package or envelope that stands out from the rest. It gets opened.  In 2008, he targeted credit unions. He sent 200 envelopes filled with shredded money and a note: “You’re shredding money working with the wrong agency. Call us instead.” The campaign drove 19 phone calls and closed six deals worth over $25,000 in business — total cost: under $1,000.

Become a walking billboard.  He started thinking about every place he went each day — coffee shops, the gym, my kid’s soccer games — and turned himself into a free attention magnet. He ordered shirts with one line: “Do you need marketing? Scan me.” The QR code sent people to his booking page. In the first quarter, it produced dozens of conversations a month and three new clients.

According to the author for a long time, he believed that if we built a great product, everything else would follow. A better product. Better taste. Better customer experience. Better pricing. Better packaging. The assumption was simple: if we created something genuinely valuable, the market would eventually reward it.  And to be clear, product quality matters. It matters a lot. But what I didn’t understand early on is that building a great product and building a successful business are not the same thing. A great product that nobody knows about is still invisible.

One of the biggest mistakes founders make is assuming the best products naturally get discovered. In reality, great businesses fail every day because they spend all of their energy creating value and very little making that value visible. Customers can’t buy what they don’t know exists, and opportunities rarely appear simply because a product deserves them.

But over time, something started to compound. People began following the journey. They became familiar with our story and understood what we were building long before they encountered our product. They knew the mission, the challenges and the progress. That familiarity created a level of trust and recognition that traditional marketing often struggles to achieve.

The internet is a 24/7 trade show.  For decades, business development happened primarily in physical spaces. Those channels still matter, but today they are only part of the equation.  Layered on top of the physical world is a digital environment where customers, investors, partners and future employees are constantly discovering businesses, evaluating founders and forming opinions. Whether you’re actively participating or not, people are researching, comparing and deciding who deserves their attention.  Your content is your booth. Your story is your pitch. Your consistency is your presence. Unlike a traditional trade show, this one never closes. It operates every hour of every day, giving people countless opportunities to discover your business before you ever meet them.

What happened when I started showing up.  As the author continued sharing consistently, the results became impossible to ignore. The content eventually reached millions of people organically, but the views themselves weren’t the most important outcome. The real value was the opportunities that followed.  None of those opportunities came from a single viral moment. They came from repeatedly showing up over time. Every post became another opportunity for someone to discover the business, understand the mission and remember the brand.

At its core, attention creates familiarity, and familiarity influences decisions. People are more likely to engage with what they recognize, trust what feels human and remember stories more than logos or taglines.  When founders consistently share their experiences, lessons and perspectives, they reduce the distance between themselves and the audience they’re trying to reach. The company becomes more than a product or service. It becomes a story people can follow and a mission they can understand.  In crowded markets, that distinction matters. People don’t just buy products. They buy from companies they trust, and trust often begins long before a purchase decision is made.