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Rohit Prasad becomes Boston Dynamics chief executive

Rohit Prasad became Boston Dynamics chief executive on 7 October 2026, taking charge as Hyundai plans to deploy 25,000 Atlas robots across its Kia factories and other global plants from 2028.

AI & modelsAnalysisGrace OkonkwoPublished: 5 November 20264 min readSources 11
Rohit Prasad becomes Boston Dynamics chief executive

Prasad became chief executive on 7 October 2026, the company said. He took over from Robert Playter, who stepped down in February 2026 after six years as chief executive and more than 30 years at the company. Chief financial officer Amanda McMaster served as interim chief executive during the search. Playter was only the second chief executive in Boston Dynamics' more than three-decade history.

For buyers and competitors, the question is whether a celebrated research shop can become a supplier that ships at volume. Hyundai's own plants are the first customer: the group plans to deploy 25,000 Atlas robots across its Kia factories and other global plants, with an initial rollout in 2028. That is five-sixths of the 30,000-a-year output Boston Dynamics has targeted for 2028.

Prasad spent 12 years at Amazon, where he rose to senior vice president and head scientist for Alexa and artificial general intelligence. He helped build Alexa into a service used in hundreds of millions of households. He later led development of the Amazon Nova family of foundation models, used by several global organizations. Before Amazon he spent 14 years at Raytheon BBN Technologies, running machine-learning research for US government agencies and commercial clients. Business Chief reported that he has lived in the Boston area for more than 25 years. Boston Dynamics said he is expected to join its board, subject to an approval process.

Jaehoon Chang, chair of the Boston Dynamics board and vice chair of Hyundai Motor Group, cited Prasad's "proven ability to turn AI into products at global scale". He said that skill would combine with Hyundai's manufacturing, logistics and mobility operations. Prasad said in his own statement that the company is "uniquely positioned to advance Physical AI through its world-class robotics expertise".

Hyundai's factory plan sets the deadline

Hyundai became the sole owner of Boston Dynamics after buying SoftBank's remaining shares in July. It had taken majority control in 2021. In January the group announced a target to build a factory capable of producing 30,000 robots a year by 2028. Kia separately has a $33bn plan to expand in electric vehicles and robotics.

Boston Dynamics told investors it aimed to make 30,000 humanoid robots by 2028. Of those, only 5,000 would be retail products and 25,000 would work in Hyundai's and Kia's own automotive factories. That captive customer shields Boston Dynamics from having to win outside buyers early, and it means the first verdict on Atlas will come from Hyundai's own production lines.

The gap between viral videos and factory shifts

Semafor reported in May that Boston Dynamics was making only four Atlas robots per month. That is roughly 48 a year, against a stated target of 30,000 a year by 2028. Former employees told the outlet they were under pressure to integrate tens of thousands of working humanoids into Hyundai's automotive plants over the next few years.

Playter's exit was followed by the departures of chief technology officer Aaron Saunders and the chief operating and chief strategy officers, Semafor reported. Boston Dynamics began pilot training at the Robotics Metaplant Application Center at Hyundai's electric vehicle plant outside Savannah, Georgia, in June, and opened a robotics center there in September.

Boston Dynamics built its reputation on demonstrations. Gizmodo noted that the company staged a humanoid robot performance at CES earlier this year, presenting itself as "the sane, stable alternative to Tesla and its outlandish claims". A factory contract is judged on uptime, cycle time and cost per unit, not on a video.

What Atlas can do, and what it costs

A Boston Dynamics press release says one Atlas robot can reach objects up to 7.5 feet high and lift 110 pounds. Those are specifications, not productivity figures. The company has published neither an official production cost nor a final retail price for the machine.

Expert estimates reported by the Korea Herald put the build cost of one Atlas at $130,000 to $140,000. The same estimates say unit cost would fall to $30,000 once the company has made 50,000 of them, which on the current schedule would be well after 2028. The distance between those two figures is the commercial case for volume.

What happens next

Prasad's board seat depends on an approval process the company has not put a date on. The first Atlas deployment at Hyundai and Kia plants is scheduled for 2028. Boston Dynamics says it wants to begin exploring use cases for other industry sectors next year.

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Sources

11
  1. 01Amid Its Big Humanoid Robot Rollout, an Ex-Amazon AI Chief Will Now Lead Boston DynamicsEN
  2. 02Boston Dynamics Appoints Former Amazon AI Leader as CEOEN
  3. 03Who is Rohit Prasad? The Indian-origin former Amazon AI executive now leading humanoid robot maker Boston DynamicsEN
  4. 04Boston Dynamics hires former Amazon AI leader as CEOEN
  5. 05The Robot in Unitree’s New Ad Looks Ready to Commit—and Fight—Crimes - GizmodoEN
  6. 06Xiaomi’s humanoid robots work a full 8-hour shift in its EV factoryEN
  7. 07TechCrunch Founder Summit 2026: Doors open at 9 a.m. ET in BostonEN
  8. 08Learning Implicit Solves for Dynamic SimulationsEN
  9. 09Attraction-Ranking Mismatch in Out-of-Distribution Detection: Theory and Evidence from Hamiltonian Feature DynamicsEN
  10. 10Churchill Capital XI shareholders approve $2.5B Agility Robotics humanoid SPACEN
  11. 11FCC names new Space Bureau ChiefEN

All figures and quotations in this text come from the sources listed below.

Content prepared by the editorial team with AI assistance.

Grace Okonkwo

Grace Okonkwo

AI, models and technology

Grace Okonkwo covers AI, models and technology for FLASH24, working from primary sources such as model cards, API documentation and benchmark papers rather than vendor summaries. She checks training data provenance, evaluation conditions and reported scores against the underlying datasets before any figure reaches print. She interviews researchers and engineers directly, tracks release calendars from major labs, and compares successive model versions on the same tests. Her own self-hosting, home-network and documentation-reading habits feed straight into that desk, since she tests tools on her own hardware first. She does not publish benchmark claims without a reproducible method.

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