Inside OpenAI, This Productivity Hack Is Giving Workers Their Own Chief of Staff. You Can Use It Too

Inside OpenAI, the company behind ChatGPT, employees both technical and non-technical are using Codex, the company’s agentic coding app, to handle an increasing amount of work. Codex is OpenAI’s label for its AI-coding platform, which has been accessible through the cloud for over a year. It experienced a huge surge in growth, however, following the release of a dedicated Codex desktop app for Mac and Windows PCs. Unlike the cloud-based versions of Codex, the desktop app is capable of connecting to a computer’s local filesystem, editing existing files, and creating whole new files. In effect, this means that by using the Codex app, users can direct AI agents to do any work that requires a computer. Within OpenAI, non-technical employees are using it in ambitious ways. “The investor relations team is like three people,” says Alex Embiricos, the product manager responsible for Codex, and has been using Codex to monitor the influx of cash from the company’s recent $122 billion fundraise. During a recent all-hands meeting, Embiricos recalls, CFO Sarah Friar told staff that a member of her team had about half an hour to kill, and in that time “vibe coded a dashboard that just showed the incoming expected transfers lighting up, and sent it to Sarah over the weekend.” Two members of the Codex team spend much of their time hosting office hours and answering questions from non-engineering teams at OpenAI. By monitoring if usage rates spike after one of these sessions or hackathons, the Codex teams can understand more about what use cases certain divisions of the company are discovering for themselves. Laura Peng, one of the members of the Codex product operations team, says that many employees are getting utility out of simple dashboards that connect to their email and work communications service like Teams, Slack, and Google Meet, along with data sources like Excel and Sheets. Employees are monitoring emails, keeping track of deadlines, and summarizing and responding to Slack messages. “I feel like I almost have my own chief of staff, in a way,” says Peng, “just making sure that I’m staying on top of my job.” As OpenAI’s ever-growing number of employees get up to speed with Codex, Peng says, “the floor has just [been] raised for everyone, where people’s level of curiosity about a tool they previously would have been really intimidated by has just grown.” Personally, Peng is currently planning a trip to Korea, and used Codex to create an interactive interface where she could “kind of click on the different cities that we were visiting and see how far everything was from each other.” One popular method for learning how to get the most out of Codex, says Peng, is developing small games. Maybe you start by making a classic Snake game, she suggests, and then ask Codex to make the snake neon pink, and then turn the targets that the snake eats into digital strawberries. This kind of rapid iteration is fantastic for teaching people about the open-ended problems that Codex can solve. Katy Shi, a research lead on Codex, also creates games with Codex, but usually as a means of benchmarking the agent when new updates or models are released. One popular challenge is to see how Codex reacts when being tasked to make a first-person version of Tetris from the perspective of the piece. Part of Shi’s work involves crafting the personality of Codex, and finding a way to thread the line of being helpful without becoming too trigger-happy or overeager in its actions. To be sure, OpenAI isn’t the only company that is bringing the benefits of agentic coding to non-engineering work. In January, the company’s chief rival Anthropic released Claude Cowork, a feature on the company’s Claude desktop app that takes some of the abilities of its popular Claude Code product, and packages them in a more beginner-friendly user interface. Instead of creating two separate products for engineering and knowledge work, OpenAI has elected to make Codex adept at handling both types of tasks. Recently, OpenAI has begun adding some aspects of Codex back into ChatGPT, such as Workspace Agents in ChatGPT, which take the form of little animated characters that workers can assign to handle specific tasks, and then share among their organization. Eventually, the plan is for Codex and ChatGPT to merge into a single “super-app” that handles everything for you in a simple interface. One-off features like Workspace Agents are just a taste of what’s to come in the future. For now, Embiricos is laser-focused on making the Codex desktop app as good as it can possibly be, and those efforts are paying off. Since the February 2 launch of the Codex desktop app, OpenAI says, the app has grown to over 4 million weekly active users. At some point, Embiricos says, work will shift over to making the mobile version of Codex as useful as the desktop version, and then the need for desktops and laptops will eventually just go away. “I don’t even want to have to open my computer,” says Embiricos. It won’t be long before everyone in the world has a true “personal assistant on their phone.” BY BEN SHERRY @BENLUCASSHERRY

Google’s New Workspace Intelligence Is About to Be Your Ultimate AI Co-Worker

Some of the most popular work-focused apps of all time are about to become more intertwined than before, thanks to artificial intelligence. Google has announced Workspace Intelligence, a new semantic layer for its Google Workspace suite of products. The new capability enables Gemini-powered AI agents to understand and replicate the unique context of your workplace and job when you use products like Google Sheets and Google Docs. Yulie Kwon Kim, Google Workspace’s head of product, says that Workspace Intelligence is intended to emulate the institutional knowledge of a long-tenured employee, but for AI systems. “There are a lot of LLMs out there that are powerful and capable,” says Kim, “but they end up being generic. They don’t know your institutional history, how you like to format your professional voice and preferences.” The key to creating an actually-useful AI co-worker, says Kim, is to provide AI models with the “rich organizational context” found in a company’s documents, emails, presentations, and messages. Because Workspace Intelligence automatically scans your emails and messages, it can generate content that sounds as if it were written by you, and can transform its outputs to replicate your unique voice. With this context, Gemini will know to format a spreadsheet in Google Sheets the right way, for example, or know to never include exclamation points in your emails. Or maybe to include them all the time! With Workspace Intelligence, Kim says that conversations with Gemini will become more like running a “command center for your work,” enabling users to pull in data from sources including Google Drive, Gmail, and Sheets. Google says that the chat feature, now named “Ask Gemini in Chat,” will make it easier for workers to find files, schedule meetings with coworkers, and get recommendations for the first actions they should take after opening their laptop. In Sheets, Google’s web-based spreadsheet software, users can harness Workspace Intelligence to develop and edit spreadsheets based on data from Gmail or Google Drive. The tech has also enabled a new feature called Sheets Canvas, which enables Gemini to turn spreadsheets into dynamic mini-apps and dashboards. In Docs, Google’s cloud-based word processor, Workspace Intelligence can be used to add infographics to text documents, and to edit documents based on comments left by the user. To be sure, Google has already offered AI tools in different forms in Workspace. Over the past few years, Google has added windows for interacting with Gemini to Docs, Sheets, and Slides, but until now, those interactions didn’t have access to an organization’s data. Workspace’s apps currently include a button that brings up a Gemini chatbot that can make edits to your files, but until now, context from your larger organization wouldn’t be automatically pulled in. Workspace Intelligence is just one of many announcements that Google is making at Google Cloud NEXT, its annual conference in Las Vegas, and Kim says she used the semantic layer to prepare for her trip. To manage the “overwhelming” amount of slide decks, chats, emails, and docs getting thrown her way, Kim says, she has used Ask Gemini in Chat to develop briefings for all the customer meetings she has scheduled, along with separate briefings for her onsite internal team meetings. Workspace Intelligence is now available for all paid Google Workspace users. BY BEN SHERRY @BENLUCASSHERRY

Duolingo’s AI U-Turn Is a Warning for Other Companies

At many big companies these days, finding ways to use AI to do your job better isn’t a suggestion. It’s a requirement. As The Wall Street Journal recently reported, “From small startups to giants including Amazon.com, Alphabet, Google, and Meta Platforms, tech companies are measuring [AI use] with an eye on productivity gains and in certain cases factoring it into performance reviews.” Given the industry’s mad dash to realize the potential productivity gains of AI and keep ahead of the competition, leaders’ desperation to have employees embrace AI makes sense. But is tracking and scoring AI usage in performance reviews the best way to go about it? The experience of learning app Duolingo, as well as some fascinating recent research, suggests companies should think carefully about how they evaluate employees’ AI. The potential for unpleasant and unintended consequences is high. Duolingo’s AI U-turn Duolingo embraced AI early and enthusiastically, stirring controversy. So on a recent episode of the Silicon Valley Girl podcast, host Marina Mogilko wanted to dig into the details of the company’s AI push. She asked CEO Luis von Ahn to explain how Duolingo tracks and evaluates AI use as part of the performance review process. But von Ahn pushed back against the premise of the question. “For a while, it was part of performance reviews. We decided not to do that,” he clarified. Why the change of heart? “I sent a memo to the company that said, ‘Part of your performance review is going to be usage of AI.’ And we found that people were … kind of asking, ‘Do you just want us to use AI for AI’s sake?’” he explained. The focus on maximizing AI use over maximizing AI benefits wasn’t what Duolingo was after. Von Ahn changed course. “We said, ‘No, look, the most important thing in your performance is that you are doing whatever your job is as well as possible.’ A lot of times AI can help you with that, but if it can’t, I’m not going to force you to do that,” he said. “We backtracked from that because it felt like, rather than being held accountable for the actual outcome, we’re trying to just push something that in some cases did not fit.” Beware workslop Duolingo discovered that forced, performative AI use wasn’t actually benefiting anyone. Instead, it was creating AI showpieces to cite when performance review season rolled around again, and crowding out other, more impactful work in the process. Power to management for recognizing the problem and reversing course. But is this only the unique experience of one particular company? Or are other leaders likely to discover, as von Ahn did, that forcing AI usage creates time-wasting, resource-consuming distraction? Recent research from Stanford University and coaching platform BetterUp suggests the problems that cropped up at Duolingo are a danger that more managers need to consider. And they gave that danger a catchy name: workslop. You may have heard the word because it ricocheted around the internet once the researchers coined it. That instant popularity probably reflects how many of us recognized the widespread problem it describes—low-quality, AI-assisted output that forces others to spend time understanding, processing, and fixing it. Just how widespread is workslop? In an initial study, the researchers crunched some numbers and came up with a startling estimate. “Employees reported spending an average of one hour and 56 minutes dealing with each instance of workslop. Based on participants’ estimates of time spent, as well as on their self-reported salary, we find that these workslop incidents carry an invisible tax of $186 per month. For an organization of 10,000 workers, given the estimated prevalence of workslop (41 percent), this yields more than $9 million per year in lost productivity,” the researchers wrote on HBR. How Duolingo accidentally encouraged workslop Using AI to cut cognitive corners and/or impress the boss costs companies millions a year. It also annoys workers tremendously. And leaders, the researchers discovered in a subsequent study, are often guilty of accidentally making the problem worse with AI mandates like the one originally instituted at Duolingo. “Many leaders are facing pressure to make responsible investment decisions about AI in the face of uncertainty and macroeconomic pressures,” the researchers wrote in a second HBR article. “In response, leaders are using a blunt strategy, mandating that employees use AI broadly and quickly.” The predictable result of these less-than-well-thought-out AI mandates isn’t tech-driven productivity gains. It’s more workslop, more wasted time, and more frustrated employees. Better ways to get employees to use AI Bosses thinking of following the lead of tech giants like Meta and using brute force to compel teams to use AI more should take Duolingo’s experience as a warning. Everyone agrees that AI will ultimately have huge upsides for businesses. The stakes are high, and pressure is on leadership. But rushing out blanket AI mandates has serious downsides. So what should leaders do instead of one day announcing to workers that they’ll be evaluated on their AI use at their next performance review and hoping for the best? In their second HBR article, the researchers lay out a handful of suggestions. They include creating an atmosphere of trust where people can discuss their AI experiments honestly, warts and all, and investing in training and knowledge-sharing initiatives between employees. Some companies might even consider creating a position of “AI collaboration architect” to help employees figure out the best ways to deploy AI. EXPERT OPINION BY JESSICA STILLMAN @ENTRYLEVELREBEL

5 Lessons From an AI Startup That’s Quietly Disrupting a $30 Billion Industry

I’ve spent years writing about how entrepreneurs can leverage AI in their businesses and the non-obvious ways AI is changing the game. But I’ve been lucky enough to spend the last two decades surrounded by entrepreneurs who look at massive industries and ask one simple question: Why does this still work this way? My friend Trevor Sumner is one of those entrepreneurs. Trevor is the CEO of an AI company that’s shaking up the consumer market research industry—a space worth more than $30 billion that, until recently, still relied heavily on the same methods it used before the internet existed. Think focus groups, quarterly surveys, and PowerPoint decks that arrived months after the question was asked. As I’ve written before, your network is often worth more than your startup—and it was through my network that I first connected with Trevor and learned about what he’s building. Trevor’s company uses AI to analyze millions of real consumer signals online—social conversations, reviews, search behavior—and turns them into the kind of insights that used to take months and cost a fortune. And they’re growing fast: revenue up significantly, team quadrupled in a year, working with major global brands across 30-plus countries. But here’s what I find most interesting. The lessons from Trevor’s journey aren’t just about market research. They’re a blueprint for any founder trying to build a company in an industry being disrupted by AI. And let’s be honest—that’s almost every industry right now. Here are the five lessons that stood out to me. 1. Find the industry still running on fax machines Every industry ripe for disruption has a tell: the output is genuinely valuable, but the process is stuck in a different era. In market research, major brands desperately need consumer insights to make billion-dollar decisions. But the way those insights were generated hadn’t fundamentally changed in decades. Surveys designed before TikTok—or even the internet—existed. Reports delivered months after the question was asked. I see this pattern everywhere. When I was building Likeable Media in 2007, the advertising industry was still spending the majority of budgets on TV and print while consumers were spending their time on social media. The gap between how an industry operates and how the world actually works—that’s where the opportunity lives. The lesson: Look for industries where the process is visibly broken but the need is undeniable. That gap is where AI creates the most dramatic ROI. 2. Don’t sell AI—Sell the outcome AI makes possible This one is huge, and I see founders get it wrong all the time. Nobody signs a contract because they’re excited about your algorithm. They sign because you can deliver a result they couldn’t get before—faster, cheaper, or more reliably. Trevor told me that when his team pitches major brands, AI is never the headline. The headline is: What if you could understand what millions of consumers actually think about your brand—in real time, instead of waiting three months for a survey? The moment you make AI the hero of your pitch, you’ve invited a procurement committee to debate whether AI is ready, safe, or overhyped. When the outcome is the hero, the conversation shifts to: Can you deliver this result? That’s a much better meeting. I think about this with my own ventures. When Carrie and I built Likeable Media, we didn’t sell “social media management.” We sold the ability to turn your customers into your marketing department. The technology was the how. The outcome was the why. The lesson: Position the result, not the technology. AI is how you do it. The outcome is why they buy. 3. Your first five clients should scare you a little Trevor’s company didn’t start by landing small, safe clients to cut their teeth. They went straight after some of the biggest consumer brands in the world—and they did it before they’d even raised outside funding or built a formal sales team. That’s not recklessness. That’s strategy. I learned this lesson the hard way. Early in Likeable Media’s life, we spent too long working with small accounts that were easy to manage but didn’t push us to be better. It wasn’t until we landed bigger clients that our product, our team, and our confidence leveled up. Big logos validate your product, compress future sales cycles, and set your pricing floor permanently higher. The lesson: Don’t wait until you feel ready. Punch up. Your first five clients should stretch you and push your vision forward. 4. Context beats capability in a disrupted market Here’s something that keeps coming up in every AI-disrupted industry I watch: incumbents fight back by slapping the word “AI” onto their existing products. Traditional research firms are rebranding legacy tools as “AI-powered,” creating confusion for buyers who can’t tell the difference between a company built on AI and one that just bolted AI onto the side. But here’s what separates the winners from the noise: deep domain expertise. Anyone can access powerful AI models these days. Not everyone understands the problem well enough to apply AI in a way that actually matters. Trevor’s co-founders spent decades inside the world’s biggest consumer brands. They know how brand equity works, how category dynamics shift, what a CMO actually needs on their desk Monday morning. That kind of context can’t be replicated by fine-tuning a model. I see this as the single biggest differentiator for AI startups right now. The founders who win won’t necessarily have the most powerful technology. They’ll be the ones who understand their buyer’s world better than anyone else. The lesson: Anyone can access powerful AI. Not everyone understands the problem well enough to apply it. Domain expertise is your moat. 5. Build for the transition, not just the transformation This is the lesson I think most founders miss entirely. Enterprise clients aren’t going to abandon their existing tools and processes overnight—no matter how much better your solution is. Trevor’s company was designed to complement existing workflows first, and replace them over time. They even provide playbooks for managing the internal transition—helping their clients navigate change management and stakeholder buy-in. That patience, counterintuitively, accelerated their adoption. I think about this with my own ventures too. When you’re building something that asks people to change how they operate, you can’t just show up with a better mousetrap and expect everyone to switch. You have to earn the transition by meeting people where they are. The lesson: The boldest disruption often wins by moving slowly enough for the buyer to say “yes.” The AI gold rush is real, but the founders who win won’t just be the ones with the most powerful models. They’ll be the ones who found the broken process, led with the outcome, punched up early, earned domain trust, and respected the buyer’s journey. That’s the playbook. And from what I’ve seen, it works. EXPERT OPINION BY DAVE KERPEN, CEO, KERPEN VENTURES @DAVEKERPEN