The Next AI Goldmine: Profits Beyond Big Tech

The next great transfer of wealth in tech is already underway. The AI winner-take-all game has gone into overdrive.

While Big Tech spent $364 billion in 2024, decentralized AI startups raised just $436 million—triple the year before. Yet that’s only 0.12% of Big Tech’s outlay, according to PitchBook. The imbalance shows how early these challengers remain.

A handful of giants dominate the AI stack, from the data centers that train models to the platforms that deploy them. Everyone else gets scraps. Independent researchers publish breakthrough papers that fuel billion-dollar models but see no upside. Open-source developers contribute critical code, only to watch it vanish into proprietary systems. And armies of annotators spend months labeling training sets that form the backbone of ChatGPT, Gemini, Claude, and others, paid once by contract but excluded from the billions in ongoing value their work creates.

The economics are unsustainable. When a $2.5 trillion market funnels nearly all rewards to the top, innovation slows, talent gravitates to the giants, and smaller developers get priced out. That frustration is fueling entrepreneurs building alternatives that let professionals and institutions capture value from what they are currently giving away for free. Crucially, blockchain infrastructure has matured to the point where attribution and compensation mechanisms, once theoretical, are now technically feasible.

If markets won’t rebalance rewards, lawsuits may force the issue.

The Legal Avalanche

The legal assault is intensifying. On September 9, 2025, a federal judge refused to grant preliminary approval for Anthropic’s record-breaking $1.5 billion settlement with 500,000 authors, sending the case back to trial. The New York Times’ lawsuit against OpenAI and Microsoft cleared a major hurdle in March. Eight major newspaper publishers are now demanding billions for “purloining millions” of articles without permission.

Instead of retreating, Microsoft, Google, Amazon, and Meta are doubling down, investing $320 billion into new data centers to entrench their lead. Legal outcomes remain uncertain, and Big Tech has a long history of adapting quickly to new rules in ways that protect its advantage.

What’s at stake isn’t the models; it’s whether companies can freely use copyrighted content to train them. Defenders point to the fair use doctrine, which permits certain limited uses of copyrighted material without permission. But fair use doctrine is a defense, not a blanket right, and whether mass training of AI models qualifies is now being tested in court.

That’s where blockchain challengers step in, attempting to solve attribution at the root. This tension is most evident in the infrastructure layer, where models are trained, datasets refined, and GPUs burn electricity to generate intelligence. Today, that work happens behind the closed doors of a handful of tech giants. Now, a new generation of startups is building direct alternatives. For example, Trie network treats AI models, datasets, and compute power as tradeable digital assets on its Rubix-based marketplace. The platform logs every development step on-chain: from dataset labeling to model fine-tuning, creating transparent attribution trails. At the Dallas AI Summer Hackathon, participants published models with verifiable provenance, demonstrating blockchain-based AI transparency.

But achieving that user-centric vision requires transparency at every level of AI development. Behind every AI breakthrough are the MLOps and DataOps workflows that orchestrate training runs and data pipelines. Today, they remain opaque and unverifiable, forcing blind trust in systems that shape critical decisions. The researchers, developers, and content creators behind them receive no attribution or compensation.

The appeal goes beyond attribution. “I don’t think anyone wants to live in a world where all our digital lives are controlled by a handful of mega tech corporations that have full access to all our most intimate data,” says Hoolie Tejwani, head of ventures at Coinbase. “Applying decentralized systems, AI can be delivered in a way that’s more bottom-up, more grassroots, more user-centric.”

But turning that vision into reality won’t be simple. The path is riddled with engineering challenges as daunting as training GPT itself. Building attribution systems that can track contributions across complex AI pipelines without creating privacy nightmares or computational bottlenecks is an engineering challenge in its own right. And even if the tech works perfectly, there’s still the network effect problem: why would anyone join a decentralized system when all the users, data, and talent are already locked into Big Tech’s walled gardens?

Then there’s the regulatory wild card. As governments worldwide scramble to regulate AI, new compliance requirements could either accelerate the shift toward transparent, auditable systems or create barriers so complex that only the giants can navigate them.

While Big Tech writes settlement checks, blockchain-based systems attempt to flip the script: contributors are credited and compensated from day one.

The Two-Layer Shift: Infrastructure and Execution

The redistribution story doesn’t stop at training data. The next battleground is the execution layer, where the question becomes not just who builds the models, but who profits when AI stops talking and starts acting for users. AI has become powerful at generating content and insights, but it still stops short of acting on them. Useful, but passive. The next leap, what many call agentic AI, shifts from recommendations to actions.

Consider the possibilities: telling an AI, “Rebalance my portfolio into 40% ETH, 30% BTC, and the rest stablecoins.” Instead of suggesting steps, the system executes trades in real time, sets stop-loss orders, and updates allocations autonomously.

In traditional finance, finding yield is simple: you compare CDs at a few banks, pick the best rate, and trust that your deposit is FDIC-insured. DeFi flips that logic on its head. Instead of a handful of safe, predictable products, it offers a sprawling menu of yield strategies: staking tokens, lending and borrowing, providing liquidity, yield farming, plus complex auto-compounding schemes. The upside is enticing, but every option comes with its own quirks, risks, and moving parts. You’re not just comparing interest rates; you’re auditing smart contracts, weighing risks like impermanent loss or shifting governance rules.

Agentic AI could turn this maze into a single command. Imagine saying: “Find me a 6% yield opportunity using only audited protocols with over $100 million in total value locked.” The system would scan dozens of options, verify audits, check liquidity depth, assess performance, then execute allocations and monitor positions while documenting each decision. Of course, autonomous financial agents raise regulatory and liability questions that the industry is still working through.

An example: GT Protocol positions itself as middleware that bridges users and Web3 protocols through autonomous AI agents. Its AI Executive Technology turns complex crypto interactions into conversational commands: handling algorithmic trading, portfolio rebalancing, and DeFi yield farming. With modular SDKs and APIs, GT Protocol enables other platforms to deploy AI-driven execution logic for what it calls “Web4” infrastructure. The company reports early adoption among development partners.

While such platforms remain early-stage, they illustrate how the execution layer could evolve: from passive assistants to active agents capable of navigating Web3’s complexity on behalf of ordinary users.

Together, the infrastructure and execution layers frame a broader vision: a more democratic flow of economic value. Not pyramids where rewards concentrate at the peak or among a few specialized skills, but networks where attribution, accountability, and profit-sharing are shared more broadly.

Where the Money Will Be Made

The next wave of fortunes won’t come from building another ChatGPT. It will come from unlocking the specialized data trapped in proprietary systems: data so valuable that, once aggregated and attributed, it could generate trillions in new wealth. Across industries, vast datasets sit underused inside corporate silos. Even the companies that collect them rarely extract full value, lacking the advanced AI tools and analytics now being developed. Imagine a radiologist’s rare cancer case in Mumbai improving detection accuracy globally while generating ongoing revenue for the contributor. That’s the scale of redistribution we’re talking about.

Healthcare: $187 Billion Opportunity

AI in healthcare is projected to surge from $26.6 billion in 2024 to $187.7 billion by 2030, a 38.6% CAGR (Grand View Research). It also generates 30% of the world’s data, growing at 36% annually, faster than any other sector (RBC Capital Markets).

Yet most of this knowledge disappears into corporate databases. Radiologist scans, rural clinic diagnoses, logged insights—all vanish once recorded. The opportunity is to build platforms where every scan, every diagnosis, every contribution compounds global accuracy and pays dividends to the people and institutions behind it. The prize is exponential accuracy that grows with every data point. Yet the maze of HIPAA compliance, FDA approvals, and patient consent could delay progress for years.

Energy: $150 Billion Efficiency Play

The global smart grid market is projected to exceed $150 billion by 2030 (Precedence Research). Even larger is the value hidden in the petabytes of energy data inside proprietary systems, from oil rigs, solar farms, and IoT meters, that rarely cross company firewalls.

Every well log, turbine reading, and weather-linked consumption forecast has predictive value. Today, it mostly benefits the companies that hoard it. Attribution could flip that equation, creating revenue streams for operators who feed data into collective intelligence models that optimize grids, cut waste, and prevent blackouts. Contributing a data point could become as routine as selling a kilowatt-hour: tracked and monetized in real time.

Pharma: $100 Billion Data Dividend

Drug discovery costs are enormous, with global R&D spending exceeding $300 billion in 2023. Yet 90% of clinical trial data goes unpublished once studies end, leaving billions in value untapped.

Attribution could change that. Imagine a platform where every molecule test, failed experiment, and patient response is logged, attributed, and monetized when it informs future breakthroughs. What looks like dead-end research today could become a building block for tomorrow’s cure. Knowledge would compound, cutting billions in wasted experiments and accelerating pipelines worth hundreds of billions.

Finance: $200 Billion Transparency Market

Banks already spend $270 billion annually on compliance, and those costs continue to rise. Under the EU AI Act, compliance costs can run €52,000 per AI system per year.

The problem is duplication: each bank builds in isolation, hoarding risk and fraud data. Attribution infrastructure could allow them to pool anonymized insights while retaining competitive advantage, unlocking billions currently burned in duplicated compliance. The player that cracks transparent, attributable AI doesn’t just save money; it becomes the infrastructure provider for the entire industry. But convincing risk-averse regulators to approve data-sharing, even anonymized, will be a major hurdle.

The Prize

The biggest prize isn’t tied to one sector. It’s the meta-layer: building attribution infrastructure that transforms siloed, underutilized data into compounding value across industries while ensuring contributors share in the upside.

The New Gold Rush

Remember that $364 billion Big Tech spent in 2024, while decentralized AI raised just $436 million? That gap isn’t a sign of weakness; it’s a sign of how early we are. The next great transfer of wealth won’t come from building another chatbot. It will come from cracking the code on attribution itself.

The winners won’t be the companies that hoard the most data or train the biggest models. Rather, they’ll be the ones who build the infrastructure that finally lets anyone with specialized knowledge capture value from what they’re currently giving away for free.

The challenges are enormous, the hurdles daunting, the giants advantaged—but that’s what makes the opportunity so electric.

When breakthrough cancer research in Mumbai starts generating dividends for its contributors while improving treatment protocols in São Paulo, when wind-farm operators in Texas earn ongoing revenue from grid optimization algorithms deployed in Germany—that’s when we’ll know the great AI wealth redistribution has truly begun.

The infrastructure to make this happen is being built right now. Every breakthrough is waiting behind locked doors. The companies building the keys won’t just unlock trillion-dollar markets; they’ll strike the mother lode of the new AI economy.

Disclosure: Company examples are provided for illustrative purposes only. Metrics are self-reported and have not been independently verified. This analysis does not constitute investment advice or endorsement.