Why OpenAI Is Racing to Make Computer Use Superhuman

Summary

• Ari Weinstein’s speed thesis is that expert-level computer use could make delegation the default. He says agents are already faster than the average human at using a computer and are “getting really close” to exceeding experts. The adoption threshold is overcoming the feeling that “I could’ve just done that myself,” particularly when a task must finish before other work can happen.
• Computer use gives agents access to legacy software without waiting for new integrations. Weinstein’s framing is that “computer use works with everything,” using applications through the same interface as a person. He expects decades-old systems and cheaper, newly built software to coexist, creating room for products that wrap legacy workflows in better experiences.
• Enterprise adoption depends on enforceable boundaries alongside better models. James Sun describes application and website restrictions, configurable approval requirements, and a system he calls Guardian—or “auto-approve” in the app, he thinks—that checks actions against safety and the user’s intent. As agents become too fast to watch continuously, “the control part becomes more and more important.”
• The clearest immediate workflows connect tedious work across systems of record. Sun’s procurement example moves 1,000 spreadsheet rows through purchasing software and vendor outreach; finance reconciliation offers another opening where APIs are missing. Seema Amble’s interjection captures the opportunity: “boring is opportunity.”
• Vertical expertise and deployment remain openings for startups despite broader lab capabilities. Sun argues that understanding regulation, go-to-market and customer service still matters, while OpenAI’s own teams remain stretched. His “speed and pain” moat is illustrated by a restaurant workflow connecting inventory photos, demand forecasting, purchasing and employee scheduling.
• Agents create pressure on web monetization without eliminating the value of interfaces. Sun thinks agent ad impressions usually do not generate revenue, while WebMCP could let people browse and agents handle tedious tasks. His broader position is explicit: “I don’t think UI is going away”; Fabrizio Serafini adds that enterprise users still need payment explanations, audit trails and control.
• Cloud distribution could broaden adoption, but product coherence remains unfinished. Sun reports “YC-level growth week over week” after browser capabilities entered products already used agentically, without providing figures. Reaching a billion users is an ambition tied to giving phone users cloud computers; fragmented browsers, credentials and context remain obstacles, while Weinstein wants agents to “adapt to you instead of you adapting to it.”

Deep dive

1. Superhuman speed would make delegation the default

• Weinstein says computer use is already faster than the average human at using a computer and “getting really close” to being superhuman, meaning faster than an expert computer user. Background jobs can take time, but watched tasks—or tasks that must finish before something else can happen—must overcome “I could’ve just done that myself.” Once agents are faster, he expects delegation to become the default unless someone specifically wants to perform the task.

• Weinstein says Sol was already strong at computer use. Astra is “a little bit faster on average” because it needs fewer thinking tokens for the same task, and feels more reliable around the edges. His comparison between 99% and 97% reliability illustrates why a small improvement matters; he presents that assessment as anecdotal experience.

• Creative gains appear in the Microsoft Paint launch demo Weinstein cites. Sun adds that Astra beats 5.6 “every time” in internal game matchups and cites tweets describing Astra beating Fallout in “about 30 hours or something like that.” His bridge to practical work remains loose: “I know that probably translates to something.”

• Weinstein calls OpenAI’s offering the “fastest, cheapest, and most reliable” computer-use product, without supplying comparative figures. He expects improving cheaper models to produce more variation in model choice by task. Sun still sometimes handles a two-button task himself because that is easier; further speed gains could remove even that boundary.

2. Enterprise readiness requires controls beyond model capability

• Amble’s pushback is accountability: employers can hold a trained person responsible, even if that person performs worse. Sun generally finds Astra capable of “anything that a person can do” on a computer, with sensitive actions and messages that should feel personal warranting involvement. Repeatable work benefits from product-spec-like requirements; flexible input asks for missing details but can keep gathering context if the user does not answer.

• Sun calls Astra “our most aligned model,” citing greater resistance to prompt injection and unintended actions. That matters for procurement, vendor interactions and customer communications. Yet enterprise reassurance also involves showing administrators internal and third-party evaluations and giving them explicit ways to exclude sensitive applications or data sources.

• The deterministic layer is a configurable sandbox: administrators can permit particular applications and websites while excluding others. Separate approval settings can require a person’s intervention for downloads, uploads or entry to a new website. Sun describes these as “dials” an enterprise can adjust, allowing different workflows to carry different levels of autonomy.

• Sun describes a system called Guardian—or “auto-approve” in the app, he thinks—that uses a model to assess each action for safety and consistency with the original intent. Consumers may want minimal interruptions; enterprises can choose which actions proceed. Weinstein adds visible cursor movement and permissions for new applications or websites, depending on mode, as ways to build trust before users become comfortable delegating more.

3. Universal access connects fragmented systems of record

• Weinstein’s architectural point is universality: “computer use works with everything,” without waiting for a developer to create a connector or MCP server. Sun calls it a “universal kind of API” that increasingly appears inside complex tasks. His desired “shared intelligence layer” would let applications benefit from context gathered elsewhere, avoiding a fragmented world in which every website has an agent that cannot talk to yours.

• For the “boring is opportunity” category, Sun’s sharpest example is procurement. Teams fill a spreadsheet with 1,000 rows of lab-equipment requests; operations transfers those requests into internal purchasing software; procurement then uses its output to contact vendors. He sees computer use automating the entire sequence across the spreadsheet, internal system and external interactions.

• Sun also highlights month-end reconciliation across expense and other ledgers whose software lacks APIs: agents can reconcile records, flag anything interesting and investigate. His second category is more ambitious creation—circuit design, architecture, hardware engineering and game design—supported by stronger visual capabilities. He thinks usage there could expand as enterprises understand the models beyond the first opportunities to automate tedious work.

4. Proactivity helps when tasks change shape

• Sun’s Dots example starts with notification fatigue: his old Codex automation checked Slack every hour or 30 minutes, but inconsistent relevance taught him to ignore notifications. A Dot can instead follow a judgment-based goal—flag messages from his reporting chain or critical product feedback—when the task is “not the same shape every single time.” Each Dot gets its own cloud computer, letting it act on what it finds.

• For a relevant bug report, Sun describes the agent using a browser to enter Sentry, retrieve logs, analyze them and return feedback. That replaces his manual loop of deciding which report matters, opening Sentry, downloading logs and feeding them into Codex. He says Dots can control both local and cloud computers, supporting this kind of proactive, long-running workflow.

• Weinstein’s consumer examples include DMV forms, unsubscribing from emails, and work people previously lacked time to undertake. His own examples include finance and meal-planning spreadsheets in Apple Numbers, which he calls an “obscure spreadsheet program” that the model nevertheless handles well. He also mentions a personal trainer making customized materials for clients. These are tasks that can be time-prohibitive in personal life but become easier with computer use.

5. Legacy systems and crafted interfaces can coexist

• Sun asks whether agents preserve clunky incumbents or enable new AI-native vendors. Weinstein expects both: the cost and complexity of creating software are much lower, but production systems written 10, 20, 30 or 40 years ago will not disappear overnight. Computer use “may put more pressure” on vendors to work better with agents because their software can already be operated through its existing interface.

• On the open web, Sun identifies a business-model conflict: he thinks agent ad impressions usually do not count toward revenue. For willing sites, OpenAI supports WebMCP in its in-app browser. He imagines people retaining enjoyable hotel or shopping browsing, while agents handle tedious work; the person can finish the transaction, enter their credit card, and then hand work back to the agent. WebMCP lets the person and agent use the site’s interface together.

• Sun’s defense of craftsmanship is a tactical trainer he built for his wife for a game of connecting five pebbles that he thinks is called Omok. He tailored the learning experience to her preferences; an agent could summarize mistakes afterward, but playing remains her activity. “I don’t think UI is going away.” Weinstein extends that possibility to software designed for “100 people or one person.”

• Serafini’s enterprise counterpart is an interface for setting goals and inspecting logic. A user should be able to understand why a payment happened and specify timing or a discount at 30 days without executing the whole workflow. “Why should we settle for shitty software?” Weinstein imagines purpose-built interfaces with agents underneath, sometimes operating legacy applications to deliver the experience.

6. Startup opportunity remains in domain knowledge and deployment

• Sun remains bullish on startups despite OpenAI’s scale: even with thousands of employees, its teams feel stretched. Founders who understand an industry’s regulation, go-to-market motion and customer service still have room to build. His formulation is that “speed and pain are kind of the moat,” with slower AI diffusion across many verticals leaving substantial work available.

• His restaurant example joins several systems: photos of back-of-house storage help estimate what will be missing based on projected traffic for the next day; computer use then orders groceries through Shopee or another procurement application. The workflow also adjusts employee schedules in an ERP, taking account of historical traffic, such as a holiday period. Sun’s point is that these customers need help understanding the tools, connecting them, deploying them and servicing them.

• Weinstein’s practical advice is to decide whether the product assists a person at their desktop or operates software on a cloud machine through the API. Human intervention should follow that choice. Founders must also decide when to report success or failure and how to summarize activity: a stream of “this happened” notifications will eventually be ignored.

7. Cloud distribution needs a more coherent experience

• Weinstein describes six to nine months of change: capabilities improved earlier in the year, followed by rapid product uptake in the last couple of months, although only part of OpenAI’s user base uses computer use. Sun’s candid lesson comes from Atlas, which he joined in January: “RIP Atlas.” Its agentic features were not a major draw because many people treated it as an ordinary browser.

• Sun says incorporating browser capabilities into existing products with established agentic usage produced “YC-level growth week over week,” without giving numbers. He sees cloud access as the next inflection: giving ChatGPT users, including people using their phones for daily life, a powerful computer they do not need to understand how to operate is his route toward a billion users.

• For the next quarter, Sun emphasizes coherence. The Chrome extension can use Chrome’s password manager and other Chrome features, while the desktop app’s in-app browser has different cookies and passwords, and the cloud computer has different things. Consumers encounter several browsers that do not communicate as expected. Computer use is conceptually simple—“it’s an AI using your computer”—but those implementation details make the experience harder to understand.

• Weinstein wants to add real-time, multimodal collaboration to a product currently strong at background work. He also identifies a behavioral hurdle: people must remember to delegate and change established habits. Dots’ proactivity suggests a way forward, with the agent identifying useful work itself. His ambition is for it to “adapt to you instead of you adapting to it,” while people continue setting and steering goals.