Latest news with #SamuelAltman
Forbes
4 days ago
- Business
- Forbes
Artificial Intelligence Collaboration and Indirect Regulatory Lag
WASHINGTON, DC - MAY 16: Samuel Altman, CEO of OpenAI, testifies before the Senate Judiciary ... More Subcommittee on Privacy, Technology, and the Law May 16, 2023 in Washington, DC. The committee held an oversight hearing to examine A.I., focusing on rules for artificial intelligence. (Photo by) Steve Jobs often downplayed his accomplishments by saying that 'creativity is just connecting things.' Regardless of whether this affects the way you understand his legacy, it is beyond the range of doubt that most innovation comes from interdisciplinary efforts. Everyone agrees that if AI is to exponentially increase collaboration across disciplines, the laws must not lag too far behind technology. The following explores how a less obvious interpretation of this phrase will help us do what Jobs explained was the logic behind his genius The Regulatory Lag What most people mean when they say that legislation and regulation have difficulty keeping pace with the rate of innovation because the innovation and its consequences are not well known until well after the product hits the market. While that is true, it only tells half of the story. Technological innovations also put more attenuated branches of the law under pressure to adjust. These are second-order, more indirect legal effects, where whole sets of laws—originally unrelated to the new technology—have to adapt to enable society to maximize the full potential of the innovation. One classic example comes from the time right after the Internet became mainstream. After digital communication and connectivity became widespread and expedited international communication and commercial relations, nations discovered that barriers to cross-border trade and investment were getting in the way. Barriers such as tariffs and outdated investment FDI partnership requirements—had to be lowered or eliminated if the Internet was to be an effective catalyst to global economic growth. Neoliberal Reforms When the internet emerged in the 1990s, much attention went to laws that directly regulated it—such as data privacy, digital speech, and cybersecurity. But some of the most important legal changes were not about the internet itself. They were about removing indirect legal barriers that stood in the way of its broader economic and social potential. Cross-border trade and investment rules, for instance, had to evolve. Tariffs on goods, restrictions on foreign ownership, and outdated service regulations had little to do with the internet as a technology, but everything to do with whether global e-commerce, remote work, and digital entrepreneurship could flourish. These indirect legal constraints were largely overlooked in early internet governance debates, yet their reform was essential to unleashing the internet's full power. Artificial Intelligence and Indirect Barriers A comparable story is starting to unfold with artificial intelligence. While much of the focus when talking about law and AI has been given to algorithmic accountability and data privacy, there is also an opportunity for a larger societal return from AI in its ability to reduce barriers between disciplines. AI is increasing the viability of interdisciplinary work because it can synthesize, translate, and apply knowledge across domains in ways that make cross-field collaboration more essential. Already we are seeing marriages of law and computer science, medicine and machine learning, environmental modeling, and language processing. AI is a general-purpose technology that rewards those who are capable of marrying insights across disciplines. In that sense, the AI era is also the era of interdisciplinary boundary-blurring opportunities triggered by AI are up against legal barriers to entry across disciplines and professions. In many professions, it requires learning a patchwork of licensure regimes and intractable definitions of domain knowledge to gain the right to practice or contribute constructively. While some of these regulations are generally intended to protect public interests, they can also hinder innovation and prevent new interdisciplinary practices from gaining traction. To achieve the full potential of AI-enabled collaboration, many of these legal barriers need to be eliminated—or at least reimagined. We are starting to see some positive movements. For example, a few states are starting to grant nurse practitioners and physician assistants greater autonomy in clinical decision-making, and that's a step toward cross-disciplinary collaboration of healthcare and AI diagnostics. For now, this is a move in the right direction. However, In some other fields, the professional rules of engagement support silos. This must change if we're going to be serious about enabling AI to help us crack complex, interdependent problems. Legislators and regulators cannot focus exclusively on the bark that protects the tree of change, they must also focus on the hidden network of roots that that quietly nourish and sustain it.
Forbes
09-04-2025
- Business
- Forbes
The Rise Of Decentralization: 'Bitcoin Meets Artificial Intelligence'
Samuel Altman, CEO of privately owned artificial intelligence company, OpenAI (Photo by Win McNamee) As artificial intelligence becomes increasingly central to the global digital economy, decentralized alternatives are beginning to challenge the dominance of corporate-led AI development. Among the most promising is Bittensor, a blockchain-based machine learning network that allows anyone to contribute to and profit from AI innovation. As reported by Coinbase, the network offers an estimated annual reward rate of 17.52%, making it a cutting-edge technological platform and an attractive economic opportunity. Bittensor functions as both a blockchain and a peer-to-peer AI platform, enabling the creation, evaluation, and monetization of digital commodities such as AI inference, training, and storage. A core innovation of Bittensor is its subnet architecture. Modular, independent communities within the network that focus on specific digital commodities. Each subnet operates autonomously, establishing its unique incentive structures and performance benchmarks. Contributors, known as miners, compete to solve AI-related tasks ranging from translation to large language model inference. Their outputs are evaluated by validators, who rank the quality of each submission. Seth Bloomberg, an investment partner at Crucible Labs, told Forbes, 'Crucible acts as a validator—not a subnet—unlike inference-focused subnets such as Manifold or Chutes.' While subnets ensure data accuracy and model performance, Bloomberg emphasized that harmful or illicit content must be moderated at the application layer. He also noted that there is 'no inherent advantage to being an early subnet,' especially following the Dynamic TAO upgrade, which allows the open market to determine the value of each subnet. Previously, all rewards were distributed exclusively in TAO tokens. With the introduction of Dynamic TAO, each subnet now has its unique token, which miners receive as a reward. Bloomberg says that today, Bittensor hosts over 80 subnets, from experimental projects refining their incentive mechanisms to mature systems actively generating revenue and serving end users. This model redefines what it means to 'mine' in the blockchain context. Rather than validating blocks through cryptographic puzzles as seen in Bitcoin, miners in Bittensor produce tangible, valuable digital work. The economic incentives are substantial. With TAO emitted daily, participants, which include miners, validators, and subnet creators, can earn real income based on their contributions. Participation is merit-based. Underperformers may be de-registered and required to re-enter the system. This ensures that performance, not capital or central authority, dictates success. According to Bittensor's whitepaper, the system includes strong safeguards against collusion and manipulation. Rewards are distributed based on consensus-based rankings. This rewards only peers whose evaluations align with the broader network. As decentralized AI gains momentum, Bittensor stands out for its transparency, openness, and potential to democratize access to machine learning. In contrast to centralized platforms like OpenAI or Google, where access is gated, and development occurs behind closed doors, Bittensor enables global collaboration through an open-source protocol. With growing concerns about centralized AI's ethical and regulatory pitfalls, ranging from data privacy to monopolistic control, Bittensor's model offers a compelling alternative. As the protocol continues to evolve, it promises to scale intelligence in a decentralized manner and ensures that the ownership and benefits of AI remain in the hands of the many, not the few.
