In 1987, Apple released a four-minute vision of computing that was nearly 40 years ahead of its time. <\/p>\n\n\n\n
The video, named Knowledge Navigator<\/a>, showed a professor at his desk talking to a digital agent on a tablet-style computer. The agent surfaced new research, reminded him of an upcoming lecture he was scheduled to deliver, and dialed in a colleague to help with the material. It moved between modes without being asked. Voice when voice made sense. Screen when screen made sense.<\/p>\n\n\n\n Today, work still happens largely from app to app. You open a dashboard to check last quarter\u2019s numbers or fire up a spreadsheet to project this month\u2019s sales. Each is a destination you visit to get something done.<\/p>\n\n\n\n But thanks to agentic AI<\/a>, work increasingly happens within the tool you\u2019re already using. Ask a question, crunch the numbers, produce a chart, all without opening separate programs to do so. <\/p>\n\n\n\n If you\u2019re working in, say, Slack, WhatsApp, or Telegram, the open-source agents OpenClaw and Hermes carry out their instructions there, where you already are. Claude Code goes further by generating the interface itself. That matters because a machine that builds interfaces on demand can tailor them to an individual, then discard them when the task is done: bigger letters for the visually impaired, an app in someone\u2019s native language, a voice interface for someone whose hands are busy, a simpler view for new hires that fills in as they demonstrate fluency.<\/p>\n\n\n\n This shift changes the app from a stand-alone destination to a capability that can surface wherever and whenever it\u2019s needed. The software still exists. But increasingly it reaches users through agents, conversations, and workflows rather than requiring them to seek it out.<\/p>\n\n\n\n Instead of navigating menus and dashboards, an agent talks straight to the data and the software that runs it.<\/p>\n<\/blockquote>\n\n\n\n What that looks like in practice is something every business leader needs to start thinking about now: how employees will talk to machines, where work will reach them, and what it will look like to work a job in the years ahead. <\/p>\n\n\n\n Sometimes the deepest value of software is the interface itself. More often, though, it\u2019s the data underneath and the business logic that acts on that underlying information \u2014 the algorithms in a spreadsheet, say, that turn numbers into a forecast. <\/p>\n\n\n\n AI agents<\/a> don\u2019t need the same kind of doorway that humans do. Instead of navigating menus and dashboards, an agent talks straight to the data and the software that runs it. In that scenario, the app\u2019s menus and dashboards slide into the background; the user\u2019s interface is the agent itself, on whatever screen they happen to be looking at. What comes back to you is the completed task or a decision that still needs a human response, delivered through whatever channel you\u2019re already using.<\/p>\n\n\n\n \u201cThe interface is melting<\/a>,\u201d product designer Andrew Sims wrote in a Substack post making the rounds among AI UX designers. Increasingly, the screen becomes a layer for oversight while AI handles execution. What takes its place as the user\u2019s primary interface is the agent itself: knowing what to ask, what it can do, and what to trust it with. <\/p>\n\n\n\n A manager approves an expense report from inside a chat window without opening the expense app. An analyst pulls sales data into a presentation without logging into the dashboard that stores it. The underlying systems still matter. But they increasingly recede into the background while AI handles the navigation between them.<\/p>\n\n\n\n As AI takes over more execution, the interface becomes less the place where work happens and more the place where work is supervised, reviewed, and governed.<\/p>\n\n\n\n Ubiquitous computing is a long-held dream. In the 1960s, a Star Trek<\/em> officer needed only to speak and the ship\u2019s computer answered. Sensors in Bill Gates\u2019s home, completed in the late 90s, read a pin clipped to each person\u2019s clothing and adjusted the lighting, music, and art accordingly. We have been imagining computers that fade into the background for almost as long as we have had computers.<\/p>\n\n\n\n The difference now is that the pieces are finally showing up in products people use every day, starting with our ability to talk to the machine. Boris Cherny, the creator of Claude Code, has talked about using the voice-to-text AI tool Wispr Flow<\/a> to communicate with his computer. When he typed a prompt, he tidied his thoughts before hitting enter. When he spoke, however, he rambled, stammered, doubled back \u2014 yet the results improved. Freed from the instinct to clean things up, Cherny gave the machine more of his actual thinking, which gave the AI more to work with.<\/p>\n\n\n\n What Cherny noticed on his own is already working in the field. Water treatment inspectors <\/a>at a Salesforce customer Workdry Group used to spend two to four hours after each plant visit writing up their compliance surveys. Now, while doing their rounds in a noisy plant and wearing safety gloves, they can simply speak what they are seeing, out of order, with pauses, and in industry shorthand. Salesforce\u2019s Voice to Form<\/a> routes each spoken detail into the proper field. The job can now be finished onsite in 20 minutes. <\/p>\n\n\n\n The next step is real-time conversations. The industry is building models that process speech in real time, the way a colleague in the room would. That\u2019s in contrast to current chatbots, which are turn-based: You talk, the bot responds, you talk again. Real human conversation is messier: People talk over each other, trail off, double back, and throw in false starts and tangents. Continuous voice turns the machine from a tool you must invoke into something simply present, listening, and attending.<\/p>\n\n\n\n\n
Demoting the interface<\/strong><\/h2>\n\n\n\n
Talk as the new interface<\/strong><\/h2>\n\n\n\n