In a recent exploration of the shifting digital landscape, Brian Feinberg, founder and CTO of the Tomorrow Company, shared insights into the transformative power of artificial intelligence (AI) and Web3 technologies. We are entering what Feinberg describes as an “agentic era,” where the focus is shifting from simple machine obedience to complex execution and trustless global interaction.
The convergence of AI and Web3 represents more than just another technological trend. It marks a structural transformation in how humans interact with software, how businesses operate, and how digital trust is established. For years, technology has focused primarily on improving communication and access to information. However, the next phase appears to center on autonomous systems capable of executing tasks, coordinating workflows, and operating across decentralized digital ecosystems.
This emerging era introduces new possibilities for productivity, financial systems, governance, and global collaboration. At the same time, it raises important questions about trust, security, identity, and the future role of human creativity in increasingly automated environments.
Beyond Obedience: The AI of Execution
A central theme of the discussion was the evolving nature of AI’s “understanding”. Feinberg argues that modern technology has reached a point where it doesn’t just process data but understands the conviction of an argument.
For decades, software systems operated strictly on predefined instructions. Traditional systems could process commands efficiently, but they lacked contextual reasoning and adaptability. Modern AI systems are beginning to bridge that gap by interpreting intent, recognizing patterns, and generating responses that simulate reasoning and strategic thinking.
This transition fundamentally changes the role of technology in business and society. Instead of acting solely as tools that assist humans, AI systems are increasingly becoming operational collaborators capable of handling complex workflows.
- Human-Machine Intertwining: As we work more closely with AI, our personalities and the machine’s outputs become increasingly intertwined.
This growing collaboration between humans and machines is already visible across industries. Writers use AI-assisted drafting tools, developers rely on AI-generated code suggestions, designers leverage generative image systems, and businesses automate customer interactions through intelligent systems.
As these interactions become more frequent, the distinction between human-generated and machine-assisted work becomes less clear. AI systems begin learning communication styles, strategic preferences, and behavioral patterns from their users. In many ways, these systems become extensions of human intent.
This creates both opportunity and responsibility. The quality of AI outputs increasingly depends on the quality of human guidance, oversight, and ethical considerations embedded within these systems.
- A Shift Toward Execution: While we often expect AI to be merely conversational or obedient, its true value lies in its ability to execute tasks.
The next generation of AI systems may not simply answer questions but actively complete objectives.
For example, AI agents are already being developed that can schedule meetings, conduct market research, write software code, analyze contracts, generate reports, manage workflows, and even coordinate multiple systems simultaneously. In enterprise environments, this could significantly reduce operational inefficiencies and accelerate innovation cycles.
The concept of “agentic AI” reflects this transition from passive assistance to active execution. Rather than requiring constant human instruction, these systems may eventually operate semi-autonomously while still aligning with broader human-defined goals.
This shift could redefine productivity at both the individual and organizational level.
- Focusing on Higher Meaning: By offloading execution to machines, humans gain more time to focus on “bigger things,” such as the meaning and purpose of life.
Historically, technological advancement has often reduced the amount of manual labor required from humans. Industrial machines transformed manufacturing. Computers transformed administrative work. AI may now transform cognitive labor itself.
If repetitive operational tasks become increasingly automated, humans may have greater opportunities to focus on creativity, innovation, strategy, emotional intelligence, and philosophical inquiry. The future workplace may place higher value on uniquely human capabilities such as empathy, leadership, ethics, and visionary thinking.
However, this transformation also requires society to rethink education, workforce development, and economic structures to ensure that automation benefits people broadly rather than concentrating advantages among a small group of organizations.
The Rise of the “Dark Software Factory”
The Tomorrow Company serves as a blueprint for the future of productivity, operating as what Feinberg calls a “phase three to phase four dark software factory”.
The term “dark software factory” refers to highly automated development environments where AI systems handle large portions of the software lifecycle with minimal direct human involvement. Similar to the concept of “dark factories” in manufacturing — factories capable of operating with little or no human presence — software development may be moving toward highly autonomous production systems.
- Massive Automation: The company sees between 20 to 40 merges of new code daily without any human involvement.
This level of automation demonstrates how quickly software engineering practices are evolving. AI-assisted development tools can already generate code, identify vulnerabilities, run tests, optimize infrastructure, and perform documentation tasks.
As these capabilities improve, development cycles become dramatically faster. Organizations can iterate rapidly, reduce costs, and bring products to market at unprecedented speed.
The implications extend far beyond software engineering. Similar automation patterns may emerge in finance, legal operations, healthcare administration, logistics, marketing, and research.
- Streamlined Productivity: This level of automation allows a handful of people to achieve what traditionally required organizations of 20 to 50 employees.
Small, highly skilled teams empowered by AI may become one of the defining business models of the next decade.
Instead of scaling primarily through headcount, organizations may scale through intelligent systems and automated infrastructure. This could fundamentally alter startup economics, operational efficiency, and competitive dynamics across industries.
Companies that successfully integrate AI into their workflows may gain significant advantages in speed, adaptability, and resource allocation.
At the same time, businesses must carefully consider governance, accountability, and quality assurance when relying heavily on autonomous systems.
- Rapid Development Cycles: By leveraging AI to assist in everything from conception to security audits and user testing, entire project lifecycles—including regulatory infrastructure for token launches—can be completed in just 30 days.
Traditionally, launching complex technology infrastructure could require months or even years of planning, coordination, testing, and deployment.
AI-assisted systems compress these timelines dramatically by accelerating ideation, coding, compliance reviews, testing, and deployment processes simultaneously.
This acceleration may reshape innovation cycles globally. Industries that once moved slowly due to technical or regulatory complexity may experience rapid experimentation and iteration.
However, faster execution also increases the importance of robust oversight and security frameworks to prevent vulnerabilities, misuse, or unintended consequences.
Web3 and the Democratization of Capital
The Tomorrow Company is heavily involved in the Real World Asset (RWA) space, specifically focusing on the tokenization of carbon credits.
The tokenization of real-world assets is emerging as one of the most discussed applications of blockchain technology. By converting physical or regulated assets into digital tokens, organizations can improve liquidity, accessibility, transparency, and transferability.
This concept has implications far beyond carbon credits. Real estate, commodities, intellectual property, infrastructure projects, and financial instruments may all become more globally accessible through tokenization frameworks.
- Unlocking Liquidity: Through a merger with Carbon Distributed Technologies, they are using blockchain to unlock capital liquidity and drive new ways of distributing wealth globally.
One of blockchain’s most significant advantages is its ability to reduce friction in financial systems.
Traditional markets often involve intermediaries, geographic limitations, lengthy settlement processes, and restricted access. Blockchain infrastructure enables more direct, transparent, and programmable transactions.
In theory, this could allow smaller participants around the world to access opportunities previously limited to large institutions or wealthy investors.
Tokenization may also improve the efficiency of global capital allocation by enabling fractional ownership and real-time settlement systems.
- Transparent Marketplaces: The goal is to create efficient, transparent marketplaces for carbon credits, which have historically suffered from a lack of innovation.
Carbon credit markets have long faced criticism regarding transparency, verification, and efficiency.
Blockchain-based systems may help address these challenges by providing immutable transaction records, traceable asset histories, and programmable compliance frameworks.
Transparent infrastructure could improve trust among buyers, regulators, and environmental organizations while enabling more efficient tracking of sustainability initiatives.
As climate concerns continue to shape global policy and investment strategies, digital infrastructure supporting environmental accountability may become increasingly valuable.
- Impact-Based Value: Feinberg believes future company value will be measured not just by traditional earnings (PE value) but by impact and regulatory compliance, particularly regarding carbon emissions.
The definition of corporate value may continue evolving beyond purely financial metrics.
Investors, regulators, and consumers are increasingly evaluating companies based on sustainability, governance practices, environmental impact, and long-term resilience.
AI and blockchain technologies may provide the infrastructure necessary to measure and verify these metrics more accurately and transparently.
In the future, organizations that successfully combine profitability with measurable positive impact may hold stronger competitive positioning and public trust.
Securing the “Agentic Era”
As we move toward an internet dominated by bots and AI agents—a concept often referred to as the “dead internet theory”—establishing trust becomes paramount.
The rapid growth of automated systems introduces new concerns around authenticity, misinformation, digital identity, and cybersecurity.
As AI-generated content becomes increasingly sophisticated, distinguishing between genuine and synthetic interactions may become more difficult. This creates a growing need for secure frameworks that verify trust without relying solely on centralized authorities.
- Trustless Environments: The focus is on building trustless environments where users don’t need to inherently trust the entity on the other end, but can rely on the security of the framework.
Trustless systems are one of the foundational concepts behind blockchain technology.
Rather than depending entirely on institutions or intermediaries, blockchain networks rely on cryptographic verification, distributed consensus, and transparent protocols.
This approach may become increasingly important in a world where AI systems can autonomously transact, negotiate, and communicate at scale.
Secure infrastructure may become the foundation that enables safe interaction between humans, organizations, and intelligent digital agents.
- A Secure Web3 Browser: Feinberg’s team has spent five years developing a Web3 browser and network designed to allow users to securely connect with applications through a trusted interface.
User experience remains one of the largest barriers to mainstream Web3 adoption.
Many blockchain applications remain too technically complex for average users. Wallet management, private keys, transaction approvals, and network interactions can create friction and confusion.
Secure and intuitive interfaces may play a critical role in enabling broader adoption. If blockchain systems become easier to use while maintaining security and decentralization, they may transition from niche technologies into mainstream infrastructure.
- Authentic Global Engagement: By using AI to translate and index content across multiple languages, organizations can engage authentically with the 85% of the world that does not think in English, driving organic growth without traditional advertising.
Language remains one of the internet’s largest barriers.
AI-powered translation systems are rapidly improving the ability of organizations to communicate across cultures and regions. Businesses can now localize content, support multilingual communities, and build global audiences more efficiently than ever before.
This shift may decentralize digital influence and create opportunities for broader participation in global innovation ecosystems.
Organizations capable of building authentic, multilingual engagement strategies may gain substantial long-term advantages in global markets.
The Five-Year Horizon: Seamless Adoption
Looking ahead, Feinberg anticipates real commercial adoption of blockchain, particularly in the RWA marketplace, over the next five years.
While blockchain technology has existed for more than a decade, mainstream adoption has often been slowed by complexity, scalability limitations, unclear regulation, and poor user experiences.
However, infrastructure improvements, regulatory clarity, and AI-enhanced interfaces may accelerate adoption significantly in the coming years.
For this to happen, the technology must become invisible to the end-user. Developers must strive to make blockchain interactions completely seamless—”one-click” environments where users feel protected and can transact easily without needing to manage complex crypto wallets.
Historically, transformative technologies succeed when users no longer need to think about the underlying infrastructure.
Most internet users do not understand how TCP/IP networking works. Smartphone users rarely think about cloud architecture or semiconductor design. In the same way, blockchain may only achieve mass adoption once its complexity disappears behind intuitive interfaces.
The future of AI and Web3 may therefore depend less on technological capability and more on user-centered design, trust, security, and accessibility.
As AI systems become more autonomous and blockchain systems become more integrated into global commerce, the convergence of these technologies could redefine digital interaction itself.
The coming years may determine whether this “agentic era” leads to greater empowerment, efficiency, and innovation — or introduces new challenges related to governance, trust, and human identity in increasingly automated environments.
Regardless of the outcome, the intersection of AI and Web3 is likely to remain one of the most influential technological developments of the next decade.
