Over the past few weeks, it's become obvious that consumer AI agents have hit an inflection point. Meta launched Muse, a personal agent that runs on its own virtual machine and can shop, schedule, and fill out forms on a user's behalf. Meanwhile, startup Instinct's valuation grew to $10B in a matter of months. We’re finally seeing what agentic AI looks like beyond tech demos.
It's been fascinating to watch, but the more consequential battle is being fought elsewhere. As I wrote over a year ago, the pool of revenues AI companies can extract from consumers is limited relative to AI capex: consumer AI will mostly reshuffle existing ad dollars (with some TAM expansion from subscriptions and AI agents reducing transaction friction). For the AI trade to work, net new revenues in the trillions have to come from the enterprise.
The enterprise AI landscape is still in flux. Today, frontier labs capture most of the economics, largely through coding. This revenue concentration likely won't extend to all knowledge work. As open-weight models become good enough, most everyday knowledge work, by volume, will be able to run on cheaper models, in the cloud or on-device. Frontier labs will still capture the bulk of revenue from the most demanding work, but there's plenty of room below the frontier for very large businesses. This has real ramifications for the current AI trade.
In this post, I cover the following:
Why bounded tasks are crucial in driving the next leg of AI revenue growth
How past shifts in the way humans interacted with technology changed who captured value
What agentic knowledge work looks like today and why the current approach isn't the end state
A framework for thinking about where models run and where data lives
How incumbents, frontier labs, and startups are coming at the problem from different angles
The ramifications of good-enough intelligence for the AI trade
Let's dive in.
Why bounded tasks are crucial for the next leg of AI revenue growth
This piece is mostly about knowledge work for “everyone else” (i.e. bounded tasks). To level-set the conversation, unbounded tasks are defined as tasks that can always benefit from additional intelligence (e.g. AI for R&D, trading, coding). Bounded tasks, in contrast, only require a fixed level of intelligence, above which model improvement doesn’t increase task performance. Examples include extracting data from financial filings, drafting email replies, or resolving simple customer service requests. Today, outside of coding, revenues from unbounded tasks are limited, and it’s difficult to project when these workloads can generate significant revenues (e.g. when can AI cure cancer). So, in the meantime, it’s critical that other types of knowledge work make up for the gap in AI revenue if coding revenue growth slows down. This is why understanding what the product surface for bounded tasks looks like is crucial to the AI trade.
How we interacted with technology pre-AI
To understand what knowledge work looks like in the agent era, it's worth studying the last couple of technology shifts, as each shift changed who captured the value.
One shift was from the command-line interface to the GUI. Microsoft was the biggest winner here, owning both the operating system (Windows) and the apps where work got done (e.g. Word, Excel, PowerPoint, Outlook). Another was the shift from single-player to collaborative, cloud-first software. Compute and files moved to the cloud, and the PC increasingly became a thin client for the browser. Companies like Salesforce, Workday, ServiceNow, and Google were able to “steal” share of the job to be done. As a result, the concept of files mattered less: CRM users don’t care where their account records are stored. That said, files never fully went away. Finance still runs on Excel, and most enterprises still pass around decks and Word documents.
Even before AI agents, enterprise work was in a weird state: depending on their function, knowledge workers would use SaaS apps, local apps that create / edit local files, or often, both. With the advent of AI agents, it’s unclear where “work” will happen. Where will data live? And where will inference run? Most importantly, who captures this value?
Current agentic knowledge work is not the end state
The market is still quite early in terms of product maturity. Depending on the organization, a knowledge worker's sophistication might range from simple ChatGPT queries to using ChatGPT / Claude's Excel add-ons to using bespoke harnesses (e.g. Harvey / Legora for legal). OpenAI and Anthropic’s desktop apps are early, but imperfect, glimpses of the future. They can perform a wide range of tasks (e.g. doing research and editing Excel, Word, and PowerPoint files). What's interesting is how these products are architected. The agent works on files sitting on the users’ computers (or connects to cloud-based applications), but the model doing the "thinking" runs in the cloud. There are rough edges with this flow:
Destructive local edits: agents can overwrite or delete local files, and it's hard to see what changed or to roll it back. Coding agents inherited a safety net in Git, but there's no equivalent for an agent editing a Word doc or an Excel model on a user's hard drive unless the user uses Google Drive / Box / Dropbox’s versioning features or puts their files on GitHub
Inconsistent behavior when interacting with applications: it’s very common for agents to go haywire as they interact with third-party applications (e.g. accidentally deleting emails)
Security: agents with broad access to a user's drive are exposed to prompt injection attacks, where malicious instructions hidden in a document or webpage hijack the agent
The closed lid problem: if a user closes their laptop lid, the agent pauses. Cloud versions of these products keep running, but reaching local files depends on the user's computer being awake and online (users can get around this by using cloud storage and connecting this data to the agent)
A 2x2 matrix for reasoning about agentic knowledge work
The simplest way I've found to think about the current landscape is a 2x2 matrix: where the model doing the work runs, and where the data it works on lives (for SaaS apps, the data already lives in the cloud).
Cloud models, local data (transitional): this is where Anthropic and OpenAI's desktop apps sit today, with all the rough edges highlighted above
Local models, cloud data (implausible): if the data already lives in the cloud, the user is online anyway. It makes little sense to pull files down onto a device just to run a weaker model on them
Cloud models, cloud data (cloud-native): agents get their own VMs and work on copies of files in a sandbox. Cloud-native products can incorporate sophisticated versioning / rollback strategies. This makes whoever “owns” data more important (Google Drive, OneDrive, Dropbox, Box)
Local models, local data (fully local): everything happens on the user's device. This assumes that local models will be good enough for most work
Many of today's products are in the transitional state (with the associated drawbacks). The frontier labs have added cloud-native capabilities, but those capabilities are still comparatively immature. Over time, I expect products to converge on a single, seamless form factor (automatically switching between the three configurations above).
The other important point here is intelligence sovereignty. For certain types of tasks (e.g. biology or model training), proprietary models can outright refuse user requests. More sophisticated customers will, to the extent they can, migrate workloads to models they own.
Challengers in the arena are attacking knowledge work from different angles
In terms of competitive dynamics, companies currently fall into one of three buckets.
Frontier / near-frontier AI labs (OpenAI, Anthropic, SpaceX): OpenAI, Anthropic, and SpaceX have do-it-all surfaces that can research, edit files, write code, and plug into other apps. These labs are making a push toward cloud-native products:
SpaceX’s Grok Bot was first and was a natural evolution of OpenClaw / Hermes. Grok Bot provisions a VM per user which can be used to sign into a user's apps
OpenAI just unveiled Dots, which is its own take on always-on agents
AI labs have the best models but have limited access to data. So it makes sense they are going for the agentic play where they connect to / do work on external data sources (e.g. Google Drive, GitHub, Notion). However, outside of code, user experience is not very consistent. So, the logical next step for these labs is to acquire data beyond chat histories.
Big Tech incumbents (Google, Microsoft, Meta): both Microsoft and Google are where “work happens” right now via Office and Google Workspace, while Meta brings massive consumer distribution. However, none of them has yet turned that position into hundreds of billions of dollars in new AI revenue from general knowledge work.
Microsoft initially opted for a copilot approach via its various Office apps, but given the success of OpenAI and Anthropic, is going the all-in-one route and recently launched the Copilot app
Google has deeply integrated Gemini within Google Workspace. However, its models now trail frontier labs’ offerings, and it’s unclear when its newest model, Argon, will be broadly rolled out
Everyone else: there is a dispersion of product directions:
Apple is well-positioned as a local AI player. Its top-end Macs are powerful enough to run smaller local models. Apple will likely take on a hybrid approach where only harder requests are escalated to its Private Cloud Compute servers, which could go beyond providing inference for Apple Intelligence
Growth-stage startups like Perplexity, Genspark, and Manus have unique product wedges. Genspark, for example, embeds an Excel-like product next to its chat interface. These startups’ revenue scale (in the hundreds of millions of dollars) is 2-3 orders of magnitude smaller than larger competitors, so they are in a race to win distribution before labs and incumbents kill them
Open-source self-learning agents like OpenClaw and Hermes enable models that run locally and in the cloud, but are harder for the average user to set up
Harnesses like LM Studio have adopted a hybrid approach where users can run a combination of local models and cloud-hosted models
What are the ramifications for investors?
The platform that “wins” mainstream knowledge work is far from decided, despite the dominant positions of Anthropic and OpenAI. There are hundreds of billions / trillions of dollars of bounded AI revenue up for grabs. We can then reason about the downstream ramifications of this insight:
If AI adoption for mainstream knowledge work is slower than expected, there will be a gap in AI revenue as coding revenue growth slows down. It’s also unclear when other unbounded tasks (e.g. drug discovery, trading, robotics) can pick up the slack at a meaningful scale
Furthermore, if we are already spending $1T globally today on AI and reach multiple trillions by, say, 2030, then inference revenues need to be several trillions a year, and the actual “ROI” from AI needs to be a multiple of inference revenues
So, for the AI trade to remain intact, mainstream knowledge work needs to generate enough revenues in the meantime. Otherwise, most names in the bottleneck trade will be affected
While AI labs would like to capture revenues via their own models, they may be forced to shift to being neutral platforms as customers become more sophisticated. OpenAI, for example, is testing out a hybrid approach via its partnership with Baseten. This means potentially slower revenue growth and thinner margins
In the medium term, personal computing devices will likely ship with enough compute / memory such that local models in the tens of billions of parameters can handle some portion of knowledge work. This means some revenue will shift from cloud models to local ones
There’s room for a “Switzerland”-type platform that dynamically shifts workloads between local and cloud models and seamlessly transitions work across devices (e.g. laptop to smartphone)
Microsoft is best positioned to be this “Switzerland” among incumbents. The company just unveiled its hybrid AI vision where AI runs locally or in the cloud depending on the workload. Here, Windows laptops with Nvidia's RTX Spark chips handle local inference. What’s unclear is whether Redmond can design thoughtful products to get users to switch from ChatGPT and Claude. Apple is another potential beneficiary
There are many startup challengers: local harnesses (LM Studio, OpenCode), cloud-first do-it-all platforms (Perplexity, Genspark, Manus), model routers (OpenRouter). All of these startups have the chance to become neutral knowledge work platforms
The current way that models interact with third-party data is via various connectors. AI labs will likely want to own the data layer themselves, so it’s very possible that the labs’ corporate development teams have considered acquiring Dropbox / Box to fast-track this process
Mainstream knowledge work is still up for grabs
Today's product experience, where cloud models work on files sitting on a user's laptop, will likely not be the final state. I expect the labs to keep the bulk of AI revenue, especially from unbounded work like coding and science. But most knowledge work doesn't need the frontier, which changes the shape of the AI trade. Work that moves to cheaper cloud models earns a fraction of frontier prices, and inference that runs on a user's own device is revenue that never reaches the data center at all. The various players in the arena understand this dynamic, and we are seeing early signs of their competitive response (e.g. OpenAI’s Baseten partnership, Microsoft’s hybrid AI strategy). Whoever wins this “Switzerland” layer will claim the very large knowledge work market below the frontier.
What happens to the AI trade when good-enough intelligence does most knowledge work?
This piece was originally published on my Substack. I'm around on X / Twitter and LinkedIn.
Over the past few weeks, it's become obvious that consumer AI agents have hit an inflection point. Meta launched Muse, a personal agent that runs on its own virtual machine and can shop, schedule, and fill out forms on a user's behalf. Meanwhile, startup Instinct's valuation grew to $10B in a matter of months. We’re finally seeing what agentic AI looks like beyond tech demos.
It's been fascinating to watch, but the more consequential battle is being fought elsewhere. As I wrote over a year ago, the pool of revenues AI companies can extract from consumers is limited relative to AI capex: consumer AI will mostly reshuffle existing ad dollars (with some TAM expansion from subscriptions and AI agents reducing transaction friction). For the AI trade to work, net new revenues in the trillions have to come from the enterprise.
The enterprise AI landscape is still in flux. Today, frontier labs capture most of the economics, largely through coding. This revenue concentration likely won't extend to all knowledge work. As open-weight models become good enough, most everyday knowledge work, by volume, will be able to run on cheaper models, in the cloud or on-device. Frontier labs will still capture the bulk of revenue from the most demanding work, but there's plenty of room below the frontier for very large businesses. This has real ramifications for the current AI trade.
In this post, I cover the following:
Let's dive in.
Why bounded tasks are crucial for the next leg of AI revenue growth
This piece is mostly about knowledge work for “everyone else” (i.e. bounded tasks). To level-set the conversation, unbounded tasks are defined as tasks that can always benefit from additional intelligence (e.g. AI for R&D, trading, coding). Bounded tasks, in contrast, only require a fixed level of intelligence, above which model improvement doesn’t increase task performance. Examples include extracting data from financial filings, drafting email replies, or resolving simple customer service requests. Today, outside of coding, revenues from unbounded tasks are limited, and it’s difficult to project when these workloads can generate significant revenues (e.g. when can AI cure cancer). So, in the meantime, it’s critical that other types of knowledge work make up for the gap in AI revenue if coding revenue growth slows down. This is why understanding what the product surface for bounded tasks looks like is crucial to the AI trade.
How we interacted with technology pre-AI
To understand what knowledge work looks like in the agent era, it's worth studying the last couple of technology shifts, as each shift changed who captured the value.
One shift was from the command-line interface to the GUI. Microsoft was the biggest winner here, owning both the operating system (Windows) and the apps where work got done (e.g. Word, Excel, PowerPoint, Outlook). Another was the shift from single-player to collaborative, cloud-first software. Compute and files moved to the cloud, and the PC increasingly became a thin client for the browser. Companies like Salesforce, Workday, ServiceNow, and Google were able to “steal” share of the job to be done. As a result, the concept of files mattered less: CRM users don’t care where their account records are stored. That said, files never fully went away. Finance still runs on Excel, and most enterprises still pass around decks and Word documents.
Even before AI agents, enterprise work was in a weird state: depending on their function, knowledge workers would use SaaS apps, local apps that create / edit local files, or often, both. With the advent of AI agents, it’s unclear where “work” will happen. Where will data live? And where will inference run? Most importantly, who captures this value?
Current agentic knowledge work is not the end state
The market is still quite early in terms of product maturity. Depending on the organization, a knowledge worker's sophistication might range from simple ChatGPT queries to using ChatGPT / Claude's Excel add-ons to using bespoke harnesses (e.g. Harvey / Legora for legal). OpenAI and Anthropic’s desktop apps are early, but imperfect, glimpses of the future. They can perform a wide range of tasks (e.g. doing research and editing Excel, Word, and PowerPoint files). What's interesting is how these products are architected. The agent works on files sitting on the users’ computers (or connects to cloud-based applications), but the model doing the "thinking" runs in the cloud. There are rough edges with this flow:
A 2x2 matrix for reasoning about agentic knowledge work
The simplest way I've found to think about the current landscape is a 2x2 matrix: where the model doing the work runs, and where the data it works on lives (for SaaS apps, the data already lives in the cloud).
Cloud models, local data (transitional): this is where Anthropic and OpenAI's desktop apps sit today, with all the rough edges highlighted above
Local models, cloud data (implausible): if the data already lives in the cloud, the user is online anyway. It makes little sense to pull files down onto a device just to run a weaker model on them
Cloud models, cloud data (cloud-native): agents get their own VMs and work on copies of files in a sandbox. Cloud-native products can incorporate sophisticated versioning / rollback strategies. This makes whoever “owns” data more important (Google Drive, OneDrive, Dropbox, Box)
Local models, local data (fully local): everything happens on the user's device. This assumes that local models will be good enough for most work
Many of today's products are in the transitional state (with the associated drawbacks). The frontier labs have added cloud-native capabilities, but those capabilities are still comparatively immature. Over time, I expect products to converge on a single, seamless form factor (automatically switching between the three configurations above).
The other important point here is intelligence sovereignty. For certain types of tasks (e.g. biology or model training), proprietary models can outright refuse user requests. More sophisticated customers will, to the extent they can, migrate workloads to models they own.
Challengers in the arena are attacking knowledge work from different angles
In terms of competitive dynamics, companies currently fall into one of three buckets.
Frontier / near-frontier AI labs (OpenAI, Anthropic, SpaceX): OpenAI, Anthropic, and SpaceX have do-it-all surfaces that can research, edit files, write code, and plug into other apps. These labs are making a push toward cloud-native products:
AI labs have the best models but have limited access to data. So it makes sense they are going for the agentic play where they connect to / do work on external data sources (e.g. Google Drive, GitHub, Notion). However, outside of code, user experience is not very consistent. So, the logical next step for these labs is to acquire data beyond chat histories.
Big Tech incumbents (Google, Microsoft, Meta): both Microsoft and Google are where “work happens” right now via Office and Google Workspace, while Meta brings massive consumer distribution. However, none of them has yet turned that position into hundreds of billions of dollars in new AI revenue from general knowledge work.
Everyone else: there is a dispersion of product directions:
What are the ramifications for investors?
The platform that “wins” mainstream knowledge work is far from decided, despite the dominant positions of Anthropic and OpenAI. There are hundreds of billions / trillions of dollars of bounded AI revenue up for grabs. We can then reason about the downstream ramifications of this insight:
Mainstream knowledge work is still up for grabs
Today's product experience, where cloud models work on files sitting on a user's laptop, will likely not be the final state. I expect the labs to keep the bulk of AI revenue, especially from unbounded work like coding and science. But most knowledge work doesn't need the frontier, which changes the shape of the AI trade. Work that moves to cheaper cloud models earns a fraction of frontier prices, and inference that runs on a user's own device is revenue that never reaches the data center at all. The various players in the arena understand this dynamic, and we are seeing early signs of their competitive response (e.g. OpenAI’s Baseten partnership, Microsoft’s hybrid AI strategy). Whoever wins this “Switzerland” layer will claim the very large knowledge work market below the frontier.