Growth has traditionally meant building bigger. More offices, staff and layers of management. Customers want fast and simple processes, so increased capacity only made sense. However, it also resulted in delayed decision-making and higher overhead expenses. That outdated strategy is no longer effective in today’s dynamic economy. For many scaling businesses, the question we’re all trying to answer is how to get bigger without making things harder for everyone.

A straightforward answer is to shift focus from hiring based on location to hiring for specific results. Strategic offshoring provides companies with a head start. This strategy enables companies to create a focused team for a particular function, be it around-the-clock customer support or back-office processing.

Entrepreneurs get the exact expertise and output they need while operating on a timeline they can define — all without adding a single square foot of office space or another layer of management. This is talent leverage, where you must start looking at talent as a high-precision tool, not just a measure of headcount.

Related: 3 Reasons Why a Hiring Pause Is the Best Time to Recruit Top Talent

The strategic flexibility of remote teams

Traditional hiring often ties companies to large, fixed costs, such as offices, equipment and lengthy contracts, which may not always align with demand. Remote teams break this pattern. They provide businesses with enough flexibility to scale up or down as needed by accessing talent from anywhere without the varying challenges of being constrained to a single location.

Launching a product? You can bring in specialized designers for a set project. Managing seasonal demand? Expand your customer service team without year-round expense. Remote teams aren’t a “shortcut” to success; they’re a more agile operating model.

Many entrepreneurs may still be reluctant to the idea, but keep in mind that flexibility doesn’t equate to chaos. The most effective distributed teams thrive on clear systems, accountability, and collaboration tools that make distance irrelevant.

Related: The Value of Flexible Management in the Age of Remote Work

AI that pays for itself

The endless tide of repetitive tasks that consumes leadership and high-value employees drags your business. Reporting, scheduling, pipeline updates and data processing — they’re necessary, but they dilute focus from strategy.

That’s where AI starts to change the game. Instead of teams getting buried in admin tasks, modern tools can sort leads in seconds, manage outreach seamlessly, recap meetings in great detail and turn messy data into clear insights. The point isn’t to replace people’s judgment but to give teams back the hours they need to think, create, and lead.

And I’ve personally seen it work. In fact, even recent studies show that teams taking advantage of AI are significantly more productive, by as much as nearly 60% per head. This shows tangible impact when AI is integrated into everyday workflows.

Run the clock, not the workplace

Remote teams give you reach. AI gives you efficiency. Together, they unlock a new kind of scalability.

Picture entering a new market. Traditionally, it included recruiting full-time employees and spending a lot of money up front before seeing any results. When people and technology are properly integrated, multinational teams may continue working on projects basically 24/7. Leaders may now make choices that used to take weeks in a couple of days, thanks to AI systems that provide real-time data.

These result in accelerated and more foreseeable growth. Instead of managers exhausting themselves or their systems collapsing under pressure, companies keep their focus where it matters most, such as on innovation and strategy.

Related: How AI Agents Are Making Remote Work More Cohesive

From size-based to systems-based growth

Scaling today isn’t about leaping at every new trend or treating remote work as a band-aid solution to bigger challenges. It is about building in those systems in which people and technology each do what they do best. People bring judgment, creativity, and empathy. AI does the repetitive work that holds everything else back.

Growth today isn’t about size for its own sake. It’s about thinking smarter. Remote talent, combined with technology, creates a solid foundation for that sustainable growth, even in volatile markets.

Growth has traditionally meant building bigger. More offices, staff and layers of management. Customers want fast and simple processes, so increased capacity only made sense. However, it also resulted in delayed decision-making and higher overhead expenses. That outdated strategy is no longer effective in today’s dynamic economy. For many scaling businesses, the question we’re all trying to answer is how to get bigger without making things harder for everyone.

A straightforward answer is to shift focus from hiring based on location to hiring for specific results. Strategic offshoring provides companies with a head start. This strategy enables companies to create a focused team for a particular function, be it around-the-clock customer support or back-office processing.

Entrepreneurs get the exact expertise and output they need while operating on a timeline they can define — all without adding a single square foot of office space or another layer of management. This is talent leverage, where you must start looking at talent as a high-precision tool, not just a measure of headcount.

Artificial intelligence now powers much of the modern economy. It verifies our identities, assesses loans, detects fraud and helps doctors read scans. All of it depends on one input: data. Large, reliable, representative data. In many parts of the world, that foundation is thin or missing.

For billions of people, the data needed to build effective AI systems is incomplete, fragmented or skewed. This isn’t just a technical challenge; it is a structural barrier that risks deepening inequality and determining who benefits from the AI economy and who is left behind.

Related: AI to Boost Global GDP by USD 15.7 Trillion, But Divide Widens

The data divide beneath everyday AI

Every AI system— whether it scans for fraudulent transactions, analyzes speech or predicts disease — depends on high-quality datasets, such as transaction histories, voice recordings and medical images. In advanced economies with decades of digitized records, these datasets are abundant.

Elsewhere, the picture is stark. According to a 2025 IMF analysis, economies with mature data ecosystems are capturing AI-driven productivity gains at roughly triple the pace of those without. This gap is widening, and if left unaddressed, it will accelerate the global economic divide.

The risks are not abstract. When models trained on data-rich environments are deployed in data-constrained ones, they often fail. A credit model designed for Singapore can reject qualified borrowers in Central Asia, not because of overly high repayment risk, but because of data drift. A diagnostic system trained on U.S. hospital records may miss critical signals in clinics where patient files are incomplete. In each case, people are excluded from opportunities not because of their capability, but because the data to represent them simply does not exist.

From scarcity to ingenuity

Waiting for perfect datasets is not an option. The more urgent task is to design AI systems that perform under conditions of scarcity.

A key emerging approach at the core is synthetic data generation. A subfield of AI established a decade ago, synthetics consist of realistic, AI-generated datasets designed to complement existing datasets, however limited those may be. Synthetic data generation embeds an advantage of enabling the training of models on rare or extreme scenarios — overcoming challenges with underrepresented data in historical records.

Another path is adaptive learning. Through techniques like transfer learning, models built in data-rich environments can be fine-tuned for new markets with far less local data. This makes it possible for institutions in emerging economies to benefit from global advances in AI without having to wait for decades of digitized records to accumulate.

Federated learning offers a third solution. Instead of pooling sensitive information into a single central repository, institutions can train models locally and share the insights, thereby preserving privacy while bridging fragmented data landscapes.

Together, these approaches make AI resilient even when the data is thin — and crucially, they ensure that progress is not confined to economies already ahead.

Building guardrails

Technology alone is not enough. Governments and industry must accelerate the digitization and standardization of core records, from credit histories to health files.

Equally important is designing systems that acknowledge their own limits. AI models should be engineered to quantify uncertainty and signal when human oversight is required. In finance, for example, an automated system should flag instances where its confidence is low, allowing a credit analyst to step in. In healthcare, a diagnostic model should highlight when incomplete inputs could undermine reliability.

Handled this way, scarcity can shift from being a barrier to becoming a catalyst for innovation.

Related: AI and the Global Economy: Growth or a New Divide?

The stakes

The global opportunity is enormous. But unless the data gap is addressed, billions of people risk being excluded from these gains.

We face a choice. Either we build AI models that serve only the data-rich, or we develop systems that extend opportunities for all. The second path will require creativity, investment and collaboration across borders. It will also require courage — to innovate beyond the current limits — and to design AI systems that are robust even in the absence of perfect information.

If we get this right, AI will not just accelerate economies that already work. It will extend access to finance, healthcare and economic opportunity to the billions still excluded. The technology shaping our future should be a force for inclusion. By rethinking how we build and deploy it in a world where data is often missing, we can turn scarcity into opportunity and ensure that AI delivers on its promise of equitable progress.

Artificial intelligence now powers much of the modern economy. It verifies our identities, assesses loans, detects fraud and helps doctors read scans. All of it depends on one input: data. Large, reliable, representative data. In many parts of the world, that foundation is thin or missing.

For billions of people, the data needed to build effective AI systems is incomplete, fragmented or skewed. This isn’t just a technical challenge; it is a structural barrier that risks deepening inequality and determining who benefits from the AI economy and who is left behind.

Related: AI to Boost Global GDP by USD 15.7 Trillion, But Divide Widens

This Side Hustle Spotlight Q&A features Jordan Harper, 38, of Knoxville, Tennessee. Harper was working as a nurse practitioner, providing in-office aesthetic treatments for patients, when she started the side hustle that would become Barefaced, a multimillion-dollar skincare brand. Responses have been edited for length and clarity.

^{Image Credit: Courtesy of Barefaced. Jordan Harper.}

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When did you start your side hustle, and where did you find the inspiration for it?
I started posting skin tips and education on Instagram through my nurse practitioner lens in 2015. At first, I was not trying to create a brand: I simply wanted to share skincare education in a way that felt approachable and easy to understand and grow my patient base. What I discovered was that my patients were overwhelmed by all the noise in the skincare industry, and I wanted to simplify things for them. That spark, combined with finding solutions to their challenges, turned into Barefaced, which I officially launched in 2020.

What were some of the first steps you took to get your side hustle off the ground? How much money/investment did it take to launch?
The first steps were very grassroots: educating on Instagram, connecting with my patients and people through DMs and sharing routines that worked. I had no business background and didn’t know anything about raising money, so naturally I took out five 0% interest credit cards and sent an online pre-order to my Instagram community to fund the first production order. Initially, my thought was to only sell products to patients in my office, but word-of-mouth spread quickly, and the pre-order sold thousands of units, so I knew I was on to something.

^{Image Credit: Courtesy of Barefaced}

Related: I Turned a Side Hustle Into $20,000 a Month — Working Part-Time Without a College Degree

Are there any free or paid resources that have been especially helpful for you in starting and running this business?
Community feedback has been my most valuable resource. Listening to what my patients and people were confused or struggling with surrounding their skin guided everything. On the practical side, when I couldn’t afford to hire anyone, I leaned on Google and Upwork and was always looking for people to connect with who were more knowledgeable in the DTC space than I was. I can’t imagine how much easier it would be now with ChatGPT. Over the past four years, we have been very intentional about shaping the brand story and the overall customer experience. Our community has always been at the center of what we do, and we constantly look for ways to serve them better and create an exceptional journey every time they interact with us.

If you could go back in your business journey and change one process, what would it be, and how do you wish you’d done it differently?
If I could go back, I would have built out our C-suite earlier. In the beginning, every hire was a do-it-all person, which worked for a while, but it also meant we lacked the structure and management needed to scale efficiently. Two years ago, the business could have afforded senior leadership, and having that guidance sooner would have saved time, energy and growing pains. This year, we finally built out our C-suite, and having experts lead from different perspectives has been transformative. If I could do it differently, I would have made that move earlier to strengthen our processes and overall structure.

Related: I Was a 25-Year-Old Nurse When I Started a Side Hustle to Combat Anxiety. It Made $1 Million in 7 Months — Then Sold for a Life-Changing Amount.

When it comes to this specific business, what is something you’ve found particularly challenging and/or surprising that people who get into this type of work should be prepared for, but likely aren’t?
One of the most surprising challenges I faced once entering the beauty industry was realizing just how much brands rely on constant newness. Many release product after product to create hype and energy, and I quickly understood why: It drives momentum. At Barefaced, though, our philosophy is “less but better.” We promise to deliver best-in-class, results-driven skincare without adding clutter or unnecessary steps, which means we launch fewer products than most.

The challenge, and what I think many founders aren’t prepared for, is finding creative ways to curate experiences and build excitement that don’t depend on endless launches or limited-edition drops. It requires thinking differently about longevity: How do you keep customers engaged, supported and connected to your brand when you’re not giving them something “new” every other month? That has pushed us to innovate not just in formulation, but in education, storytelling and customer experience.

^{Image Credit: Courtesy of Barefaced}

Can you recall a specific instance when something went very wrong, and how you fixed it?
We once produced 50,000 units of eye patches, and when I received the final production run, they didn’t meet our standards. They were supposed to be 0.4 millimeters thick, but they came in at one millimeter, and the tins we had chosen for packaging began chipping after just a few uses. A half millimeter may not sound like much, but those details are what separate good from great — and customers feel the difference. Technically, they were usable, and many brands might have moved forward, but they did not reflect Barefaced or our “best-in-class” promise. I made the decision to scrap them, which meant a $100,000 loss and a six-month delay in our launch. It was one of the hardest calls I’ve made, but protecting our integrity and maintaining customer trust mattered more than taking the easy way out.

Related: Nurse Makes $2 Million In One Year With Side Hustle Selling Class Notes Online

How long did it take you to see consistent monthly revenue? How much did the side hustle earn?
I saw consistent revenue from the very beginning, and I truly believe that was because I had been educating my community, both in office and online, for five years before launching Barefaced. By the time the first product came out, there was already a strong, loyal customer base waiting for it. Within the first three months, it was clear this wasn’t a side hustle; it was a real business. We finished our first year at eight figures in revenue, which confirmed the demand and gave me the confidence to keep building.

What does growth and revenue look like now?
Barefaced has continued to grow year over year and has grown into a nine-figure brand with a full team, a loyal community and an in-house R&D team. Growth is not only about revenue — it is also about the trust we have built as a reliable skincare resource.

Related: College Friends Started a Side Hustle With $50. It’s ‘So Fulfilling’ — and On Track for $500,000.

^{Image Credit: Courtesy of Barefaced}

What do you enjoy most about running this business?
Seeing the transformation in people’s confidence. We’ve been so fortunate that women are willing to share their personal stories with us because how you feel in your skin is so personal and vulnerable. It’s not lost on me that they open up about this part of their lives. We’ve received thousands of before-and-afters over the years, but the ones that stay with me aren’t just about skin — they’re about confidence.

One that comes to mind is a mom who had always struggled with breakouts and redness. She told us she avoided photos and often felt like her skin kept her on the sidelines. She started using a Barefaced routine in the months leading up to her daughter’s wedding, and afterward she sent us a message saying it was the first time in years she didn’t think about her skin. Instead, she felt present, beautiful and confident celebrating her daughter.

That’s the impact that fuels me: not just improving skin, but giving women the freedom to fully show up in their own lives.

Related: Couple’s Side Hustle Surpassed $1,000 a Day Fast — With ChatGPT’s Help

What is your best piece of specific, actionable business advice?
My best advice is to start building a community on social media as early as possible. Show your face, answer questions and share the messy middle of your journey. People don’t just buy products; they buy trust. The fastest way to build that trust is by showing up consistently, even if imperfectly. Social media is a free and powerful tool to grow a community, listen to what people actually want and test ideas in real time. If you do this well, by the time you’re ready to launch a product or service, you’ll already have a loyal customer base, and you may not even need outside funding.

If you want your team to come up with brilliant ideas, you can’t just tell them to “be creative.” You have to create a culture where people feel safe, happy and heard, because that’s when the ideas start flowing. A growing body of research shows that psychological safety is the secret ingredient behind innovation.

Employees who feel psychologically safe at work are significantly more likely to contribute creative solutions, especially at the team level: That’s what a meta-analysis of 94 studies with more than 19,000 participants found.

Similarly, a 2024 survey of 580 high-tech employees found that teams with more collaboration, open information sharing and a fair give-and-take achieved much higher levels of innovation. These studies are only two of countless examples proving the point. And it confirms what I experienced in decades of leading scientific teams: When people feel safe, they help one another and collaborate on real fixes.

Related: Why You Should Care About Psychological Safety in the Workplace

Empathy, openness and collaboration

In practice, leaders often struggle to apply these lessons, and I’d argue that’s especially true for leaders of technical and scientific teams. Having a talented team alone isn’t enough to drive innovation.

Over the years, leading teams in science-driven organizations, I’ve actually experienced how much true innovation depends on empathy, openness and collaboration. What keeps surprising me, even after years of doing this work, is just how influential that kind of leadership mindset is.

In the technical side of a business, it’s so easy to get caught up chasing the next result, always focused on the next experiment or deadline. But real progress only happens when we slow down enough to actually listen to each other. Making time and space for empathy and honest conversations isn’t a luxury. It’s the foundation everything else stands on.

Collaborative leadership isn’t just about everyone sitting around the same table (or ordering pizza together). It means giving real, thoughtful feedback and making sure every voice gets heard. I’ve lost count of the times a breakthrough came from a direction nobody expected — whether from another department, or from someone new who’d only just joined the team.

In my experience, innovation happens when people across disciplines and hierarchies feel encouraged to speak up and trust that their leaders will help move their ideas forward. I talk to anybody who has an idea and help them to formulate it, talk to others and see if it’s something people will want to do. Leadership isn’t just about vision or managing a project. More than anything, it’s about creating an environment where people articulate and develop their ideas with colleagues.

Related: 5 Key Ways to Create an Innovation Culture

Welcome the “bad” ideas

A culture that encourages even so-called “bad” ideas is a culture where real solutions emerge. I often tell my teams: You carry your bad idea book, you write down your bad idea. Then you try to turn it into a good idea. It’s not enough to just have that idea, try to formulate the thesis from it and talk to people.

Some of our best solutions started out as half-baked thoughts. By giving people the freedom to think their crazy thoughts out loud, things that may seem wildly impossible at first, we end up with creative leaps that wouldn’t have happened in a more judgmental environment.

In our team, we created this culture of transparency, providing feedback and cultivating ideas. We really promoted that. When people know their contributions are valued, they step up. They challenge assumptions. They take risks. And that’s the point at which innovation becomes the norm, not the exception.

Harvard Business School even did a case study on ICL’s BIG‑Big Innovations for Growth program that highlights how, by systematically listening to every employee’s ideas and nurturing them, the company sparks breakthrough projects and drives sustained growth.

Related: Why Leaders Must Encourage Their Employees to Explore Their Creativity — and How to Do It

What leaders must understand

Leaders need to think about what that actually means for them. Expecting excellence is one thing, but creating too much pressure, micro-managing or leaving no time for out-of-the-box thinking are prime innovation killers. The conversation about innovation in leadership is ongoing, but in my experience, it’s simple. Small, everyday choices that create an atmosphere of trust are what lead to breakthrough ideas.

Even with all the evidence, old habits persist in technical organizations. A 2024study by Deloitte found that only half of workers report that their team leaders create the psychological safety necessary for them to be themselves at work. Being open is just that: Allowing people to be as they are. Not having a preconceived notion of the perfect team member and the right answer.

When you put your ego aside, amazing things happen. And naturally, the best ideas come from the most unexpected places. It’s something you witness more and more as you let go, let your team work and invite in all those different perspectives to build something together. It takes patience, and sometimes it means things move more slowly, but it’s always worth it.

If you want to spark innovation, start by building trust. Start by listening. Start by encouraging ideas, including those that seem rough around the edges. With the right leadership mindset, you can make your team’s innovation spirit skyrocket!

AI is transforming how business leaders across industries structure their organizations and manage workflows. From automating repetitive tasks once handled by entry-level employees to accelerating high-level decision-making, AI delivers speed and scale far beyond human capabilities. Even the most talented and efficient employees can’t match its raw output.

But let’s be clear: AI isn’t taking over the world — or replacing every job. Despite its speed, AI is far from infallible. It still makes hallucination-level mistakes, logical missteps and often lacks the emotional intelligence required in leadership, collaboration and decision-making. Human judgment remains essential — particularly when relationships, context, and nuance are at play.

As companies race to integrate automation, leaders should see AI not as a substitute for people but as a force multiplier. Long-term success will depend on balancing the advantages of AI with the irreplaceable human traits — context, empathy, ethics — that machines can’t replicate.

Here are four practical tips to help you know when to lean into AI and when to rely on the human touch:

1. Know what AI can (and can’t) do

A simple rule of thumb: If a task is expensive, repetitive and repeatable, it’s probably a good candidate for automation.

Take coding. An engineer’s time is costly. Backend development is often repetitive and follows predictable patterns. And it’s highly repeatable across projects. That’s why AI excels at tasks like structuring data, reconciling datasets, and unifying data formats.

The same applies in customer service. Agents often spend hours answering the same basic questions. AI chatbots can handle these routine interactions, freeing up humans to solve complex problems AI can’t yet tackle. Automation doesn’t eliminate the human role — it elevates it.

Related: Can AI Come Up With Better Ideas Than People? Sort Of, Science Says.

2. Know when to lean in — and when to step back

AI can significantly amplify productivity. For example, a top engineer might previously have written 5,000 lines of code a month. With AI, that number can reach 30,000. But speed without precision is risky. The most effective developers now act like editors — prompting AI to generate smaller chunks of code, then reviewing and refining them to ensure accuracy.

The same applies to business tasks. Asking ChatGPT to generate a full slide deck or report can be tempting — but don’t set it loose without oversight. It’s still prone to factual errors, misinterpretations and awkward phrasing. The better approach: break the task into pieces, guide the AI with detailed prompts and curate its output. The result? Faster turnaround and higher quality.

3. Lead with a human-centric approach — use AI as a partner

We recently held a hackathon aimed at building new features for a financial product using AI. Teams were given 24 hours to deliver working prototypes using LLMs to source and rank deals.

The results were extraordinary. Coding faster than ever before, each team demoed a fully functional feature — many of which would have taken a month to build without AI. The client wanted everything we built.

The real win wasn’t just AI speed. It was the combination of smart tools, cross-functional teamwork and a bit of healthy competition that made the difference. AI didn’t replace human effort — it amplified it.

Related: Stop Trying to Outwork AI — Focus on These Soft Skills That Make You Irreplaceable

4. AI is changing the game — but you still need humans to play

AI is here to stay — and its impact on productivity is undeniable. But it’s not the whole story.

The most successful leaders won’t just automate — they’ll elevate. They’ll use AI to take care of repetitive tasks so their teams can focus on what matters most: strategy, creativity, trust, and human connection.

As AI continues to evolve, the bar for innovation will rise. But so will the value of the people behind the technology. Don’t just invest in AI. Invest in your people’s ability to work with it.

Behind every ChatGPT response and Midjourney image lies an uncomfortable truth: Four corporations now control the physical and intellectual infrastructure of artificial intelligence. Add to that, NVIDIA’s 92% monopoly on AI chips, combined with the cloud dominance of Amazon, Google and Microsoft. As economist Mariana Mazzucato observes, we are moving toward an age of “digital feudalism,” where public risk and knowledge are preceded by private extraction, concentrating control over AI’s means of production.

The raw economics reveal why this matters. Training cutting-edge AI models now costs between £60-150 million per run — a figure projected to exceed £800 million by 2027. These aren’t just development costs; they’re admission tickets to the AI arms race, priced to exclude all but tech giants and nation-states. A leading AI infrastructure figure admitted that his startup directs most of its capital to NVIDIA GPUs and cloud services like AWS, which underscores that compute costs dominate budget allocation and reinforces dependency on a few major providers.

Related: Open Source AI and Blockchain Will Define Global Dominance in the Future

Why data solutions aren’t enough

Recent concerns about AI’s “garbage in, garbage out” problem rightly highlight data quality issues. Blockchain-based verification systems — like those tracking data provenance in healthcare AI, are crucial for building trustworthy systems. However, they address only half the crisis.

Consider this paradox: Even if we achieve perfect data transparency through decentralized verification, it means little when:

• The models processing that data remain corporate black boxes

• The compute running those models sits in proprietary clouds

• The chips powering everything flow through one company’s fabs

Although Anthropic bills Claude as “open” and Meta markets Llama as “open-source,” in practice, both reinforce centralization. They rely on the same locked-down chip and cloud infrastructure, and their licensing terms restrict real access. Critics have likened this to an “illusion of choice” inside a walled garden, where openness becomes a strategic veneer, not real decentralization.

Decentralization’s infrastructure revolution

True alternatives require reinventing AI’s physical backbone. Three disruptive approaches are emerging:

1. Hardware liberation:

Startups like Rivos and AheadComputing are pioneering open-source chip development using the RISC‑V instruction set. Their aim is to dramatically reduce chip design cost, from billions to mere millions — mirroring how Linux disrupted proprietary UNIX systems. Academic efforts such as Croc (ETH Zurich) also demonstrate how RISC‑V-based systems can support edge AI, helping decentralize silicon development away from NVIDIA and ARM monopolies.

2. Compute commons:

The DePIN sector, led by projects like Akash Network and Render, is proving that distributed computers can scale. Akash now offers up to 90% cost savings compared to traditional cloud platforms like AWS and Azure, and its Supercloud has delivered tens of millions of GPU memory-hours. Real-world case studies show startups reducing infrastructure costs by over 50%. These networks challenge cloud monopolies by making computer access permissionless, scalable and geographically distributed.

3. Protocol-owned AI:

Decentralized protocols like Bittensor are building tokenized ecosystems for machine intelligence. Rather than routing value upward to centralized AI labs, Bittensor pays contributors whether they supply computers, models or training in its native $TAO token. The project has reached a multi-billion-dollar market cap, signaling appetite for alternative AI governance models. Similarly, there are other projects that create tokenized data marketplaces where users maintain control over their data and earn fair compensation for sharing it, all while ensuring transparency and provenance.

The political economy of AI development

This isn’t merely technical infrastructure — it’s democratic infrastructure. When AI systems influence everything, from medical diagnoses to judicial decisions, their architecture becomes political:

• Centralized AI replicates colonial patterns: Value extraction flows from many users to a few owners

• Decentralized AI enables what researcher Audrey Tang calls “pluralistic interoperability,” where diverse communities co-create intelligence tools

The EU’s Digital Markets Act has begun recognizing this, requiring interoperability for “gatekeeper” platforms. But we need bolder policies that fund public AI compute clusters, support open-chip manufacturing initiatives and treat AI infrastructure as essential public goods.

Related: Moving Towards Decentralization

Rebuilding the stack

The AI revolution presents a stark choice: Will we accept corporate-controlled intelligence as inevitable, or build alternatives that distribute power as radically as the technology itself?

This goes beyond fixing data quality — though that remains crucial. It requires reinventing every layer of the stack, from silicon to software, through:

• Open hardware to break chip monopolies

• Distributed compute to escape cloud captivity

• Tokenized ecosystems that reward contributors fairly

The companies controlling AI today didn’t invent neural networks. They privatized public research while building moats around implementation. Now, decentralized technologies offer tools to reclaim AI’s infrastructure for collective benefit.

As we face down what may become history’s most consequential monopoly, one truth becomes clear: In the age of machine intelligence, infrastructure isn’t just about what works. It’s about who governs.

In 2024, the tech sector saw more than 152,922 employees lose their jobs, according to data compiled by Layoffs.fyi. While this trend has slowed slightly in 2025, it serves as a stark reminder of how quickly market conditions can shift and how vulnerable even the biggest names in business are to economic pressures.

When the purse strings tighten and headcount freezes, many business owners instinctively put their entire hiring process on hold. However, for savvy founders, this time is a strategic opportunity to build a more resilient, future-ready talent pipeline.

Smart entrepreneurs know that staying engaged with high-impact candidates, even when they’re not actively hiring, can create a powerful competitive advantage. It all comes down to shifting your mindset from reactive hiring to proactive talent engagement. Instead of waiting for a new budget to be approved and then scrambling to fill a role, you can use these quieter moments to build the relationships that will put you ahead of the competition when the time is right.

I’ve spent years helping companies build and scale their teams. I’ve seen firsthand how a bloated, reactive approach to hiring can fall apart in a downturn, and I’ve seen how a more agile, strategic approach can help a company thrive. Here’s how you can make your hiring process more resilient and stay top-of-mind with top-tier talent:

Related: 3 Strategies to Optimize Your Hiring Process and Find the Best Employees

1. Emphasize a lean, relationship-driven approach

When economic pressures mount, it’s easy to assume that a robust hiring process requires a large budget and a massive recruiting team. But in reality, a more nimble, high-touch approach is far more effective. Boutique-style recruiting prioritizes quality over quantity, focusing on building genuine relationships with a smaller pool of exceptional candidates. This model is all about efficiency and impact, which is perfect for an unpredictable market.

Consider the recruiting process at Shopify, a company that has undergone a significant transformation and adapted its hiring strategy to a changing landscape. Following a major round of layoffs, the company has shifted its approach to focus on more strategic, targeted hires. Rather than casting a wide net, Shopify has homed in on building a lean team with specific, high-impact goals. This allows it to stay agile and avoid the “bloated” feeling that can plague companies in uncertain times, a sentiment that CEO Tobi Lütke has emphasized in the past.

When your process is relationship-driven, every interaction is an opportunity to showcase your company’s values and culture, creating a more memorable experience for the candidate. Instead of relying on a convoluted, multi-stage interview process, focus on making every touchpoint meaningful and personal. This attracts better talent while also reducing the time and money spent on candidates who aren’t the right fit.

Related: I Hire a New Employee Every Week. Here’s What This Practice Has Taught Me About Hiring and Recruiting

2. Proactively build your talent pipeline

In a tight economy, waiting until Q1 to start hiring means you’re already behind. By the time your budget is approved and you post a job, the competition has already had months to build relationships with top talent. Instead, use a “hiring hold” to focus on pre-engagement and passive search. This means identifying potential candidates, making contact and building rapport long before a role is open. This can be as simple as sending a personalized email, connecting on LinkedIn or inviting them to a virtual coffee chat to learn more about their career goals.

One of the most powerful tools in this process is leveraging your internal network. Referred candidates are seven times more likely to be hired than those who apply through career sites, according to Pinpoint. Encourage your current employees to act as brand advocates and share company wins, market insights and team growth on their own social media channels. Not only is this a low-cost way to reach new candidates, but it also provides a more authentic glimpse into your company culture.

This type of proactive networking is more focused on nurturing relationships than immediate hiring. Keep top candidates engaged with updates on company progress, share thought leadership content and celebrate wins. This keeps your company top-of-mind and ensures that when a role opens up, you already have a list of qualified, interested candidates ready to go.

Related: How to Hire Peak Performers with These 5 Proven Strategies

3. Refine your hiring machine

When your company is in a hiring sprint, it can be difficult to take a step back and identify what’s working and what isn’t. A hiring pause is the perfect time to audit and refine your processes. This can involve everything from updating job descriptions to be clearer and more inclusive to identifying and removing redundant steps in your interview process. A lean, agile hiring machine is not only more cost-effective but also more effective at attracting top talent.

Let’s consider the story of the video conferencing company Zoom. During the pandemic, Zoom experienced an unprecedented period of hyper-growth. However, as the world began to return to a new normal, the company had to scale back and reevaluate its strategy. This forced Zoom to streamline operations, including its hiring process. By taking a closer look at what was working and what wasn’t, the company was able to make its recruiting more efficient, focusing on the roles that would drive future growth rather than those that were simply filling a need in a moment of rapid expansion.

Use this time to identify your most critical hiring goals for the next quarter. What roles are absolutely essential for your Q1 success? What skills will be most valuable? By clarifying your priorities, you can ensure that every dollar you spend and every minute you invest in recruiting is strategic and effective.