Who Will Make Money in the Age of Agents?

marsbitPublicado a 2026-05-27Actualizado a 2026-05-27

Resumen

In the Agents era of blockchain, traditional value capture theories face challenges. The "Fat Protocol" theory, dominant since 2016, suggested protocols capture most value as their tokens are essential for network use. However, the proliferation of interchangeable L1s, L2s, and modular layers has eroded protocol scarcity and pricing power. Conversely, the "Fat App" theory posits that applications capturing user relationships (like wallets and exchanges) become the primary value layer by controlling distribution and transaction flows. This aligns with the current "Great Repricing" cycle. Agents disrupt this logic. As software users, they lack brand loyalty, prioritize cost and efficiency, and switch between platforms seamlessly. This undermines the front-end UX moats that "Fat Apps" rely on. The article explores several potential futures: 1. **Headless Applications:** Current leading apps could strip their front-ends and become backend API infrastructure for Agents, preserving their role. 2. **Protocol Resurgence:** If integration becomes trivial, Agents might bypass aggregators and interact directly with protocols, reviving "Fat Protocol" dynamics. 3. **Pricing Power Collapse:** Agents' rational, frictionless routing could commoditize the entire stack, compressing margins toward cost and leaving little profit for intermediaries. 4. **Unprecedented Activity:** Agents may enable new, high-frequency, machine-to-machine economic activities, expanding the total value pie ev...

Author: Jonah Burian

Compiled by: Jiahuan, ChainCatcher

Many speculate that the next billion users of blockchain will be Agents. But few ask the further question: In that world, who will make money?

Every previous theory of value capture in the crypto space assumes the user is human. The "Fat Protocols" theory posits that protocols are best at monetizing human users.

The "Fat Apps" theory I explored with my colleagues in "How to Capture Value" and "The Great Repricing" argues that the application layer can do better. But Agents change the nature of the user, and existing theories will break down accordingly.

The Fat Protocols Theory

In 2016, @jmonegro proposed "Fat Protocols." For nearly a decade, it has been the dominant value capture theory in crypto.

The core idea: In the traditional internet, value accumulates at the application layer (@Google, @facebook), while the underlying protocols (TCP/IP, HTTP) capture almost no value. The crypto world will completely reverse this. Blockchains have open, shared data, so applications will become commoditized.

And because using the network requires consuming the protocol's token, the token will capture the speculative value generated by usage growth as it appreciates. Each application's success drives demand for the token. The underlying protocol will grow faster than any application built on top of it.

For years, this seemed correct. Bitcoin and Ethereum's values far surpassed any companies built on them.

This model works perfectly when the protocol itself is scarce, costly to build, and difficult to replace. Bitcoin and Ethereum in 2017 were indeed very scarce; there weren't dozens of general-purpose L1s competing for the same workloads.

Block space was sufficiently constrained that holding the underlying asset felt like holding a piece of every application that needed it.

Today, reliable alternatives exist at every layer of the infrastructure stack: multiple high-throughput L1s, dozens of L2s, and modular settlement and data availability (DA) layers competing fiercely on price. Block space has gone from constrained to abundant.

As cross-chain bridges and aggregators make the underlying chain nearly invisible to users, user switching costs collapse. Infrastructure becomes interchangeable, and interchangeable commodities compete on price. The result is that protocol pricing power dies along with scarcity.

The Fat Apps Theory

By 2026, the entities capturing most of the economic benefits are applications, not protocols: e.g., @phantom, @coinbase, @Polymarket, @Pumpfun.

In my view, the reason is that the most valuable asset in crypto is the user relationship.

If you control the user interface and transaction flow, you control distribution, and can profit from almost any on-chain product users touch: swaps, lending, staking, minting, and fiat on/off ramps. This is probably why funds are so obsessed with neobanks.

Applications also push infrastructure into pure price wars, forcing infrastructure margins down to marginal cost. I documented this dynamic in "How to Capture Value." The same dynamic is playing out in the stablecoin space, which I've discussed elsewhere.

Asset prices are reflecting this theory. Spencer and I called this shift "The Great Repricing": in this cycle, value is starting to accrue to the layer that owns the user.

Why Agents Break This Logic

The Fat Apps theory assumes users are humans who value UX, brand, and convenience. But Agents don't care about any of that. They call APIs directly, have no brand loyalty, and switch between platforms at zero cost.

When the user becomes software, owning the user relationship is no longer an unbreakable moat. The entire front-end moat upon which the Fat Apps theory relies is failing.

So, who captures value in the age of Agents?

Applications Go "Headless"

In one version of the future, winners at the application layer remain winners by shedding their front-end interface, i.e., going "headless."

Wallets and aggregators have already done the hardest work: integrations with dozens of protocols, routing logic, authentication, and fiat on/off-ramp infrastructure.

The logical next step is to open this tech stack as an API for Agents, letting Agents route through them—just as humans route through @phantom or @JupiterExchange today.

In this world, the Fat Apps theory survives. It just loses its front end. The companies that won in the human era transform into pure back-end infrastructure for Agents. We already see traditional SaaS companies like Salesforce moving in this direction.

The Resurgence of Protocols

In another version, Agents completely skip the middle layer.

If integration becomes easy enough (well-documented APIs, standardized RPCs, predictable execution semantics), Agents have no real reason to pay an aggregator to do something they can do themselves. Aggregators' advantages in the human era were UX and handling routing complexity.

But Agents don't need UX, and routing is an engineering problem that Agents are increasingly good at solving.

If the world evolves this way, the Fat Protocols theory gets a second life.

Pricing Power Collapses Across the Stack

Perhaps Agents will apply commoditization pressure everywhere. They are perfectly rational, frictionlessly routing to the cheapest venue every single time with zero loyalty.

Applications lose the ability to charge a UX premium from humans. Aggregators and infrastructure lose pricing power because there is no longer human inertia protecting them from price wars.

In this scenario, no one in the tech stack captures much profit. Margins across the supply chain are forced down to marginal cost. The residual value accrues to the owners of the Agents, or to the end-users the Agents serve.

Crypto becomes a utility, and it's hard to make big money in utilities.

Agents Create Unprecedented Activity

The simple take on this is: Agents are doing everything humans do, just faster and at greater scale. Even if margins are compressed, the overall pie grows.

I think there's a more interesting version.

Agents enable activities that were previously infeasible: e.g., continuously rebalancing a portfolio for less than a penny in execution costs, machine-to-machine commerce between Agents, and entirely new markets that exist only because pricing and transaction speeds far outpace what humans can follow.

Current on-chain activity data doesn't reflect this because we assume a human must be involved.

If this is the change Agents bring, then the question shifts from "how is the existing pie divided" to "how much new economic activity pours on-chain, and which layers are ready to serve it."

A Yet-Unnamed Business Model

Every cycle, we try to guess where value will flow, and tend to think existing business models will extend into the future. This assumption usually causes us to miss the new models that haven't yet emerged.

When the internet was new, no one foresaw the attention economy. The idea that "slicing up user attention and auctioning it to advertisers would become the dominant business model, and a single company could capture a significant share of global ad spend" was alien. It only seems inevitable in hindsight.

AI looks like one of the biggest technological disruptions in decades. In an Agent-dominated world, some value capture might flow to business models no one is talking about today. And the entities capturing that value might not be the ones the market is currently focused on.

What to Watch

The most likely outcome is not one system completely replacing the other. For a long time, humans and Agents will coexist as crypto users, with entirely different maps of value capture.

As long as humans interact on-chain, the Fat Apps theory still applies: consumers willing to pay for UX, brand, and convenience will continue to pay a premium to the applications that own that relationship. And the layers involved in Agent transactions will be governed by a separate set of theories, whichever of the above scenarios becomes reality.

For builders, I think the question worth iterating on the Agent side is: What actually makes an Agent come back to you instead of routing directly to the next cheapest alternative? UX probably isn't the answer. Liquidity, latency, settlement guarantees might be.

Preguntas relacionadas

QAccording to the article, what was the dominant value capture theory in crypto for the past decade, and what was its core argument?

AThe dominant value capture theory was the 'Fat Protocols' theory. Its core argument was that in the crypto world, value accrues to the underlying blockchain protocols (like Bitcoin and Ethereum) rather than the applications built on top. This is because protocols become more valuable as they are used (demand for their token increases), while applications become commoditized due to open, shared data.

QWhat is the 'Fat App' theory, and why is it currently challenging the 'Fat Protocols' theory?

AThe 'Fat App' theory argues that value is increasingly captured at the application layer. It posits that the most valuable asset in crypto is the user relationship. Entities that control the user interface and transaction flow (like wallets and exchanges) control distribution and can monetize various on-chain products. It challenges 'Fat Protocols' because infrastructure has become commoditized with many alternatives, eroding protocol pricing power.

QWhy does the author believe AI Agents will disrupt the current 'Fat App' value capture model?

AThe 'Fat App' model assumes users are humans who value UX, brand, and convenience. AI Agents do not care about these aspects; they interact via APIs with zero brand loyalty and can switch between platforms at near-zero cost. Therefore, the front-end moat (user relationship) that 'Fat Apps' rely on becomes ineffective when the 'user' is software.

QThe article outlines several possible scenarios for value capture in an Agents-dominated world. Name two of them.

ATwo possible scenarios are: 1) Successful applications become 'headless,' pivoting to serve as backend infrastructure APIs for Agents, keeping the 'Fat App' theory alive but without the front-end. 2) Agents bypass middlemen entirely and interact directly with protocols if integration is simple enough, potentially revitalizing the 'Fat Protocols' theory.

QWhat does the author suggest builders in the Agents economy should focus on, instead of user experience (UX)?

AThe author suggests builders should focus on what would make an Agent choose their service repeatedly over a cheaper alternative. Potential answers include factors like superior liquidity, lower latency, or stronger settlement guarantees, rather than traditional UX elements that appeal to humans.

Lecturas Relacionadas

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

Looking Back After Three Years: Revisiting My 2023 Predictions on ChatGPT In March 2023, shortly after ChatGPT's debut and before GPT-4's release, I made over twenty predictions about AI's future based on limited information and intuition. Now, in May 2026, I revisited those forecasts using an AI-driven analysis with 41 Opus 4.8 agents to cross-reference them with the latest data. The assessment used symbols: ✅ Correct, 🟢 Mostly Correct, 🟡 Partially Correct, ❌ Incorrect. Overall, the directional judgments held up well, with only one major factual error regarding GPT-4's rumored parameter size (incorrectly cited as 100T). However, nuances and degrees of accuracy revealed more. **What Was Largely Correct:** Predictions about mechanisms and directions proved accurate. The rise of RAG (Retrieval-Augmented Generation) as the standard architecture for combating AI hallucination was confirmed, as was the transformative potential of LUI (Language User Interface) in creating a new industry layer atop GUIs. The emergence of "robot networks" (agent-to-agent communication protocols) and China's rapid catch-up in developing capable large models (closing the performance gap with top models to ~2.7%) were also on point. The analysis affirmed that LLMs lack consciousness and that the Turing Test merely measures perceived intelligence. **What Was Off Target:** Errors often involved specific numbers, over-optimistic timelines, or misjudged distributions. The prediction that value would primarily accrue to the application layer was half-right but missed NVIDIA's dominance as the profitable infrastructure layer. Forecasts about AI circumventing copyright issues and fostering a "global common ground" by averaging human viewpoints were incorrect; instead, major copyright settlements occurred and AI personalization is increasing. Estimates for model training costs ("$5-10 billion cap") were significantly off, underestimating frontier costs and overestimating replication costs. The notion that LLMs could never do complex math without tools was disproven by later models winning IMO gold. **Key Patterns from the Review:** 1. **Direction over precision:** Judgments about mechanisms and trends were more reliable than specific numbers or definitive statements. 2. **Timing bias:** There was a tendency to overestimate short-term speed but underestimate long-term magnitude and transformation. 3. **The distribution blind spot:** Aggregate-level correctness often masked uneven impacts (e.g., on young professionals' employment). 4. **The value of qualifiers:** Predictions framed with caution (e.g., "reportedly," "for now," "prototype in 2-3 years") aged better. 5. **Some debates continue:** Issues like the nature of "emergent abilities" or machine consciousness remain unresolved. This three-year review highlights that while seeing the big picture is crucial, humility regarding specifics, timelines, and disparate impacts is essential for future forecasting.

链捕手Hace 13 min(s)

Three Years Later: Looking Back on My 2023 Predictions for ChatGPT

链捕手Hace 13 min(s)

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

The article issues a stark warning about a potential AI investment bubble. It notes that while the AI boom shares similarities with the TMT bubble of the late 1990s, its scale is vastly larger, currently driving 93% of U.S. GDP growth. Major hyperscale cloud providers like Microsoft, Alphabet, Amazon, Meta, and Oracle are planning to invest trillions in AI data centers over the coming years. However, calculations based on analyst projections for 2025-2030 reveal a concerning math problem: expected capital expenditure growth far outpaces projected revenue growth. Even under an extremely optimistic scenario of zero costs, the implied return on investment for most of these tech giants (except Amazon) is deeply negative. This suggests that the current trajectory could lead to one of history's largest shareholder value destruction events. The piece outlines two potential escapes: AI generating vastly more revenue than currently anticipated—a near-impossible task—or a significant cutback in the planned investment splurge. The latter scenario could trigger a domino effect, severely impacting the entire tech supply chain (from Nvidia to TSMC), potentially pushing the U.S. economy into recession, and causing a major stock market downturn. The author suggests upcoming high-profile IPOs by companies like OpenAI and Anthropic might represent a transfer of risk from early investors to public market participants. While the peak of the hype cycle might sustain investment through 2026, the fundamental financial dilemma remains unresolved, setting the stage for a potential market correction in 2027 or 2028, similar to the years following Alan Greenspan's "irrational exuberance" warning.

marsbitHace 1 hora(s)

AI Bubble Warning: AI Investments Are Negative Returns for Most Tech Giants

marsbitHace 1 hora(s)

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

The article "From Token to Machine Labor: AI is Evolving from Tool to 'Worker'" argues that the business model for AI is shifting beyond simply selling computational resources (tokens, GPU hours) or model access. Instead, a new "machine labor market" is emerging, where the core economic transaction is the purchase of economically useful work directly performed by software. The central thesis is that AI pricing will evolve through four stages: 1) raw tokens, 2) standardized LLM capabilities (e.g., text generation), 3) industry-specific labor markets (e.g., legal review, radiology), and finally 4) a programmable results market where tasks like resolving a support ticket are bid on and priced based on outcome. In this future, buyers will care less about *which* model or GPU completes a task and more about whether the work meets specified standards for accuracy, latency, and cost. This transition reframes the impact of AI on human labor. Rather than simple replacement, it suggests a re-coordination where machines handle standardized, verifiable work, freeing humans for roles involving oversight, context management, responsibility, and final judgment. In some cases, this "last 1%" of human input becomes more valuable as it enables the other 99% to be automated. Furthermore, as AI reduces the cost of work, demand may expand, creating larger markets (e.g., 24/7 customer service) rather than just cheaper versions of existing ones. The article concludes that while infrastructure (GPUs, models, tokens) remains crucial upstream, the market is converging on a simpler, tradeable unit: machine labor that can be defined, measured, priced, and procured based on contractible specifications.

marsbitHace 1 hora(s)

From Tokens to Machine Labor: AI is Shifting from Tool to "Worker"

marsbitHace 1 hora(s)

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

The price of Xiaomi's MiMo-V2.5 series API has been permanently reduced by up to 99%, specifically for the "Input (Cache Hit)" cost, which covers users re-reading historical context in long conversations. MiMo's head, Luo Fuli, published a detailed technical blog to clarify that this drastic price cut stems from genuine engineering breakthroughs, not a marketing stunt or a simple price war. The core of the achievement lies in six key engineering optimizations. First, the model architecture adopts a Hybrid Sliding Window Attention (SWA), reducing the memory footprint (KVCache) to 1/7th of a traditional model. Second, a dual-pool memory management system actually utilizes these savings, allowing a single GPU to handle over 5 times more concurrent users. Third, an upgraded prefix caching mechanism achieves a cache hit rate of 93-95% for repeated reads, meaning most such requests bypass GPU computation entirely. Fourth, a self-developed distributed cache (GCache) utilizes idle SSD space on existing GPU servers, eliminating additional storage costs. Fifth, an intelligent scheduling system (LLM-Router) efficiently routes requests to maximize cache reuse and performance. Sixth, Multi-Token Prediction (MTP) accelerates the model's text generation ("output") side. Together, these systemic optimizations dramatically lower the real computational cost per request, enabling the 99% price reduction for cached inputs while reportedly maintaining positive gross margins. Luo Fuli's disclosure aims to shift the narrative from "price war" to a demonstration of substantive AI engineering progress.

marsbitHace 3 hora(s)

Xiaomi MiMo's 99% Price Cut is Not Marketing! Luo Fuli Posts on X to Refute Critics

marsbitHace 3 hora(s)

$26 Billion: An 'All-Chinese Team' Backs the World's Highest-Valued AI Programming Company

Cognition AI, the company behind the AI programmer "Devin," has raised over $1 billion in new funding at a valuation of $26 billion, just eight months after reaching a $10.2 billion valuation. The round was led by Lux Capital, General Catalyst, and 8VC. Founded by three young Chinese entrepreneurs with strong competitive programming backgrounds, Cognition initially gained fame with Devin, marketed as the world's first AI software engineer capable of handling tasks from start to finish. While its early demos were impressive, real-world usage revealed reliability and cost-effectiveness issues, leading to a significant price cut for Devin in 2025. A pivotal moment came when Cognition acquired the assets of AI IDE company Windsurf after a failed acquisition by OpenAI. This move gave Cognition a crucial developer-facing tool, allowing it to pursue a two-pronged strategy: Devin for autonomous task execution and Windsurf for integrated, collaborative coding within an IDE. This shift helped the company move away from the controversial "AI replacement" narrative towards a model of augmenting human engineers, particularly for repetitive or maintenance tasks. This strategic pivot is backed by strong commercial metrics. The company reports a 10x increase in enterprise usage this year, with an annual revenue run-rate of $492 million and a 50% month-over-month growth in enterprise Devin usage over the past six months. Its client list now includes major corporations like Goldman Sachs and Mercedes-Benz, as well as government agencies like NASA and the U.S. Army. Investors are betting on Cognition becoming a foundational piece of next-generation software engineering infrastructure, positioning it at the center of a hybrid future where AI agents and human developers work in tandem.

marsbitHace 3 hora(s)

$26 Billion: An 'All-Chinese Team' Backs the World's Highest-Valued AI Programming Company

marsbitHace 3 hora(s)

Trading

Spot
Futuros
活动图片