Google explores AI consciousness after Blake Lemoine’s claims
Four years after firing an engineer who said LaMDA was sentient, Google DeepMind is now hiring philosophers and psychologists to study whether AI can be conscious
In June 2022, Google placed engineer Blake Lemoine on administrative leave for claiming that LaMDA, the company’s large language model, showed signs of sentience. The company called his assertions unfounded. He was eventually fired.
Four years later, Google DeepMind is actively hiring philosophers, psychologists, and ethicists to research the very question Lemoine raised.
Google isn’t alone. Anthropic and Meta have also committed to studying machine consciousness as of mid-2026, marking a dramatic pivot from an industry that spent years treating the topic as somewhere between fringe science and PR liability.
From heresy to hiring spree
A March 2026 paper from Google DeepMind, authored by researcher Alexander Lerchner, actually argued the opposite of what Lemoine claimed. The paper asserted that AI systems can simulate consciousness but will never truly achieve it, citing what Lerchner called the “abstraction fallacy.”
The Financial Times reported on June 1, 2026, that leading AI labs are studying machine consciousness and even exploring potential rights for AI systems. A Washington Post article from July 1, 2026, framed this wave of research as a public embrace of consciousness studies by major tech companies.
The labs aren’t just bringing in more machine learning engineers for this work. They’re recruiting from philosophy departments, cognitive science programs, and ethics institutes.
What this means for investors
When major tech companies start publicly researching whether their products might be conscious, regulators tend to pay attention. If AI labs begin acknowledging even the possibility that advanced systems could have morally relevant experiences, that opens the door to entirely new categories of regulation.
For the crypto space specifically, AI and blockchain technologies have been converging in various applications, from AI-powered trading protocols to decentralized computing networks that provide GPU resources for model training. If AI development faces new ethical constraints or regulatory requirements, that ripple effect could reshape how AI-integrated crypto projects operate.