Latest news with #CharlesYeomans
Forbes
12 hours ago
- Business
- Forbes
When AI, Energy Demands And Capital Costs Collide
Charles Yeomans is the chairman and founder of Atombeam. In a testimony to Congress in April 2025, Google's former CEO Eric Schmidt addressed not only the future of AI but also the dramatic power consumption associated with it. Although the dramatic increase in energy used by data centers associated with AI workloads is well known—and my last article touched on this crucially important issue—the sheer scale of the challenge is becoming clearer. Uncharted Territory More specifically, Schmidt noted that data center energy consumption could quickly increase from 3% of the power generated today to 99% of that current value, with data centers requiring 67 more gigawatts of power by 2030. He also subsequently put that point in perspective by pointing out that the average nuclear power plant creates about 1 gigawatt of power, even as technology providers plan for 10-gigawatt data centers. Although those figures are subject to some debate—the U.S. Department of Energy, for example, noted that data centers consumed about '4.4% of total electricity in 2023 and is expected to consume from 6.7 to 12% of total U.S. electricity by 2028'—one thing is certain: The proliferation of AI and the more powerful computational capabilities fast-evolving use cases, algorithms and models demand brings with it a power generation challenge of unprecedented proportions. Not surprisingly, to date, nuclear power is seen as the most viable, although still arguably insufficient, answer—a reality made clear by the nuclear ambitions of the largest technology providers. In early May 2025, Elementl Power announced its agreement with Google to build 'three sites for advanced reactors,' as reported by CNBC. Notably, though, Elementl Power, founded in 2022, has yet to build any nuclear sites. We're in uncharted territory. Coming To Terms Clearly, questions abound not just about national security—something Schmidt also stressed—and the feasibility not just of how data centers will be powered but also how quickly they can be built to keep up with AI. This is a point I also made in my last article while pointing to October 2024 research from McKinsey analysts, who found that 'to avoid a deficit, at least twice the data center capacity built since 2000 would have to be built in less than a quarter of the time.' One point, though, has yet to get the attention it fully deserves as we collectively come to terms with the dramatic demands associated with AI. That point is the sheer scale of the capital needed to facilitate the building boom required to create the infrastructure AI demands. But that, too—like the scale of the power consumption Schmidt brought into focus—is beginning to become more clear as April 2024 research, also from McKinsey analysts, reveals. That study predicts that by '2030, data centers are projected to require $6.7 trillion worldwide to keep pace with the demand for compute power.' Consider for a moment the awe-inspiring reality of that number. In 2025, total federal outlays in the U.S. are predicted to reach $7.0 trillion, or 23.3% of the GDP. Viewed differently, that same amount represents nearly 38% of China's GDP of $17.79 trillion, according to the World Bank. And we're talking just about the creation of new data centers. Cost Considerations Of course, data centers are obviously but one piece of the networks needed to use, move and store data associated with AI-powered use cases and workloads. The power consumption and capital costs associated with the infrastructure AI demands also apply to the pipes that connect everything, from the wired infrastructure to the cellular and satellite networks that pull it all together and enable AI outputs to be used in real-world applications. All of those critical components of AI infrastructure require massive capital investments and expenditures. Subsea cable, for example, costs $40,000 per mile, or between $200 million and $250 million for a transatlantic connection. Despite the increased economy, launching satellites remains a costly endeavor, with outlays being highly variable and impacted by numerous factors, from the orbit itself—geostationary Earth orbit costs more than low Earth orbit—to the weight of the satellite, the launch vehicle involved and, of course, the cost of making the satellite itself. Questions also abound about how costs will evolve and how quickly in light of significant advancements in space delivery technologies. Cellular networks are no less complex and also require significant capital investments. For example, in just one deal with U.S. Cellular in 2024, Verizon confirmed earmarking $1 billion for the purchase of additional spectrum alone. The Legacy Problem And then there's the issue of legacy communications and industrial networks that remain in use across many industries. These systems will need to be upgraded to handle large AI workloads if commercial applications of AI are to progress as envisioned. This includes the supervisory control and data acquisition systems (SCADA) that remain prevalent in the utility sector and across industries like manufacturing, with many now two and three decades old. But the issue of legacy systems isn't confined to any one sector. Link 16, the U.S. military's most-used digital datalink, is now in its fourth decade of use. Focusing On The Data Clearly, given these factors, the costs of the modernization and expansion efforts AI demands aren't feasible as viewed currently, particularly in a time when public-sector and private-sector investments are contracting, not growing. Nor is it realistic to believe we collectively have the people and businesses needed to keep pace with AI's rapid expansion and building demands. That's why we must, now more than ever, be pragmatic not only about our response to the investment AI calls for but also the kinds of AI data we use and how we manage, store, share and use that information. It's time to focus on data itself. Does it need to be this big? Can we make it more efficient and reduce the strain on resources? Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
15-04-2025
- Business
- Forbes
19 Underrated Strategies To Gain A Competitive Edge As A Tech Startup
getty Early-stage tech startups often face steep competition and limited resources, so it's essential to find creative ways to stand out. While many founders focus on product features or fundraising, there are lesser-known strategies that can provide a powerful edge. Here, 19 members of Forbes Technology Council share underrated tactics that savvy startups can use to gain traction and build trust with their target audiences. From deeply engaging with early customers to partnering with established market players, these strategies can help any startup outmaneuver the competition. When I first advised startups, most saw security certifications as bureaucratic hurdles. In reality, they're trust accelerators that transform client perception. Enterprise clients fear security risks after seeing countless breaches. By pursuing ISO 27001 and SOC 2 certifications early, you speak their risk management language and gain a competitive edge others overlook. - Ben Ben Aderet , GRSee Consulting Dedicating time to deeply engage with early customers can create long-term loyalty and provide valuable feedback. This could involve offering exceptional customer support, having a regular feedback cadence about the product roadmap and developing a community. Strong relationships can create advocates who feel valued and heard, which leads to organic word-of-mouth growth. - Charles Yeomans , Atombeam Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify? Most often, a minimum viable product is better and cheaper than long-lasting, full-fledged market research. Ship fast and get real feedback from real users—they will help to tune your product strategy and navigate the market more accurately. - Andrius Buinovskis , NordLayer 4. Hire For Judgment, Not Just Skill Hiring people with good judgment is crucial to defining what is necessary for a system and balancing the many considerations and constraints that contribute to thoughtful software architecture. If you hire people without excellent taste, it's easy to get large, messy repositories due to AI's impact. You can write 1,000 lines easily, but is it the right code? That really hinges on judgment. - Mike Conover , Brightwave 5. Generate Revenue Before Product-Market Fit In the current tough fundraising environment, an underrated strategy for early-stage tech startups is to start generating revenue from day one through consulting, paid proofs of concept or other means based on the skills that the founders possess. Then, continue to do so until you find product-market fit. A famous successful example is Microsoft, which never needed to raise VC money. - Tokumasa Yamashita , Qlay Technologies 6. Prioritize Interoperability Over Feature Creep Early-stage tech startups gain an edge by prioritizing interoperability. Many focus on features, but businesses prefer seamless integration. Designing APIs for platforms like SAP or Salesforce reduces onboarding friction. A supply chain AI startup cut onboarding from six months to three weeks, outpacing feature-rich competitors. Prioritizing integration over feature bloat drives faster adoption. - Ashutosh Synghal , Midcentury Labs Inc. 7. Leverage Cross-Channel Marketing Never underestimate the power of cross-channel marketing and the impact it can have on your presence. Think five to seven touchpoints across different media. It doesn't need to cost an arm and a leg; it just needs to be consistent and compelling. Thought leadership is real, and it resonates when positioned in the right place at the right time. - Georgia Leybourne , Linnworks 8. Combine Customer Obsession With AI Agents An underrated strategy is combining deep customer obsession with agentic AI for real-time insights and competitive analysis. Startups can deploy AI agents to monitor customer behavior, gather feedback and analyze competitor offerings, allowing them to rapidly tailor solutions that meet evolving needs better than rivals. This dual focus builds loyalty while staying one step ahead in the market. - Sourav Sethia , Amazon 9. Partner With Established Market Players Partnering with firms that are established in your target market and have a solution that is complementary to your product is a great way to obtain early validation and credibility. It differentiates your offering by including your company in the ecosystem alongside other trusted providers. For customers, the benefit is lowering their risk while enabling them to benefit from innovation. - Zornitza Stefanova , BSPK 10. Build A Community That Markets For You A huge overlooked strategy is community building. When a startup can turn a community that likes them into one that loves them, every single member becomes a marketing agent. - Anthony Green , Chartered Professional Accountants of British Columbia (CPABC) 11. Solve Urgent Problems First Instead of focusing on the 'idealistic development' of your product and its market fit, find a few prospects in your ideal customer base with urgent 'hair on fire' problems. Then, solve these problems with consulting and product delivery. By doing this, you learn what the market actually needs, and you'll increase cash flow and build advocates for your business. - Keith Moore , 12. Stay Focused On Your Core Mission It becomes really difficult to gain a competitive edge if you don't have an identity and mission for your business. That can change and pivot over time, but you started your business for a reason and with a goal in mind. Focus on that, and don't try to be something you aren't. - Bill Bruno , Celebrus 13. Target Niche Communities For Loyalty Startups should leverage niche, community-driven product development. Instead of competing directly with industry giants, they can cultivate strong relationships with a small but highly engaged user base. Nothing, a UK-based startup, is already using this strategy by using its social media and YouTube channel to target tech enthusiasts and actively engage with them. - Melkon Hovhannisyan , Direlli 14. Build Your Technical MVP And Iterate Early-stage tech startups should recognize that, just like a product MVP, there's also a technical MVP. Build that first, gather customer feedback and iterate to refine. - Su Belagodu , ContextQA 15. Tap Into Advisory Talent Networks There is a rise in consultant and advisory talent in today's workforce. Early-stage startups can tap into this experienced group of leaders to advise and guide, many times for no upfront cash but rather advisory equity. - Ray Culver , CWsolutions Group 16. Use AI Strategically Early-stage startups underestimate the power of AI agents. A solopreneur today can rapidly scale by creatively leveraging accessible AI tools. With AI-driven efficiency, startups can pinpoint niche problems, swiftly build frictionless solutions and dramatically outperform established players. Thinking about building an AI agent or using one can be a game changer. - Yusuf Sar , Hardwarewartung 24 GmbH 17. Conduct Solid Market Research To break the entry barrier and find a competitive advantage, companies need a solid market research strategy to disrupt the market. One of the best I have seen is Orangetheory Fitness, which changed the fitness world for working professionals. - Hari Sonnenahalli , NDBS 18. Get Curious And Listen To Your Customers Listen! Tech startup leaders need to lean in with curiosity about the customers. They need to put aside what they think they know and engage with customers by asking questions with raw curiosity. Dig deep and invest in learning both about the user and the buyer (which are not always the same person). - Teri Thomas , Volpara Health 19. Lead Early Sales As The Founder In the early stages, founders need to focus on closing and generating revenue. Oftentimes, they are the best salespeople because they invented the technology. Scaling via a large sales force comes after you've closed the first 50 deals. - Vivek Bhaskaran , QuestionPro
Forbes
14-04-2025
- Business
- Forbes
Enterprise IT's Inflection Point: How IT And IoT Are Shaping A New Era
Charles Yeomans is the chairman and founder of Atombeam. Change is a constant in IT—change driven by new innovations, new use cases, new problems and new solutions. Over time, advancements from mainframe computers to the evolution of PCs to the rise of virtualization and the supremacy of the cloud—and even the move from desktops to mobile devices—fundamentally altered the very notion of enterprise IT. Most CIOs are accustomed to this ever-changing reality, with many having cut their teeth when businesses looked to IT to oversee internal systems and a corporate, on-premises data center. With the hub-and-spoke model, responsibilities increased. Then, the cloud changed everything. The cloud's secret weapon transformed enterprise IT. Its magical ability to automate previously weighty tasks—from adding compute power to spinning up additional storage capacity—was a game-changer. Everything didn't get easy, though. The distributed organizations the cloud helped make possible called for inherently more complex IT infrastructure. And it didn't take long for organizations to realize they still needed to keep some data under lock and key within their own walls. Organizations that previously looked to get out of the "data center business" altogether responded with hybrid approaches and single-tenet private clouds, even as many oversaw the building of new in-house data centers and the provisioning of the mission-critical systems within them. But even as many IT leaders again looked inward, the edge of the network continued to expand. This brings us to today, when enterprise networks are more complex than ever before, and IT teams—even those armed with greater visibility and control—have even more to do than ever before. And that is before even taking into account two truly transformative computing trends: AI and the burgeoning Internet of Things. With these technologies, we have entered a new era, one that will impact enterprise IT like never before. Enterprise IT teams today must oversee a highly distributed computing environment marked by unprecedented complexity. Not only is the edge of the network no longer bound by the constraints of a wired network, but the Internet of Everything is growing as satellite and mobile networks connect a dizzying array of devices, from autonomous vehicles and drones to appliances and sensors of every kind. Machine-generated data is increasing exponentially, with IoT Analytics estimating the existence of 18.8 billion connected IoT devices globally. And that is not even factoring in what is arguably the most transformative computing trend of our time: the use of generative AI, which has upended entire industries while simultaneously forcing everyone to consider its disruptive potential in less than three years of public use. Both trends pose immediate challenges to enterprise IT teams, who must account for, plan and address their significant impact on the most fundamental computing tasks, including: Heavy, data-intensive AI workloads are dramatically straining networks and using an unprecedented amount of power—a fact prompting hyperscalers to look to nuclear reactors to power their data centers. Notably, power consumption is particularly great in use cases that require low latency and near-real-time communications. AI workloads are also overwhelming the pipes, satellite and cellular networks that enterprise IT relies on. In contrast, the machine-generated data shared from sensors and other small devices that comprise the IoT is typically lightweight but often burdens infrastructure with the continual ping of shared information. In both cases, networks—including the data centers within them, the pipes that feed them and the connections between machines—are increasingly bogged down. The data deluge that made companies like EMC some of the most profitable in history ultimately drove many enterprises to the cloud, in part for its elasticity. Massive AI datasets upend that dynamic even in hyperscale environments, where data center operators are running out of capacity in their facilities and across their clouds. At the edge of the IoT, many endpoints—98% of them, according to Palo Alto Networks—are unprotected because devices like low-power sensors lack the computational power and memory encryption algorithms required, leaving bad actors with an open door. In light of these factors, the responsibilities that have for decades defined IT's estate and its core responsibilities—to provide sufficient compute power, networking speed and effectiveness, storage capacity and data protection—are increasingly untenable using traditional approaches and means. For years, we have turned to more powerful hardware to provide and manage the IT infrastructure needed to handle the ever-growing amount of data we create. Those efforts will, by necessity, continue as we work to effectively address the demands associated with AI workloads and the dramatic growth of machine-generated data. In response, leading innovators are creating faster chips and more powerful processors, even as quantum computing emerges. Simultaneously, a satellite network and data center building boom is underway, even as the aforementioned use of nuclear reactors reveals the very real concerns around how data centers will be powered. Time, however, may be the greatest challenge: In one projection for data growth, McKinsey sees demand potentially reaching 298 gigawatts by 2030, a reality it notes will require 'twice the data center capacity created since 2000 to be achieved in one quarter of the time.' Given this, one can effectively argue that no one approach or response will effectively address the very real reality that, with the growth of AI and machine-generated data, workloads have outpaced the infrastructure resources they require. We are at an inflection point that requires us to accept that the need for faster data processing and storage is not going away. For that reason, it is imperative that take a step back and think not only of the additional infrastructure capacity we can create, but also what we can do differently—from bringing information closer to the edge to changing the very notion of how data is configured and how it can be moved, stored and protected. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
