\n
![](\"https:\/\/images.ctfassets.net\/jdtwqhzvc2n1\/4Glss6wFaZ4by6Tf3rOX9S\/262f49bd913d24e09837c14557fa3016\/u7277289442_Sophsticated_AI_bots_sit_alongside_humans_at_mode_c6808a94-8ddf-4f76-8761-a77d9b192a0f_3.png\")
<\/p>\n
\n<\/p>\n
In the race to automate everything \u2013 from customer service to code \u2013 AI is being heralded as a silver bullet. The narrative is seductive: AI tools that can write entire applications, streamline engineering teams and reduce the need for expensive human developers, along with hundreds of other jobs.\u00a0<\/p>\n
But from my point of view as a technologist who spends every day inside real companies\u2019 data and workflows, the hype doesn\u2019t match up with the reality.\u00a0<\/p>\n
I\u2019ve worked with industry leaders like General Electric, The Walt Disney Company and Harvard Medical School to optimize their data and AI infrastructure, and here\u2019s what I\u2019ve learned: Replacing humans with AI in most jobs is still just an idea on the horizon.\u00a0<\/b><\/p>\n
I worry that we're thinking too far ahead. In the past two years, more than a quarter<\/u> of programming jobs have vanished. Mark Zuckerberg announced<\/u> he is planning to replace many of Meta\u2019s coders with AI.\u00a0<\/p>\n
But, intriguingly, both Bill Gates and Sam Altman have publicly warned<\/u> against replacing coders.\u00a0<\/p>\n
Right now, we shouldn\u2019t count on AI tools to successfully replace jobs in tech or business. That\u2019s because what AI knows is inherently limited by what it has seen \u2013 and most of what it has seen in the tech world is boilerplate.<\/p>\n
Generative AI models are trained on large datasets, which typically fall into two main categories: publicly available data (from the open internet), or proprietary or licensed data (created in-house by the organization, or purchased from third parties).\u00a0<\/p>\n
Simple tasks, like building a basic website or configuring a template app, are easy wins for generative models. But when it comes to writing the sophisticated, proprietary infrastructure code that powers companies like Google or Stripe, there\u2019s a problem: That code doesn\u2019t exist in public repositories. It\u2019s locked away inside the walls of corporations, inaccessible to training data and often written by engineers with decades of experience.<\/p>\n
Right now, AI can\u2019t reason<\/u> on its own yet. And it doesn\u2019t have instincts. It\u2019s just mimicking patterns. A friend of mine in the tech world once described large language models (LLMs) as a "really good guesser."\u00a0<\/p>\n
Think of AI today as a junior team member \u2014 helpful for a first draft or simple projects. But like any junior, it requires oversight. In programming, for example, while I\u2019ve found a 5X improvement for simple coding, I\u2019ve found that reviewing and correcting more complicated AI-produced code often takes more time and energy than writing the code myself.\u00a0<\/p>\n
You still need senior professionals with deep experience to find the flaws, and to understand the nuances of how those flaws might pose a risk six months from now.\u00a0<\/p>\n
That\u2019s not to say AI shouldn\u2019t have a place in the workplace. But the dream of replacing entire teams of programmers or accountants or marketers with one human and a host of AI tools is far premature. We still need senior-level people in these jobs, and we need to train people in junior-level jobs to be technically capable enough to assume the more complex roles one day.\u00a0<\/p>\n
The goal of AI in tech and business shouldn\u2019t be about removing humans from the loop. I\u2019m not saying this because I\u2019m scared AI will take my job. I\u2019m saying it because I\u2019ve seen how dangerous trusting AI too much at this stage can be.\u00a0<\/p>\n
Business leaders, no matter what industry they\u2019re in, should be aware: While AI promises cost savings and smaller teams, these efficiency gains could backfire. You might trust AI to perform more junior levels of work, but not to complete more sophisticated projects.\u00a0<\/p>\n
AI is fast. Humans are smart. There\u2019s a big difference. The sooner we shift the conversation from replacing humans to reinforcing them, the more we\u2019ll reap the benefits of AI.\u00a0<\/p>\n
Derek Chang is founding partner of<\/i> <\/i>Stratus Data<\/u><\/i>. <\/i><\/p>\n