Informed i’s Weekly Business Insights

Over the past six months crypto has taken on a new role at the centre of American public life. Several cabinet officials have large investments in digital assets. Crypto enthusiasts help run regulatory agencies. The industry’s largest businesses are among the biggest donors to election campaigns, with exchanges and issuers deploying hundreds of millions to defend friendly legislators and to crush their opponents. The president’s sons tout their crypto ventures around the world. The biggest investors in Mr Trump’s meme coin get to have dinner with the president. The holdings of the first family are now worth billions, making crypto possibly the largest single source of its wealth.

This is ironic, given crypto’s origins. When bitcoin was started in 2009, a utopian, anti-authoritarian movement welcomed it. Crypto’s earliest adopters had lofty goals about revolutionising finance and defending individuals against expropriation and inflation. They wanted to hand power to small investors, who would otherwise be at the mercy of giant financial institutions. This was more than an asset: it was technology as liberation.

That is all forgotten now. Crypto has not just facilitated fraud, money-laundering and other flavours of financial crime on a gargantuan scale. The industry has also developed a grubby relationship with the executive branch of America’s government that outstrips that of Wall Street or any other industry. Crypto has become the ultimate swamp asset.

The contrast with what is happening outside America is striking. Jurisdictions as varied as the European Union, Japan, Singapore, Switzerland and the United Arab Emirates have managed to give digital assets new regulatory clarity in recent years. They have done so without the same rampant conflicts of interest. In parts of the developing world, where expropriation by governments is rife, inflation is highest and the debasement of currencies is a real risk, crypto still fulfils something like the role that the early idealists once hoped it would.

All this is happening as the underlying technology of digital assets is coming into its own. There is still plenty of speculation. But crypto is slowly being taken more seriously by mainstream financial firms and tech companies. The amount of real-world assets, including private credit, US Treasury bonds and commodities, which have been “tokenised” to be traded on a blockchain has almost tripled over the past 18 months.

This is an opportunity that crypto firms risk blowing. Boosters argue that they had no alternative but to fight dirty in America when Joe Biden was in the White House.  The regulatory pendulum has now swung hard in the opposite direction.  

The result is that crypto needs saving from itself in America. New rules are still needed to ensure that risks are not injected into the financial system. If politicians, scared of the industry’s electoral power, fail to regulate crypto properly, the long-term consequences will be harmful. The danger of putting too few guardrails in place is not just theoretical.

The industry is largely silent about the florid conflicts of interest generated by the Trump family’s crypto investments. Legislation is needed to clarify the status of the industry and the assets, to give the regulatory security the more sensible crypto firms have long hoped for. The blending of the president’s commercial interests and the business of government is already making that harder. A crypto bill in the Senate failed to advance on a procedural vote on May 8th after many Democratic senators withdrew their support, along with three Republicans.

Satya Nadella arrived at the World Economic Forum in January ready to talk up his triumphs in artificial intelligence, when a dangerous threat emerged. A little-known Chinese startup named DeepSeek had just released an AI model that quickly became the talk of Davos, Switzerland. Nadella, the chief executive officer of Microsoft Corp., gathered his lieutenants to assess the out-of-nowhere competition. They set up a virtual war room on—where else?—Microsoft Teams to coordinate a response.

The new model, DeepSeek-R1, could deliver results roughly on par with those of OpenAI at a fraction of the price. Computer processing that would cost $1,000 through OpenAI ran for just $36 through R1. Even crazier, DeepSeek made R1 open-source, meaning anyone could install versions of it for free if they had a powerful enough computer. “OpenAI has been so far ahead that no one’s really come close,” Nadella tells Bloomberg Businessweek. “DeepSeek, and R1 in particular, was the first model I’ve seen post some points.”

To the schmoozers in Davos, this seemed like a huge problem for Microsoft. The company had invested $13.75 billion in OpenAI by that point and had already committed to spend $80 billion on AI data centers in this fiscal year alone, all under an assumption that better AI required more computing resources. Nadella immediately ordered his team to conduct a security review of DeepSeek-R1. They scrutinized a research paper DeepSeek published detailing its work and contacted the startup’s engineers, peppering them with questions about the model.

Soon roughly 100 Microsoft employees were coming in and out of the Teams videoconference rooms testing the security of DeepSeek’s codebase and exchanging notes. “People didn’t sleep,” says Asha Sharma, the company’s AI platform head, who spearheaded the effort. “It was 48 hours of going through every single thing.” R1 appeared to be legit. But instead of trying to stomp out this new rival, Nadella chose to embrace it. He instructed his team to install R1 on Microsoft’s cloud and sell access to it to customers alongside products from OpenAI and Microsoft itself.

Nadella’s primary allegiance now isn’t to OpenAI’s very expensive skunkworks. His ultimate objective is to sell whatever AI his customers might want through Microsoft’s platforms. Nadella had spent three years running various parts of the company’s cloud business, called Azure, before he became CEO in 2014, and that’s now central to his AI strategy. Customers can choose from over 1,900 different models on Azure, including ones made by Meta and OpenAI, and upstarts such as Cohere, Mistral, Stability AI and now DeepSeek. (Some, though, such as Google’s Gemini, aren’t available to Microsoft for competitive reasons.) Whether a model’s usage costs a customer $10 through OpenAI or 90¢ via DeepSeek, Microsoft gets paid for the cloud computing, cybersecurity protections, data storage and other upsold services.

The DeepSeek episode highlights another, arguably more revealing part of Nadella’s thinking: AI is rapidly commoditizing, and this is a good thing for Microsoft. While everyone in Davos was focused on AI consumption, Nadella was contemplating the history of coal production. One of his favorite economic theories is the Jevons paradox, which posits that as a resource becomes more accessible and its usage more efficient, consumption increases.  

This econ mindset has been driving the company toward creating its own AI architectures, including some tiny models with capabilities similar to those of DeepSeek-R1. Over the past year, it’s also been training a series of large language models called MAI-2, the latest iteration of Microsoft’s in-house alternatives to OpenAI’s models, which it had been developing in secret. The goal is to build AI that requires less computing power than ChatGPT and bring down the cost to operate Microsoft’s equivalent service, Copilot. The company will still regularly tap OpenAI’s bleeding-edge technology, but Nadella is convinced Microsoft can deliver near-ChatGPT quality for a lot less.  What this all means for Microsoft’s arrangement with OpenAI is complicated. Six years on, what began as a nurturing kinship has turned into an intense sibling rivalry.

Nadella is facing a concentrated version of the trial practically every CEO is undergoing now. Even with Microsoft’s considerable resources, Nadella must decide how AI will reshape his business by making a set of extremely difficult trade-offs—between embracing new-fangled technology and shielding his employees and business partners from disruptive systems, and between marching in line with what’s worked in the past and blindly leaping into the future. Up to this point, shareholders have lauded Nadella’s performance, making Microsoft the most valuable company on Earth. If he falls flat, though, his may be one of the first jobs threatened by AI.

With AI promising (or threatening) such existential change, there remain weighty questions about who will win and who will lose from this next technological transformation. In public, Nadella often sounds like a macroeconomist. He draws parallels between this moment in AI and the industrial revolution, and the convergence of the Global North and South, and cites economic and labor theorists from David Autor to Friedrich Hayek to Herbert Simon.

In particular, the Jevons paradox—first described in an 1865 book on British coal production by William Stanley Jevons—has greatly informed Nadella’s understanding of Microsoft’s place in the AI revolution. Jevons found that the invention of a more fuel-efficient steam engine made coal a more desirable fuel source, igniting a surge in coal consumption. That’s why Nadella appears unfazed by the perception of DeepSeek undermining the value of his investment in OpenAI or the buildout of Microsoft data centers. More efficient AI will eventually mean a greater need for AI services and infrastructure to power them, in his view.

Meanwhile, Microsoft said on May 13 that it’s cutting 6,000 employees, about 3% of its workforce.) But Nadella contends that AI could end up delivering more societal benefits than the industrial revolution did. “When you create abundance,” he says, “then the question is what one does with that abundance to create more surplus.”

He argues, again, that it comes down to economics. If Microsoft spends hundreds of billions of dollars on AI projects, he estimates that will likely generate trillions of dollars in economic activity—more data centers mean more construction, energy use, manufacturing equipment and so forth. More AI use means more opportunities to create new kinds of businesses, especially as AI bridges the skills gap between an entry-level employee, expert engineer and executive. “There may be a new startup with a hundred founders,” Nadella says. Or, if the AI ever gets good enough, perhaps 100 startups with just one founder.

It will take time for the jobs of the AI era to arrive, he says. And then the huge economic and productivity boosts will follow. Just as they did after the advent of the steam engine, electricity, the computer and the internet. “When that happens, then everything computes at a societal level,” Nadella says. “Otherwise, all you have is a bunch of GPUs.”

The day after his inauguration in January, President Donald Trump announced Stargate, a $500 billion initiative to build out AI infrastructure, backed by some of the biggest companies in tech. Stargate aims to accelerate the construction of massive data centers and electricity networks across the US to ensure it keeps its edge over China.

The whatever-it-takes approach to the race for worldwide AI dominance was the talk of Davos, says Raquel Urtasun, founder and CEO of the Canadian robotruck startup Waabi, referring to the World Economic Forum’s annual January meeting in Switzerland, which was held the same week as Trump’s announcement. “I’m pretty worried about where the industry is going,” Urtasun says. 

She’s not alone. “Dollars are being invested, GPUs are being burned, water is being evaporated—it’s just absolutely the wrong direction,” says Ali Farhadi, CEO of the Seattle-based nonprofit Allen Institute for AI.

But sift through the talk of rocketing costs—and climate impact—and you’ll find reasons to be hopeful. There are innovations underway that could improve the efficiency of the software behind AI models, the computer chips those models run on, and the data centers where those chips hum around the clock.

Here’s what you need to know about how energy use, and therefore carbon emissions, could be cut across all three of those domains, plus an added argument for cautious optimism: There are reasons to believe that the underlying business realities will ultimately bend toward more energy-efficient AI.

1. More efficient models.  The practice has been to grab everything that’s not nailed down, throw it into the mix, and see what comes out. This approach has certainly worked, but training a model on a massive data set over and over so it can extract relevant patterns by itself is a waste of time and energy.  There might be a more efficient way. Children aren’t expected to learn just by reading everything that’s ever been written; they are given a focused curriculum. Urtasun thinks we should do something similar with AI, training models with more curated data tailored to specific tasks.  Using synthetic data is another way.  And so is the parallel computing.  There is a lot of talk about small models, versions of large language models that have been distilled into pocket-size packages.
2. More efficient computer chips.  As the software becomes more streamlined, the hardware it runs on will become more efficient too. There’s a tension at play here: In the short term, chipmakers like Nvidia are racing to develop increasingly powerful chips to meet demand from companies wanting to run increasingly powerful models. But in the long term, this race isn’t sustainable.  Companies like Microsoft and OpenAI are losing money running their models inside data centers to meet the demand from millions of people. Smaller models will help. Another option is to move the computing out of the data centers and into people’s own machines.  But getting AI models (even small ones) to run reliably on people’s personal devices will require a step change in the chips that typically power those devices. These chips need to be made even more energy efficient because they need to be able to work with just a battery.
3. More efficient cooling in data centers.  Another huge source of energy demand is the need to manage the waste heat produced by the high-end hardware on which AI models run.  It is hard to predict what kind of hardware a new data center will need to house—and thus what energy demands it will have to support—in a few years’ time. But in the short term the safe bet is that chips will continue getting faster and hotter.  With so many high-powered chips squashed together, air cooling (big fans, in other words) is no longer sufficient. Water has become the go-to coolant because it is better than air at whisking heat away. That’s not great news for local water sources around data centers. But there are ways to make water cooling more efficient.  One option is to use water to send the waste heat from a data center to places where it can be used.  Water can also serve as a type of battery.  But as data centers get hotter, water cooling alone doesn’t cut it.
4. Cutting costs goes hand in hand with cutting energy use.  Despite the explosion in AI’s energy use, there’s reason to be optimistic. Sustainability is often an afterthought or a nice-to-have. But with AI, the best way to reduce overall costs is to cut your energy bill. That’s good news, as it should incentivize companies to increase efficiency.

AI is fast becoming a commodity, which means that market competition will drive prices down. To stay in the game, companies will be looking to cut energy use for the sake of their bottom line if nothing else.   And in the end, capitalism may save us after all.

Excellent governance, risk, and compliance (GRC) is a common aspiration, but how often is it a reality? For most companies, GRC is a work in progress, according to McKinsey’s 2025 Global GRC Benchmarking Survey. Despite efforts to broaden expertise at senior levels, corporate leaders see a “need for improvement” across numerous aspects of all three GRC pillars.

There are many reasons for GRC shortfalls, some of which can be traced back to idiosyncratic factors in how businesses are run. Yet across industries, there are also some common pain points, including limited tech enablement, insufficient resourcing of oversight capabilities, and the challenges of a shifting regulatory.

To understand the dynamics that shape GRC capabilities, the authors asked 193 corporate leaders to tell them how they structure their governance frameworks, manage risk, and comply with local and regional regulations. The survey responses offer compelling insights into levels of GRC maturity globally and highlight the strategies that some companies are using to build smarter, more effective capabilities.

Governance approaches vary widely.  Most companies in our survey understand that dedicated governance frameworks are integral to efficient and effective operations. Fifty percent of respondents have chosen a strategic board archetype, with 72 percent adding between two and five subcommittees. This approach means the board can both take a hands-on approach to governance and draw on a wide range of expertise to manage critical aspects of operations. Indeed, 55 percent of respondents opt for a board with diverse expertise across industries and functions.

Risk management: Some industries are ahead of others.  Across industries, the responses reveal that decision-makers see room for improvement, as evidenced by an average score of 2.6 out of 4.0. The only industry to rate itself as “good” (with a score of 3.2) is insurance, suggesting that financial services may be ahead of other industries following past crises (for example, the 2007–08 financial crisis) and subsequent regulatory actions. Most industries tell us that they need to up their game in strategic risk management, encompassing areas such as risk appetite, stress testing, and board oversight. Sixty-seven percent of companies in life sciences, for example, say that a well-defined risk appetite is either absent, lagging, or in need of improvement, while 54 percent of companies in the travel, logistics, and infrastructure (TLI) sector apply the same three descriptors to their use of stress scenarios.

A common pain point highlighted by the survey is that companies are generally failing to use basic GRC tools and systems as effectively as they would like to.

Leading GRC companies rarely achieve rock-steady capabilities through piecemeal or periodic initiatives. Instead, they rigorously seek out approaches to support excellent decision-making, unlock value creation opportunities, and comply with relevant regulations in their spheres of operations. Here we set out five features that can be a driver of GRC excellence.  Focus on tone from the top and revisit your GRC mandate.  Adopt a strategic lens, particularly in risk management.  Fix the fundamentals first.  Embrace technology to complement human expertise at scale.  And review incentives and bonus structures to reflect risk and compliance priorities.

Geopolitical tensions between the West and China are deepening. As a result, Western governments, especially in the U.S. and Europe, are hardening their rules toward Chinese companies.

Western governments have placed Chinese businesses in their crosshairs for three reasons. The first is that Chinese companies are becoming increasingly competitive globally.  Second, many of these innovative Chinese companies are perceived as having links to China’s Communist Party and serving as extensions of the Chinese state by supplying it with dual-use technologies (having both civil and military applications) and/or complying with laws requiring cooperation with state intelligence efforts.  Third, there is the growing realization of the West’s own declining competitiveness.

While Western policies may seem to be headed toward decoupling, identifying policies as belonging to one of the three following categories offers business leaders a more strategic perspective on how to respond to them.

Techno-Nationalism: Chinese Not Welcome.  Techno-nationalistic policies aim to build domestic technological capacity by removing Chinese companies from supply chains entirely.  Western companies facing techno-nationalistic policies might need to phase out their relationships with Chinese businesses in critical sectors sooner rather than later.  Companies in critical sectors should more proactively identify non-Chinese buyers and suppliers and reconfigure their alliances to mitigate such risks. Even open-source technologies overseen by Chinese companies need to be cautiously adopted.  In terms of nonmarket strategies, organizations should proactively lobby for greater transatlantic harmonization on policy toward Chinese companies. After all, Western companies on either side of the Atlantic may lose out if they are the only ones reshuffling their relationships in sensitive industries’ supply chains.

Techno-Localism: Playing Hardball on Market Access.  Unlike techno-nationalistic policies, which aim for the complete exclusion of Chinese companies, techno-localistic policies seek to keep critical technologies — whether foreign or domestic — within one’s domestic borders. These policies often involve a quid pro quo with foreign businesses: Access to Western markets or even government support is allowed in exchange for technology localization.  On the one hand, Western companies may be able to use techno-localistic policies to their advantage. Such rules may indeed facilitate access to new technologies and/or otherwise enable businesses to negotiate favorable terms for technology transfer.  On the other hand, caution is warranted.  As the transfer could be in non-critical areas.  So Western businesses should be wise enough not rely on them as a long-term solution to more deep-rooted competitive disadvantages.

Protectionism: Imports (but Not Foreigners), Go Home.  Protectionist policies do not explicitly target technology transfers or the removal of Chinese companies; rather, they focus on limiting imports to protect domestic industries. These policies apply to imports from foreign and domestic companies’ operations abroad.  Western businesses cannot bank their success on protectionist policies; rather, they must ramp up investments locally and carefully consider partnerships with Chinese companies. While protectionist measures can offer short-term advantages to Western businesses by shielding them from competition, complacency is risky. Some Chinese companies have shown resilience to protectionist policies by significantly increasing their investments abroad and adapting through local partnerships, impression management, and innovation. Western businesses should respond by increasing their own investments in R&D, production, and marketing. It may also behoove them to consider strategic alliances on their home turf with politically savvy Chinese companies that have the right technology, know-how, or production capabilities.

In the span of one day and 750 miles, two of tech’s biggest companies put on conferences for their armies of developers this week. And while both Microsoft’s Build and Google’s I/O conferences were all about AI, the dueling convocations highlighted how the two industry behemoths are each seeking to conquer the market through radically different strategies.

Both companies made a big push into AI coding assistants that can autonomously build and test software—with Microsoft announcing a new autonomous coding feature for GitHub Copilot and Googled debuting its Jules coding agent. But beyond coding agents, some key differences in emphasis pointed at divergent strategies.

Microsoft is battling to convince enterprises to build AI agents.  At Build, Microsoft placed a far greater emphasis in its announcements on tools that are designed to help enterprise customers create AI agents and get them to successfully automate workflows. Microsoft’s announcements were about how to allow agents to use tools, get agents to work with other agents, and, critically, to control what data AI agents access. These things matter to big companies and governments.

Google is battling for consumers and individual creators.  Contrast that to what Google announced at I/O. Here the emphasis was almost entirely on consumers, not large organizations. It was about individual web users and individual content creators. The biggest news was the revamping of Google’s core Search product, with more AI Overviews, which provide capsule answers to queries, and also a new “AI Mode” that provides a more native AI experience, similar to what users get with OpenAI’s ChatGPT, using Google’s most capable AI models. It will also have new features that allow shoppers to virtually try on outfits as they shop.

What was also striking between Build and I/O is how comfortably the innovations Microsoft is announcing sit within the software giant’s existing business model, and how awkwardly much of what Google announced sits within its own.

Sure, Microsoft is taking a risk that its customers won’t find enough value in all the agentic AI products and features it is rolling out to pay the increased license fee that Microsoft wants to charge for it. But, if the AI agents gain traction, they only reinforce its existing cloud business and subscription-based business model.

Google, on the other hand, is taking a big gamble with its rollout of AI features that could directly cannibalize the advertising-based business model on which it has depended for a quarter century. Search represents 56% of Google’s revenues and most of its profits. If people click on fewer links with AI Overviews, as independent studies suggest, or if AI Mode offers far fewer opportunities for paid links, as also seems to be the case, it isn’t clear how Google will maintain its revenues.

There are plenty of ways to imagine new business models for chatbot-like interface and a universal personal AI assistant. But Google has not said yet what it thinks those business models should be—and listening to Google executives speak at I/O one got the sense the company hasn’t really figured it out yet.

At least the image, video, and audio generation products it announced help feed YouTube, which still has a healthy ad-driven business. But for many of its AI features, Google is trying for now to pivot to selling pricey subscriptions. It has renamed its $19.99 per month AI Premium subscription Google AI Pro, and made some of its new features available to those subscribers. And then it has announced a very expensive $250 per month Google AI Ultra subscription for power users who will get access to Google’s most advanced AI capabilities, with few caps on usage.

It’s hard to imagine that Google will be able to sell enough of these subscriptions to replace the ad dollars they are potentially going to lose by rolling out the AI Search features. In fact, the Ultra tier is so expensive it isn’t really a business at all. It might just about cover the costs of those few power users who sign up for it. But it doesn’t seem like a serious business for a company of Google’s scale. It is at best a stop gap measure—a halfway house between the Google of the past, and a future Google whose shape has not yet come into focus.

As its I/O conference made clear, Google is essentially a consumer AI company. And while a subscription model can work for consumer brands—just ask Netflix or Spotify—it can’t work for consumers at $250 per month. In fact, even those streaming companies have found that to keep producing the growth Wall Street demands, they’ve had to incorporate advertising into their offerings. Ultimately, Google is going to have to figure out a way to make advertising still work in a new world of chatbots and AI personal assistants it is rapidly ushering into existence. Microsoft’s challenge is daunting but easier: it just has to figure out how to make the tech work well enough to justify its cost to serve.  In other words, Google needs to not only invent the tech, it needs to reinvent itself.

Western managers who are charged with leading global teams face a trap. Their expertise and training usually have their roots in Western, individualistic contexts, steeping them in ideals such as autonomy, empowerment, egalitarianism, and authenticity. Yet according to the GLOBE Leadership Studies, 70% of the world’s workforce is collectivist and hierarchical. These values are characteristic not only of employees in Shanghai and Dubai but also of immigrant talent in Copenhagen and Omaha.

A different approach is needed for leading global teams. It’s not that Western leadership advice is entirely wrong; rather, a global leader needs a larger toolbox and a more refined understanding of when and how to use the tools inside. Cultural sensitivity training and even culture-specific preparation often fall short because they’re too targeted and episodic. It’s like trying to teach a robot how to read and respond to body language with limited training data.

Instead, leaders need to develop “cultural intelligence”—a flexible intercultural fluency for adapting to culturally complex situations.  A good starting point is to understand the most common mistakes that derail Western leaders and learn how to use cultural intelligence to avoid them.

Too Much Autonomy.  Many leaders who come from individualist cultures fall into the trap of assuming that what motivates them will motivate their team. This often means they don’t see the best ways to motivate people from collectivist cultures.  But autonomy is not equally motivating for everyone. Some individuals thrive when their leader outlines clear processes and deadlines, and they struggle to be productive in environments that lack directive leadership. The degree to which individuals want to make decisions on their own, offer recommendations, and chart their own development paths varies widely depending on whether they have an individualist or collectivist orientation.  Leading a global team requires more than just knowing broad cultural distinctions; it demands the cultural intelligence to accurately assess each situation and adjust the level of guidance, autonomy, and control to the unique values and preferences of each team member.

Too Much Psychological Safety.  Psychological safety is a crucial aspect of effective leadership. According to Harvard Business School’s Amy Edmondson, teams need environments where members feel accepted and comfortable enough to take risks and share concerns without fear of embarrassment or retribution.  But in many global teams safety, inclusion, and belonging are emphasized at the expense of intellectual honesty and the confidence to challenge the status quo—the exact opposite intention of psychological safety.  Leaders can use an array of strategies to create psychological safety without sacrificing intellectual honesty on culturally diverse teams. One is to develop team norms that guide behavior while embracing diverse perspectives.  To ensure that norms are explicitly inclusive, rather than defaulting to the dominant culture’s preferences, leaders can co-create norms with their teams—soliciting input from everyone, identifying where cultural differences come into play, and negotiating adjustments to gain the most from the diverse approaches.  Another tactic leaders can use is to adjust the types of questions they ask their teams. For instance, instead of asking a closed question that prompts a yes or no answer (such as, “Are we missing anything?”), rephrase it as an open-ended question that encourages participation: “What are we missing?”  Psychological safety is critical, but it must be developed with cultural intelligence to ensure that diversity moves beyond being a politically correct bonus to becoming a genuine source of improved performance and innovation.

Too Much Emphasis on Differences.  Understanding our differences has become the holy grail of inclusion initiatives and cross-cultural management training. It’s considered key to building innovative teams. And there’s plenty of evidence to support the power of diversity. Team members who have diverse backgrounds and perspectives offer built-in expertise for tackling problems and viewing products and projects more critically. But overindexing on differences can be damaging.  A meta-analysis of 199 cultural intelligence studies by Thomas Rockstuhl and Linn Van Dyne showed that knowing a lot about cultural differences can be more harmful than being culturally ignorant. When people become overconfident in their understanding of differences, it can lead to rigid, categorical thinking in which behavior is reduced to monolithic labels like being “German” or “Gen X” or an “engineer.” In addition, a heightened focus on how we’re different becomes mentally taxing and prevents dynamic, generative learning about one another.  One effective strategy for harnessing diversity’s benefits is for leaders to emphasize perspective-taking with their teams. Perspective-taking is the ability to step outside one’s own experience to imagine the emotions, perceptions, and motivations of someone else. Unlike empathy, which can sometimes confuse personal feelings and the team’s mission, perspective-taking enhances cognitive flexibility while maintaining focus on the task at hand.  Another way leaders can prevent an overemphasis on differences is by focusing the team on solving a shared problem.

Too Much Transparency.  Trust in leadership is at an all-time low. According to the 2025 Edelman Trust Barometer, a majority of people in 28 countries believe their leaders are deliberately misleading them. This isn’t only happening in countries that prefer flat leadership structures, such as the United States and Sweden. Leaders’ credibility is also low in places like Japan and France that prefer top-down leadership. The conventional wisdom in the West is that leaders gain trust by being vulnerable, authentic, and transparent. As former Starbucks CEO Howard Schultz put it, “The currency of leadership is transparency.” But those values chafe against the more nuanced, discreet communication styles preferred by much of today’s global workforce. It’s not that team members from other cultural contexts don’t value transparency; they just expect leaders to communicate transparently in ways that align with their own cultural norms. Global teams need their leaders to have a broader repertoire of communication styles so that they can effectively gain the trust of team members.  Western managers are told that owning their mistakes and discussing them openly is crucial for building trust, but for someone from a face-saving culture, it can be disorienting when a leader speaks candidly about a mistake. Leaders hold a position of authority and honor, and hearing them grovel about what they did wrong may actually erode trust. People already know when something has gone awry. Many team members from collectivist cultures would rather see their leader address the issue quietly and restore trust through actions rather than words. Owning mistakes is important, but the way leaders communicate about them needs to reflect the cultural differences on the team.

Standing out in today’s competitive job market requires more than just listing credentials on a résumé. Job seekers must learn to clearly articulate the unique value they bring and align their strengths with the needs of prospective employers.

Whether through storytelling, strategic research or data-driven examples, there are many ways candidates can better “sell” their qualifications. To help, 18 members of Forbes Human Resources Council share expert tips on how to most effectively communicate your skills and experience, all while leaving a lasting impression on hiring teams.

1. Translate Skills Into Real Business Impact.  One can see your qualifications on paper, but in an interview, one wants to hear how your skills and knowledge will translate to this role. How will you use them to succeed in this position? Even better: if you can take it a step further and explain how you’ll improve a company process, increase revenue or add value in a meaningful way.
2. Tailor Applications With Relevant Experience.  Candidates should research the company and job role thoroughly and tailor their application to show how their qualifications are relevant. Then, they should highlight past experiences where skills and particular qualifications were successfully applied.
3. Highlight Quantified, Goal-Aligned Achievements.  The best way to sell your qualifications is by showcasing clear, quantified achievements and demonstrating how you solve challenges in ways that align with the company’s goals and values. You need to show impact, not just experience!
4. Showcase Soft Skills Through Real Examples.  Simply padding your résumé with keywords isn’t enough to stand out in this saturated job market. Technical skills are teachable, so it’s the “soft skills,” like conflict resolution and empathy, that set candidates apart. Candidates should come to each interview armed with anecdotes that demonstrate their tactical experience and people skills.
5. Emphasize Adaptability And Hands-On Experience.  Job candidates should highlight how their skills align with the position they are applying for. One smart way to sell qualifications is to communicate the willingness to be adaptable to innovations and tactical strategies.
6. Go Beyond The Résumé With Personal Outreach And A Customized Cover Letter.  Candidates should go beyond a résumé with a customized cover letter that specifies why they want that role at that company. They should also update their LinkedIn and stay active to form personal connections. Then, they should contact the reporting manager or someone on the hiring team. Internal references can also help. Do they know an employee or a friend of one? They can ask them to pass on your résumé. Lastly, don’t do too much. When you do enough, your work can speak for itself.

The others are:

Use The STAR Method To Demonstrate Results

Share Stories Of Initiative And Growth

Speak The Company’s Skills-Based Language

Prepare Specific, Results-Driven Examples

Connect Experience To The Employer’s Needs

Link Experience To Tangible Business Outcomes

Align Your Narrative With Company Goals

Use Data To Prove Return On Investment

Show How You Solve Problems And Drive Future Success

Ask Thoughtful Questions To Show Curiosity

Share Your Career Story With Forward Momentum

And Frame Qualifications Around Business Priorities

Founders, especially successful ones, build a unique set of skills that are constantly in demand. And while launching, running, or selling a startup is the primary mission, there’s no reason you can’t profit off of those strengths on the side, by generating some passive income.

By leaning into the things that you have learned as you launched your business, you can prepare lessons or shortcuts for founders coming up behind you. To stand out in an increasingly crowded market, you’ll need to have a track record that impresses. But if you’ve created a successful business, here are four ways you can earn some additional passive income from the skills you picked up along the way.

1. Create Notion Templates.  Structure helps in the chaotic early days of a startup. Founders who have succeeded can put together Notion templates that provide a framework for startups, focusing on everything from team collaboration to project management. There’s no one-size-fits-all in any field, of course, but putting together a template that leaders can modify to their own needs is one way to attract a customer base. You can sell these templates on the Notion marketplace for passive income or open your own shop on Shopify to begin earning.
2. Write a playbook.  You’ve built (or are deep into building) your own business empire. A book is a good way to share how you overcame obstacles and the lessons you had to learn the hard way. Founders’ playbooks can be actual books, sold via Amazon’s Kindle Direct Publishing platform, a podcast or as a video series (you’ll need a sufficient number of followers to monetize in that format). You could even create an online course and sell it on your own website or through sites like Skillshare or Udemy.
3. Build a cohort-based course library.  Much like a playbook, a cohort-based course library is a way to impart your knowledge, in this case through a series of courses that foster communication and social learning. Students take the same lessons at the same time and work together to build skills in whatever area that particular course is focused on. If you’d prefer to take a more active approach, you can (of course) run these courses yourself, interacting with students and perhaps acting as a mentor. Alternatively, you can create a library of courses to sell to other instructors, letting you reach a wider audience.
4. Become a fundraising consultant. When it comes to helping other entrepreneurs find and secure funding, many former founders take on consulting roles, commanding impressive fees in the process. But if you’d prefer to spend your time on other ventures, there are still ways to help startups and earn some side cash at the same time. These range from putting together templates to assist with grant writing or organizing a fundraising round to online courses (or books) focused on best practices and strategies. One upside to taking a more active approach in this category? You’re more likely to find a company whose team and product impresses you to the point that you choose to become a backer yourself.

The digital world is saturated with social algorithms, pay-to-play platforms and constantly changing SEO strategies. However, one channel remains consistently consequential, direct and owned: email.

Building an email list early on in your business development isn’t just a marketing move for startup founders and business leaders; it’s a smart growth strategy. Yet many wait until too late, focusing instead on social media followers or one-off ad campaigns. Email is where genuine relationships are nurtured, conversions happen and loyal communities are built.

The magic number? Your first 1,000 subscribers. This isn’t a vanity milestone or one I simply pulled out of nowhere — it’s the start of a high-value, compounding asset. Here’s a framework to get you there faster and smarter.

Define who you’re talking to (and why it matters).  Before writing a single email or designing a signup form, answer this: Who are your ideal subscribers, and what do they want from you?  You’re not collecting email addresses to simply just collect them. You’re doing so to start a conversation. Getting hyper-specific with your audience will be the best thing you can do to ensure those conversations are valuable. Once you’re clear on your ideal audience, define your unique value proposition. It should answer the following questions: Why should someone join your list? What will they get in return?

Create an irresistible lead magnet.  In 2025, people won’t give away their email for just a “newsletter.” They want value, and they want it now.  A lead magnet is a free, high-value offer that your target subscriber can receive instantly in exchange for their email. Effective lead magnets typically include:  Checklists or cheat sheets, Industry trend reports or whitepapers, Templates or toolkits, Short video tutorials or mini-courses, Quizzes with personalized results, Discount codes or early access (for product-led businesses).  Your lead magnet should be hyper-relevant to your offer and audience. Make it:  Easy to consume (no 50-page PDFs), Immediately actionable, and aligned with what you plan to sell later.

Optimize your signup experience.  You’ve got attention. Now, remove friction.  Place your opt-in form or landing page where it matters most:  Website homepage, Blog posts with relevant content, Top bar or exit-intent popups, Product pages, Social bios and link trees, and partner content (guest blogs, webinars, etc.).  Make the form frictionless:  Ask only for what’s essential (usually just name + email), Use persuasive microcopy (“Get the free guide” instead of “Submit”), Add social proof if possible (“Join 850+ founders getting weekly growth tips”), and make sure the design is clean, mobile-friendly and aligned with your brand voice.

Launch a welcome series that converts.  Your first few emails set the tone. A welcome series isn’t just polite — it’s strategic.  Here’s a simple three-email sequence to start:  Email 1: Deliver the lead magnet and set expectations – Introduce yourself. Explain what they’ll get from your emails and how often.  Email 2: Your origin story and value add – Share why you started this business and how it helps them. Include a helpful tip or insight.  Email 3: Social proof and soft CTA – Highlight a testimonial, case study or popular product. Include a light-touch call to action (visit your website, book a call, check out your offer).  This sequence helps build trust before selling — the key to sustainable growth.

Drive targeted traffic to fuel growth.  Now that your system is ready, it’s time to get eyes on it. Don’t wait for organic search to work; get proactive.  Here are five scalable traffic sources:  Organic social: Share lead magnet snippets on LinkedIn, Instagram and X. Use storytelling and pain-point content.  Partnerships: Do email swaps or joint webinars with complementary businesses.  Paid ads: Run low-budget tests on Meta or Google Ads with lead magnet landing pages.  Communities: Engage in relevant Slack groups, subreddits and forums — share value and link to your opt-in.  Content marketing: Blog posts optimized for long-tail keywords that tie into your lead magnet.  Pro tip: Use UTM parameters to track which channels bring the highest-quality subscribers.

Segment and engage (even with a small list).  You don’t need 10,000 subscribers to start segmenting — you just need an intelligent system.  Tag or segment based on:  Source: where they signed up.  Behavior: what they clicked or downloaded.  Interest: what content they engage with.  Then, personalize future content, send relevant offers and nurture based on behavior. The more relevant your emails, the faster your list will grow because people will start sharing them.

Don’t just build — engage.  Your email list is not a vault; it’s a living asset. Keep it warm.  Show up consistently — whether it’s weekly, bi-weekly or monthly.  Deliver value more often than you pitch.  Encourage replies (and read them).  Test different types of content: behind-the-scenes stories, how-tos, Q&As, curated lists.  When people feel heard and helped, they stay. And they share.

Reaching 1,000 subscribers isn’t about overnight success. It’s about setting up a repeatable, value-driven system that compounds. Once you have it, every new partnership, blog post or campaign fuels a growing engine.  Email marketing isn’t just a channel — it’s your direct line to the people most likely to become loyal customers, fans and ambassadors. Start building that line early, and your future self (and business) will thank you.