OpenAI is Building AI Agents for Everything: Will Everyone Use Them?
The question is no longer whether artificial intelligence can answer your questions. The real question is how much control you are willing to give a large language model over your digital life. Getting the most value from a model means giving it the keys, and for many, that feels like a leap of faith.
For Andrew Ambrosino, the lead engineer for OpenAI’s desktop app, it is the only way to test the future. That is why his app now has access to and control over his inbox, Slack account, phone, Notion, Figma, and more. He acknowledges the risks, saying that when asking it to write a document, there is a possibility it could pull from a private DM without knowing it should not share certain info. Yet he accepts these tradeoffs for the job, and so far, he has not had to pay a personal price for it.
Ambrosino works on OpenAI’s biggest bet, ChatGPT Work, which was released last month and is available on the company’s lowest subscription tier for $20 a month. The product is designed to allow white-collar workers to field AI agents, hooking LLMs up to the digital workflows used by accountants, investors, doctors, and everyone else whose day to day is dominated by their computer.
OpenAI’s marketing copy puts the goal succinctly: a world where intelligence goes beyond answering questions to helping everyone turn their biggest ideas into reality.
For software developers, that shift is already happening, but it has been slow to spread to other departments. ChatGPT Work is a modified version of the company’s Codex coding tool. It is meant to give non-engineers a version of the same functionality that software engineers already get from agents: an AI tool that does not just answer questions but completes multistep projects on its own.
Thibault Sottiaux, who leads OpenAI’s core product work including Work, told TechCrunch that in this new factor, ChatGPT can actually do entire very complicated tasks for you all autonomously in a way that is delightful and safe. He emphasized that it is the very mission of OpenAI to bring everyone along.
Commercially, that matters a lot. Agents that work for longer stretches burn through more tokens, which makes them more lucrative for OpenAI on a per-user basis. Reaching new professions is crucial, not just for OpenAI but for the industry at large. If coding has proven lucrative territory for AI labs, it is still a tiny subset of the professional work AI tools need to enable if these companies are to justify their massive investment in training and computation. While labs have focused on software engineers, vertical-specific competitors like Harvey for law and Clay for sales have been chasing those customers with a model-agnostic approach, meaning they will plug in whichever AI works best at the time.
Industry analysts see this as one of the major challenges facing OpenAI and its competitors. Christian Catalini wrote on a16z’s blog that if the labs cannot rapidly get ahold of the key complementary assets needed to scale AI in the market, value will accrue elsewhere.
Making the AI apps work for people who are not software engineers requires more hand-holding. OpenAI’s non-engineering workforce, like the communications and finance teams, started using Codex at a time that it was actively hostile to them, asking them about code and showing them empty diffs. So the company started to make it more general purpose between February and now.
An OpenAI-backed study found that in June, 98 percent of OpenAI employees were using Codex, but just 17 percent of organizational subscribers and less than 1 percent of individual subscribers were using the agentic coding tool. That difference between near total adoption inside the company and negligible adoption outside it is the challenge and opportunity for the company.
Sottiaux noted that the more value and utility they generate for users, the more they will be willing to pay for some part of that utility, and that is how they have always seen ChatGPT as well. You sit there and you think of course you want to pay $20 a month for this because the value you get is so much more.
How to Make AI Intuitive
To understand that disconnect, it helps to understand what OpenAI’s engineers are building. Every LLM requires what engineers call a harness, the software wrapped around a model that decides what information it sees, which tools it can use, and how it presents its answers back to you.
If you want that model to do stuff, to become an agent, the harness gives it tools and instructions for using them on long-term tasks. For developers, a command-line interface that enabled LLMs to code was enough to change the way software was built and deployed. But most people are not using CLIs; there is a reason Windows replaced DOS.
An agentic product that goes beyond software engineering is going to be something that plays with the messy world of your life and your tools and websites that were built in 1995 and never updated, Ambrosino told TechCrunch, explaining that the experiences his team is building are vital to expanding access to useful AI.
Consider apps like Claude Code and Codex: they unleashed vibe coding by abstracting away all the actual software writing and letting users just tell the model what they want in a program. Now, OpenAI wants to make functionality found in tools like OpenClaw, which coders use to put LLMs to work, as easy as prompting.
Without these products in front of the model, experts would know how to get the same results, but you would not get to a billion people using the thing, Ambrosino said. That tradeoff between what power users need and what mainstream adoption requires plays out in internal debates at OpenAI, where some employees argue that a button is unnecessary if users can just ask the model directly.
They push back on that because it is very early, Ambrosino said. Discoverability matters in this phase, and at some point they will not have the button.
Work has a few more buttons for selecting projects and plug-ins, but it aims for the same magic box interface as other OpenAI products. He compares it to skeuomorphism, the fading practice of making digital tools look like the physical objects they replaced, like a calculator app made to look like a pocket calculator. That stuff was not just cringe design. That actually helped get people into this and make the transition, Ambrosino said.
OpenAI would not say how many people used Work versus Codex, but the joint app is used by just 20 million people, compared to more than a billion users the company says are prompting ChatGPT online.
Giving ChatGPT a License to Skill
For now, OpenAI is pitching this tool as best suited for routine data-intensive coordination tasks. Its employees are setting up weekly metrics reports, for example, and making spreadsheets into planning tools.
Venture capitalists are using agents to assemble relevant communications and analysis about companies into investment memos, and ops teams are spinning up bespoke dashboards and data visualizations. Sam Altman is using it to plan his vacations. One OpenAI engineer described asking the program to look at a Slack conversation about an engineering problem and make some charts, then receiving back a series of insightful plots.
Akshay Nathan, who leads the product engineering team at OpenAI, said there is a deluge of information for the average worker or employee of any of these companies, including himself. He explained that people are actually quite limited by their ability to parse everything that is available to them and then take action on it. That information lives in all these system records tools like Salesforce, and the value of ChatGPT is that you already have access to this but now you truly have access to it.
This could be the digital personal assistant that AI evangelists dream about. As with Claude Cowork or Perplexity AI browsing agent, ChatGPT Work links agents to your existing workspace, email, web browser, and a slew of SaaS platforms, and puts that context to work for you.
When the system works, it can be impressive. A reporter asked ChatGPT Work to get his son’s weirdly formatted preschool calendar out of his email and put it into his Google Calendar, and it did, saving a lot of repetitive data entry.
However, there are trust issues. The reporter did not trust OpenAI with access to his inbox, source interviews, or story drafts and would not let it have access to his bank account, but he believes it would be more useful had he the faith. He tasked it to do financial analysis on publicly traded companies that he covers, and it delivered an auto-updating dashboard of metrics. It made a queryable database of space launches, a task he had previously had to accomplish by writing Python scripts. It also sends him a weekly email about new AI research posted at academic clearinghouses.
While asking the model for something is intuitive, giving it what it needs to take action is not as simple. Setting up the permissions for agents to access a cloud drive was confusing and circular. The reporter tried multiple times to give it just read access and received error messages. The model itself was not too helpful, but eventually on the mobile app, a dialog box popped up to tell him that only complete access would make it work.
Many important settings are only available on the web app, so he frequently found himself working in both at the same time. Sometimes ChatGPT Work’s limitations are baffling. Link it to your Google Calendar and it can create events but not new calendars. And do not bother trying to do anything unless the effort level is high, otherwise you have the worst intern you have ever worked with.
That is common advice from AI early adopters, who fear that frustrated newbies will give up. Joe Gershenson, the engineering lead for OpenAI’s harness, admitted that effort settings are not intuitive for new users yet. He said there are things they can do better to help them get the right level of reasoning, adding that people should watch this space.
OpenAI faces another important challenge breaking into normie white-collar work. Most workflows are not as measurable or evaluable as code. Software either works or it does not, and even that distinction reduces the nuance about what makes code good or bad. A good presentation, business strategy, or sales pitch is not as easy to evaluate or trace.
Ambrosino noted that one of the unique challenges with a product like this is just that it can really do anything. When asked which specific problems the team designs around and which workflows it targets, the engineers demurred, saying that was a question for OpenAI’s research team.
OpenAI later provided an answer, telling TechCrunch that it uses its benchmark GDPval, drawn from 44 occupations and hundreds of knowledge work tests, and supplements that with user feedback. A less official answer is that it comes from OpenAI employees themselves. Ambrosino said they have to always parse out whether they are doing the workflow that everybody else will be doing or if they are weird.
The early adopters of the app itself will create valuable traces with their actual usage, much of the success of coding tools is built on similar data collection, assuming they do not opt out of making it available for training.
The Rivalry That Drove OpenAI’s Product Design
Despite all the attention on the model interface, OpenAI’s engineers were reluctant to answer a fairly simple question: what sets Codex and ChatGPT Work apart from Claude Cowork or other competing agentic harnesses intended for a mass user base?
Gershenson gave a typical answer, saying it was going to be a really disappointing answer and he was sorry, but the honest answer was that he really does not look at the harnesses that they are building. He referenced the Mad Men I do not think about you at all meme.
Frankly, the reporter did not believe them, if only based on the extreme similarities between the products’ user interfaces, the need for competitive intelligence at any business, and because the first thing ChatGPT Work asked him to do when he started it up was port over his Claude Cowork data.
It is understandable if Claude Code is a sensitive topic around the OpenAI offices. Their corporate rival defined the market for AI coding and launched a revolution in how software engineers do their jobs. It is additionally frustrating because OpenAI had the idea first but did not quite harness it correctly.
When OpenAI first developed Codex as a web app, the engineers got a bit over their skis, or as Ambrosino puts it, a bit more AGI-pilled. In short, they bet on the model being smart enough to handle a task entirely on its own with minimal user input.
Built shortly afterward, Claude Code was oriented around a back and forth conversation with the user. If you gave it a problem, it would survey the possibilities and give you three or four options for proceeding. Once you chose, it would go a little further and then check back again, giving continual updates and leaving less room for the model and harness to make mistakes.
Anthropic’s approach proved more effective, even if it demanded more work from users. Ambrosino now says their product was a little ahead of where the model and harness was at the time.
OpenAI eventually followed suit by adding more opportunities for users to interact with the model. That became the Codex we know today, with desktop and mobile apps. Using download statistics as a proxy for interest in the programs, Claude Code was more in demand until April of this year, but now Codex has taken a slight lead. Surveys of enterprise use also suggest OpenAI is catching up.
Part of that lead is getting the product-market fit right, and part comes from complaints about safety restrictions on Anthropic’s models and compute shortages. OpenAI’s steps toward more human-centric harness continue with ChatGPT Work, but the engineers insisted the key differentiator is the strength of OpenAI’s latest powerful and cost-effective models.
Ambrosino said the frustrating answer is that a lot of times it is the model, and one thing they have tried to do really well with this app is fully leverage the model.
What Makes a Good Harness Anyway?
That explanation returns to the bitter lesson learned by AI researchers that a better general model is more important than specific domain experience. For true believers, the harness is a temporary crutch, not the moat.
Gershenson told TechCrunch that you could get good results in the short term by adding a whole bunch of extras, if and thens and tools, but the next model is going to come out in a couple of months and make that obsolete. His team focuses on the simplest ways to expose the model to the tools and context it needs, and no more.
He explained that the goal of good harness engineering is to be more precise about what information the model really needs to solve your problem, because the models are getting better and better at doing that if you simply let them do their thing.
There is an open question though if most people or models are ready for that. Ethan Mollick, the Wharton School of Business professor who studies AI tools in the workplace, still sees Claude as more user-friendly, writing that ChatGPT tends to want to do magic and just do it for you, while Claude does comparisons and shows them, repeatedly asking for input and feedback and doing A and B tests.
Sottiaux, and perhaps OpenAI at large, disagree, arguing that the conversational nature of the app is better than learning how to use an application. He said they definitely see that the world seems to be ready, which is why they have had incredible adoption.
Still, it is not clear that a model-specific harness is even the right bet for maximizing a model. Comparisons run by companies like Composio and Databricks show that different harness and model combinations deliver different performance on coding benchmarks. Databricks found that Pi, an open source harness published by the software company Earendil, outperformed Codex while using the same GPT 5.5 model. Pi has been used to build projects like OpenClaw and Cloudflare OS.
Pi’s creator, Mario Zechner, says his intentionally minimalist harness is evidence that an AGI-pilled approach can work, at least for software engineers and coding tasks. What it lacks in explicit features, he says, is made up for by its ability to modify itself and build its own interfaces. He sympathizes with the challenge that OpenAI’s engineers face in expanding their user base beyond engineers.
Zechner told TechCrunch that everything is coding agent shaped because the reason is that they only have training data for coding agent tasks. He explained that if someone is in management, they make a decision today and the outcome happens months later. You cannot capture that in a simple trace of a user and agent back and forth, so all of these kinds of tasks and anything that you do not digitize is inaccessible to a model to learn.
Like other open source providers, he sees the big lab’s effort to push their harnesses as a way to lock in users. He said they need to own the entire stack, otherwise they just become a model provider and then need to compete with Chinese models.
He and other engineers TechCrunch spoke to felt that the insight into token spend and agent behavior in frontier labs’ harnesses is too limited. In a sense, that is less meaningful to non-technical workers, but as with the coding tools, uptake at the scale OpenAI hopes for will eventually force harder conversations about cost.
For example, messing around on a $20 a month subscription, a reporter used more than 80 million tokens in four days, which cost $65 according to the model’s analysis. There is no dashboard in the app. That is a subsidy of more than 3 times the subscription price for four days of casual use alone.
Sottiaux said they are working every day to push the frontier on efficiency, pointing to a recent 80 percent price cut for users of OpenAI’s Luna model. He noted that if you wake up six months from now, you should be able to do all of the same tasks with less spend.
The other relevant question is whether these apps create a lock-in effect on customers through data retention or the sheer pain of configuring access to all the plug-ins and their permissions.
Inside OpenAI’s wood-paneled, plant-filled headquarters, which a reporter visited in July, the atmosphere was calm but slightly tense. These are people with a lot to do. The engineers were constantly monitoring their laptops as they talked and rushing from meeting room to meeting room.
Nathan, the head of the product engineering team, said the focus remains on the promise of the magic box, but he still thinks there is too much complexity. He said he is very optimistic that they can solve it with the model and in a truly AI-native way.
The journey toward AI agents for everyone is clearly underway, with OpenAI leading the charge. Whether the world is ready to trust these digital assistants with their most sensitive data and workflows remains to be seen. The potential is enormous, but so are the challenges of usability, trust, and cost. As these tools evolve, they may indeed become as indispensable as the internet itself, but getting there will require not just better models, but more thoughtful design and a fundamental shift in how we interact with technology.
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