For years, the artificial intelligence race obsessed over one thing: raw computational muscle. Labs competed to train larger neural networks, ingest trillions of tokens, and shatter benchmark records.
Inside Google DeepMind, however, the executive tasked with scanning the horizon says the biggest obstacle ahead isn't code or computation—it’s how human society absorbs it all.
"You have to start using AI yourself and create the time and space to do it," says Lila Ibrahim, Google DeepMind’s newly appointed Chief AI Readiness Officer. "You don't have to be the smartest in the room."
Ibrahim—an Intel engineering alum and former Coursera president who ran DeepMind’s day-to-day operations as COO for six years—just stepped into a role that doesn't look like typical Silicon Valley management. Her job isn’t to train next-generation models or stress-test software vulnerabilities. Instead, she’s focused on the messy outside world: figuring out what happens when schools, hospital systems, boardrooms, and frontline workers are forced to deal with frontier technology.
It's a blunt, candid admission from an elite AI lab: these systems are advancing far faster than human institutions know how to handle.
Safety vs. Readiness: The Overlooked Half
Tech companies have poured hundreds of millions into "AI safety." But as Ibrahim points out, safety and readiness are completely different problems.
Safety is an engineering puzzle. It belongs to red-team hackers, synthetic data filters, alignment evaluations, and mathematical guardrails designed to keep a model from producing toxic advice or weaponized biology code.
Readiness, on the other hand, is an organizational headache.
You can build a technically safe diagnostic model that hits 99% accuracy in clean lab tests. But if hospital IT systems can’t ingest its output, doctors don't trust its probabilistic recommendations, and insurance providers refuse to create billing codes for it, the software just sits on a shelf.
That operational gap is where Ibrahim spends her days. Having signed industry statements alongside DeepMind co-founder Demis Hassabis warning of existential risks, she makes clear that she wouldn’t have taken the job if she didn't believe the upside would ultimately beat the chaos. But seeing that upside means organizations have to stop sitting on their hands, waiting for the technology to settle down.
"It will not settle," she warned in an appearance on CNBC's Changemakers. "What you can control is how ready you are."
The Three Questions DeepMind Asks Its Own Teams
For professionals worried that an algorithm is coming for their job, Ibrahim’s advice is surprisingly practical: roll up your sleeves and use the tools today rather than worrying from the sidelines.
Inside DeepMind, she instituted a straightforward routine built around three simple questions managers ask their staff:
How are you using AI right now? Pinpointing daily workflows and easy productivity wins
What would help you use it more? Finding institutional roadblocks, technical gaps, or tool shortages.
Where are you not using AI?
That third question, Ibrahim insists, is where your real value lives. The work you deliberately keep away from machines shows where human judgment, empathy, and gut instinct still matter.
"AI can help automate all of my tasks, but it cannot automate my Tuesdays," Ibrahim quipped. Her point: software can summarize an inbox, clean up a slide deck, and transcribe meeting audio, but it can’t handle the messy interpersonal drama, team morale, and ethical calls that define a leader’s actual workday.
The Classroom Blind Spot
Outside the office, Ibrahim’s biggest worry is what’s happening to younger generations. As the mother of two teenagers, she thinks school districts trying to ban large language models or treating them strictly as plagiarism engines are making a massive mistake.
Banning the tools guarantees kids will meet them unprepared. Instead, Ibrahim wants tech companies and educators working hand-in-hand to teach practical AI literacy early on. If kids don’t understand how these probabilistic systems work—including where they fail, hallucinate, and carry bias—they won’t run their own careers; they’ll just become passive consumers fed by someone else’s algorithm.
Preparing for frontier models can’t be a spectator sport. By turning a top operational leader into its readiness czar, DeepMind is admitting that engineering superhuman software is only half the battle.
If governments, schools, and workers get left behind, the smartest algorithms in the world will just create chaos faster. The future is arriving whether we like it or not; whether the rest of us can handle it is what Ibrahim is trying to solve.
