On a recent call with the Yahoo Finance product chief, I pulled up a slide that looked like two burgers, side by side, to represent eras of work before and after AI. Leimer leaned in. \u201cIt\u2019s totally that,\u201d he said.<\/p>\n
The slide came from Arvind Narayanan, a Princeton computer scientist who just keynoted the International Conference on Machine Learning in Seoul. In his illustration, the burger on the left, labeled \u201cTraditional,\u201d shows a thick patty \u2014 the execute layer of work, the coding and debugging \u2014 sitting between modest buns. On the right, labeled \u201cWith AI,\u201d the patty has shrunk to a sliver. The buns \u2014 \u201cdecide\u201d on top and \u201cdeliver\u201d on the bottom \u2014 have swollen in size. <\/p>\n
Work used to be a fat hamburger with skinny buns, as the meat of the work was the actual doing of the task. Now, AI is collapsing the middle of the sandwich, but the top and bottom layers of human work are growing instead of disappearing. Work is increasingly talking about work \u2014\u00a0there\u2019s a lot of bun to chew over.<\/p>\n
\u201cWe come in and talk to each other and we make sure that we really have real confirmation and conviction about, like, these are the things we\u2019re going to do. And this is how we\u2019re going to unleash the agents.\u201d<\/p>\n
Leimer\u2019s team led the development of a new investing product, AlphaSpace, that has been live in the market for about two months, as Fortune<\/em> was first to reveal. In that short window, the team has shipped over 100 new features, illustrating how the execute layer has accelerated. Users are spending 3x more time in AlphaSpace than the average Yahoo Finance user, Leimer told Fortune<\/em>, and the team has been able to respond quickly to user feedback, like its recent addition of real-time options data, in a partnership with Unusual Whales.<\/p>\n Sprints that used to run two weeks now run 24 hours, Leimer said: planning in the morning, shipping by end of day, retrospective at night. In one case, feedback arrived at 4:45 p.m. on a Friday; by 8:30 the same evening it was fixed, reviewed, and live.<\/p>\n \u201cIn the old days,\u201d Leimer added, \u201cthat would have been, \u2018We can\u2019t launch it next week.\u2019\u201d<\/p>\n Narayanan calls his framework the \u201cdecide\u2011execute\u2011deliver sandwich.\u201d Most knowledge work, he argues, has three layers: a decide layer on top, focused on understanding what needs to be built and why; an execute layer in the middle that represents the actual implementation; and a deliver layer on the bottom, covering integration, testing, and long\u2011term accountability.<\/p>\n AI agents are compressing the middle layer. But Narayanan\u2019s point is that the execute slice was only ever about a third of the job. The buns, he says, are not just holding steady; they\u2019re expanding.<\/p>\n \u201cThe first and third layers are arguably expanding as AI compresses the middle layer,\u201d he told the ICML audience \u2014 because once building gets cheap, it becomes easier to start projects and harder to keep up with deciding and verifying them.<\/p>\n For years, product squads at big tech firms were defined by what each person couldn\u2019t do. Designers sketched static mockups and waited for engineers to turn them into working code. Product managers drafted requirements but needed developers to stress\u2011test a hunch. Analysts understood data but didn\u2019t build production systems.<\/p>\n As AI automates more of the \u201csyntax\u201d of building software, Yahoo teams say those lanes are disappearing.<\/p>\n On the team behind Yahoo Scout, the company\u2019s AI answer engine, principal product designer Nick Lockington began prompting Google\u2019s Vertex AI directly, asking it to generate structured JSON logic for new features. An engineer on the team later remarked that Lockington had effectively created a functional API by prompt, bypassing the traditional build phase.<\/p>\n \u201cIt was a total \u2018aha\u2019 moment,\u201d said David Grandinetti, a distinguished software apps engineer on the project, on Yahoo\u2019s blog. \u201cNick became immediately the most leveraged engineer on our team, because he was working at the highest level of abstraction.\u201d<\/p>\n Other team members followed. One designer prolific in code commits became known internally as a \u201cdesign engineer.\u201d An engineer with a strong eye for usability was dubbed an \u201cengineering designer.\u201d<\/p>\n Yahoo\u2019s leaders say the point isn\u2019t that job titles vanish, but that roles are increasingly defined by the decisions people own rather than the code they can manually produce. Designers still arbitrate user behavior and aesthetics. Engineers still own architecture and safety. But both can now build and iterate directly with AI.<\/p>\n \u201cThis is about increasing decision velocity and cutting the cost of experimentation, so teams can test a hundred ideas instead of five and quickly discard failures without the traditional burden of technical debt,\u201d said Stephane Koenig, vice president at Yahoo. \u201cIt allows us to reserve human expertise for the hardest decisions where you need context AI doesn\u2019t have.\u201d<\/p>\n Leimer put it another way, in conversation with Fortune<\/em>: work is now primarily a conversation about work. \u201cWe come in and talk to each other and we make sure that we really have real confirmation and conviction about like, these are the things we\u2019re going to do.\u201d<\/p>\n The danger, he added, and why you have to be \u201creally thoughtful\u201d on the top part of the bun of work now, is that AI makes it so easy to \u201cjust build whatever you want.\u201d<\/p>\n Leimer repeated some advice from his manager, Yahoo Media Group President Ryan Spoon: \u201cIt\u2019s important that you\u2019re really convicted about what you build,\u201d because you can build anything, almost instantly. \u201cIt used to be [that you] spent so much time on the top of the bun because the engineering resources were precious. You didn\u2019t want to waste one second of an engineer\u2019s time.\u201d Now the risk is that you build things you don\u2019t need to, and do it instantly \u2014\u00a0McKinsey\u2019s global tech and AI leader Kate Smaje calls this the \u201cfalse productivity\u201d trap, as she told me several months ago.<\/p>\n Leimer added that when he sees sloppy work, what\u2019s become known as \u201cAI slop\u201d out in the world, is \u201cwhere there\u2019s a really skinny bun, basically.\u201d Humans haven\u2019t been thoughtful enough about what they\u2019re putting out as work product, because the hamburger\/sandwich section has been compressed down to almost nothing.<\/p>\n There are real costs attached to this. Professionals have coined the phrase \u201cworkslop,\u201d for when an AI-generated email or work product has been sent over for review without proper verification first. Experts in the Harvard Business Review<\/em> have argued that this is more than annoying \u2014\u00a0it\u2019s actually destructive to productivity. The Stanford Social Media Lab estimated the cost of this at $9 million per year for a company of 10,000 workers. <\/p>\n Taken together, these stories reinforce Narayanan\u2019s assessment that AI is, for now, more of a collaboration technology than an automation engine. He believes most AI agents still struggle with reliability in high-stakes environments, pushing companies toward human-in-the-loop rather than automation.<\/p>\n At Yahoo, leaders say this shows up as decision fatigue rather than job loss. Because AI lets teams test and build far more features, the limiting factor is how many choices product managers, designers, and engineers can thoughtfully make and shepherd.<\/p>\n Lockington noted that AI can\u2019t solve the messiness of overlapping or layered roles in the workplace. \u201cTraditionally, you might have someone ask, \u2018Why is this engineer doing design work?\u2019 or \u2018Why is this designer doing my job?\u2019\u201d He credited Koenig with doing the difficult human job of creating psychological safety on the team to navigate these issues. \u201cWe\u2019ve been able to break that down.\u201d<\/p>\n Leimer said that once this safety is established, he\u2019s found work to be \u201creally fun,\u201d a shift from his last 20 years in management, away from the actual building and writing code that he started out doing. \u201cNow, everybody\u2019s got the cool job\u201d where they feel like they play a role in building things, he said. \u201cIt\u2019s also been fun watching people get energized around the problem and the thing they\u2019re building as opposed to spending more of their time on the process to get it built.\u201d<\/p>\n Narayanan cautions against assuming jobs will remain unchanged. He said he expects decades of structural adaptation as companies reorganize around AI, akin to the 40\u2011year transition factories underwent when electricity enabled assembly lines.<\/p>\n So far, though, the empirical pattern in fields like software, law, medicine, and content translation is that demand for human work shifts and grows as AI spreads. Radiologists have adopted AI while employment rises. Lawyers find themselves filing more suits thanks to easier drafting. Translators still have steady work years after computer translation approached human parity, because there is no ceiling on what can be translated or into how many languages.<\/p>\n Yahoo\u2019s experience suggests that white\u2011collar work may be headed for a similar, if messier, future. As technical barriers fall, the company\u2019s leaders say personal agency and judgment are becoming the differentiators: not who can write code, but who can decide which problems are worth solving and how to verify what AI builds.<\/p>\n I asked Leimer to really torture the metaphor and invoked his Philadelphia roots by bringing up that city\u2019s famous sandwich: the cheese steak. Everyone knows the secret ingredient in a cheese steak isn\u2019t really the meat, it\u2019s the delicious Italian hoagie bread. Is that the point for the future of work?<\/p>\n The major bottleneck, he added, is the \u201cbottom bun,\u201d the delivery part. The work is done for you, almost instantly, after you decide what to do, and then delivery is just fiendishly difficult. \u201cYou\u2019ve got to work through all the other stuff, you\u2019ve got to review it,\u201d he said, citing security risks, building out a deployment pipeline, all when the work seems like it\u2019s finished and could be released with just the press of a button. <\/p>\n Is the promised land some kind of pizza or flatbread, where all the human work is about decision, all the tasks shrink down to the size of toppings, and the delivery is seamless? \u201cSomething like that,\u201d Leimer laughed.<\/p>\n In Narayanan\u2019s hamburger, AI has already made the patty thin. The bun is where the jobs \u2014 and the risks \u2014 now live. Whether companies can redesign work into something flatter and more sustainable, is the question for the coming years, even decades, to answer.<\/p>\n<\/div>\n #skinny #hamburger #fat #bun #problem #future #work #pizza<\/p>\n","protected":false},"excerpt":{"rendered":" On a recent call with the Yaho… <\/p>\n","protected":false},"author":1,"featured_media":910,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[1533,535,1532,506,1531,1534,812,1530,1248,1529],"class_list":["post-909","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-finance-news","tag-bun","tag-disruption","tag-fat","tag-future","tag-hamburger","tag-pizza","tag-problem","tag-skinny","tag-work","tag-yahoo"],"yoast_head":"\nThe decide\u2011execute\u2011deliver sandwich<\/h2>\n
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<\/div>\n<\/figure>\nWhen anyone can build, roles blur<\/h2>\n
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<\/div>\n<\/figure>\nDecision fatigue is the new risk<\/h2>\n
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<\/div>\n<\/figure>\nThe hamburger\u2019s future \u2014 and the case for pizza<\/h2>\n