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Forbes
11 hours ago
- Business
- Forbes
How Business Leaders Can Leverage Blockchain And AI To Unlock New Opportunities
Daniel A. Keller, CEO and President of InFlux Technologies Limited and Flux. Blockchain and AI are increasingly becoming more integrated—the duo can work symbiotically to bolster one another. At its core, blockchain provides a decentralized, consensus-based infrastructure that enables AI solutions to operate without third parties controlling the data and algorithms. There's the privacy element as well. Blockchain can help companies address data privacy issues inherent in AI solutions that run on centralized Web2 platforms. Both of these technologies are continuously evolving. However, business leaders should embrace them sooner rather than later to avoid falling behind. The Key Business Benefits Of Using Blockchain And AI In Tandem Why should leaders embrace blockchain and AI sooner rather than later? Consider the benefits that both technologies can offer companies when used in tandem. Blockchain gives businesses more control and ownership over their data. Third-party platforms—cloud providers, social networks, etc.—can be fickle. Overnight, a third-party platform could change the rules of engagement, such as by raising costs or adding new content restrictions, that make it difficult, if not impossible, for companies to control their costs, maintain their operations and share their narratives. Blockchain breaks that grip of control from third parties. With blockchain, leaders can create cost-effective infrastructure that runs on their terms. As for AI, it can help companies streamline their operations, pinpoint issues in real time and personalize customer service, to name a few of the many use cases. However, AI comes with various risks, namely, data privacy issues and concerns about centralized data control and training when using publicly available platforms. In certain industries, such as healthcare and finance, the consequences that can stem from those risks are magnified. By using the decentralized, open-source infrastructure and consensus mechanisms that blockchain provides, leaders can more effectively safeguard their data—both at the input and output stages. Best Practices For Implementing Blockchain And AI Together Business leaders should adopt blockchain and AI before these technologies mature. The more they delay adoption, the further behind they risk falling. To effectively leverage both technologies, leaders should start by identifying how blockchain and AI can serve their business needs. They should focus on their strategic vision for the next six months to a year and then evaluate where blockchain and AI can fit in. Short iterations are vital, given how quickly both technologies are evolving; long planning cycles could render them obsolete before implementation. Once leaders have identified their strategic vision for the next six months to a year, they can research vendors and find one that aligns with their business needs. From there, they proceed to the implementation stage. There's room for flexibility here. Leaders shouldn't go all-in on adopting both technologies at once. In most cases, an incremental, scalable approach to implementing blockchain and AI will be more manageable. For instance, the executives of a local consulting firm might opt to stay in Web2 and keep 50% of their company's infrastructure there. It could move the other half of its infrastructure to Web3 and then gradually start migrating customers there. On that decentralized infrastructure, it could begin running AI tools that refine certain processes, such as client scheduling and communication. Over time, the consulting firm can move more of its infrastructure to Web3, increase the number of AI tools it runs and shift more customers. Following implementation, leaders should remain proactive in keeping their systems current. Blockchain and AI are rapidly changing, and by staying informed about those changes, leaders can pinpoint how they factor into their business needs. Risks—And How Business Leaders Can Navigate Them Business leaders should be aware that adopting blockchain and AI comes with risks. For instance, aside from technical complexity, another prominent risk is that both operate in an uncertain regulatory environment. Consider recent regulatory activity in the United States. According to TheStreet, on May 29, 2025, lawmakers 'introduced the Digital Asset Market Clarity (CLARITY) Act—a bill designed to finally bring clear regulations to the crypto and digital asset industry.' A June 3, 2025, StateScoop article noted that 'A bipartisan coalition of more than 260 state legislators from all 50 states on Tuesday sent a letter to Congress opposing a provision in the federal budget reconciliation bill that would impose a 10-year ban on state and local regulation of artificial intelligence.' The outcomes of these regulatory activities can have serious ramifications for businesses implementing blockchain and AI, making it paramount for leaders to stay informed about developments on the policy side. A new law could render a company's adoption of blockchain and AI noncompliant, requiring a costly overhaul to get back on track. Another significant risk is workforce disruptions. When a company switches to Web3 and starts implementing AI, its existing workforce will likely be restructured or cut. Leaders must carefully consider the potential workforce disruptions that may arise from leveraging blockchain and AI. However, now is the time for leaders to explore blockchain and AI. Acting proactively, rather than reactively, gives leaders the best chance at mitigating risks, leveraging blockchain and AI symbiotically to drive business results and staying ahead of their competitors. Ultimately, it's by embracing open-source, decentralized platforms and AI solutions that leaders can safeguard their costs, operations and narratives. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
07-05-2025
- Business
- Forbes
DeepSeek: Smarter Software Vs. More Compute
Daniel A. Keller, CEO and President of InFlux Technologies Limited. Cofounder of Flux. Getty Images When ChatGPT was released by OpenAI in 2022, it was the peak expression of AI chatbots built on large language models (LLMs). With an accessible interface and absolutely no need for external gadgets, it was the power of interactive AI in the palms of users, literally! Barely five days after its launch, ChatGPT broke the 1 million download milestone. (For context, that took Facebook 10 months to achieve.) Of course, there were a few problems, like the occasional lags and hallucinations, but version after version, ChatGPT continued to expand its frontiers. There were also apprehensions about the development cost of ChatGPT-4, somewhere between $48 to $71 million. But it was all completely justifiable. Sixteen thousand H100s GPUs don't come cheap, and salaries have to be paid. Or was it? Rise Of The Deep On January 20, 2025, the world woke up to news that would change the trajectory of AI technology. A little-known Chinese company had launched DeepSeek R1, an AI with capabilities comparable to OpenAI's ChatGPT. And the shocker? The initial reports claimed it did it with fewer, cheaper and older GPUs at a development cost of only $5.6 million. The ripple effect sent shock waves across the markets. By Monday, Nvidia, the biggest supplier of AI GPU chips, lost almost $600 billion in market value as investors started reconsidering their options. Indexes and corporations like Nasdaq, Microsoft and Alphabet also plummeted. Within a week, Deepseek had overtaken ChatGPT to become the most downloaded application on the Apple App Store. But since then, DeepSeek has come under scrutiny, with the head of Google's DeepMind calling its claims "exaggerated" and one critic suggesting it actually cost DeepSeek over $1 billion to create its AI model. Nevertheless, DeepSeek's arrival has caused a shift. The investment rationale for the supply chain had been quite simple: more spending and better outcomes for AI. Until now. The Paradigm Shift Deepseek's story is exceptional for several reasons. First, due to the United States' efforts to stem the flow of advanced AI technology to competing nations, the Biden administration restricted the export of GPUs to China, limiting the availability of advanced AI GPUs like the A100s and the H100s. As a result, Deepseek presumably had to rely on less sophisticated but more available GPUs like the H800. The ability of Deepseek to turn this crippling limitation into one of the marvels of AI innovation highlights a very critical question: Is ingenuity and better software architecture a more sustainable alternative to advanced but expensive GPUs? GPU availability (significantly advanced chips like the H100s) is one of the rate-limiting steps for AI research and development; even in the U.S., Nvidia, the top producer of GPUs globally, continues to grapple with meeting its high demand. A breakthrough that demonstrates that companies and research labs can maximize their computing power and cut down costs is a game-changer for the entire industry, but how exactly did DeepSeek achieve this? Flipping The Game Before Deepseek's emergence in AI, it had always been a game of who was bigger. Bigger financial investments translate into bigger LLM Models, which in turn require more compute resources and, hopefully, bigger innovative strides. However, DeepSeek's approach was counterintuitive. Instead of slapping on more compute and developing bigger models, the Chinese company focused on optimizing for a more efficient use of available resources. This included enhancing its model abilities through reinforcement learning, leveraging improved software architecture and optimizing its algorithm. Rather than dwarfing prevailing challenges with sheer brute power, Deepseek turned the game on its head. Early benchmarks showed it was 20 times more efficient and far less compute-intensive than its more pronounced competitors. Since it relied on reinforcement learning, Deepseek-R1 also eliminated the need for large teams of human reviewers and supervised fine-tuning, keeping operating costs to a minimum. Another important paradigm that Deepseek adopted was its incorporation of MOE (mixture of experts) architecture. MOE leverages multiple expert sub-models and uses selective gating to activate only the most relevant parameters for each input. For context, the Deepseek MoE framework comprises around 671 billion parameters; however, less than 0.5% of these parameters are used during any input. Picture a diverse team of seasoned experts across different disciplines. When needed, the gating mechanism dynamically selects the best combination of experts to solve the problem. The result? Dynamic routing and allocation lowers the amount of computation the model requires by reducing unnecessary computation. This approach also improves efficiency, promotes seamless scalability and supports progressive fine-tuning of different expert system components for specific problems. Implications For The Broader AI Industry Compute-efficient AI solutions encourage democratization, allowing for dynamic innovations from different quarters. This could, in turn, promote cheaper access to AI resources, breaking Big Tech's monopoly on AI innovation. Deepseek's open-source nature provides a level playing field for researchers to engage in deep R&D without breaking the bank. Its lower energy requirements and smaller carbon footprint can also positively drive environmentally sustainable designs for data centers in the near future. However, as revolutionary as the emergence of Deepseek has been, there are also a few drawbacks (on top of the dubiousness of its claims). First, while DeepSeek's open-source nature encourages technology sharing and participation, it also means malicious actors can repurpose it, raising fresh concerns about heightened misinformation, deepfakes and other sinister possibilities. Another danger hinges on data sovereignty and the possibility of the Chinese government mining users' data. Rounding Off While DeepSeek has demonstrated capabilities that are comparable to OpenAI ChatGPT in many ways, its long-term effect on repositioning AI technology, compute and market dynamics still remains to be seen. Whatever the future might hold, Deepseek's successful deployment of a powerful open-source model has introduced a new level playing field for innovation in the AI industry. As this distills into the mainstream, its ripple effect could determine the face of the next iteration of artificial intelligence. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
