(Reuters) -Tesla rallied 6% on Monday after Morgan Stanley said its Dojo supercomputer could power a near $600 billion surge in the electric-car maker's market value by helping speed up its foray into robotaxis and software services.
Tesla (NASDAQ:TSLA), already the world's most valuable automaker, started production of the supercomputer to train artificial intelligence (AI) models for self-driving cars in July and plans to spend more than $1 billion on Dojo through next year.
Dojo can open up new addressable markets that "extend well beyond selling vehicles at a fixed price," Morgan Stanley analysts led by Adam Jonas wrote in a note on Sunday.
"If Dojo can help make cars 'see' and 'react,' what other markets could open up? Think of any device at the edge with a camera that makes real-time decisions based on its visual field."
The Wall Street brokerage upgraded Tesla's stock to "overweight" from "equal-weight" and replaced Ferrari (NYSE:RACE)'s U.S.-listed shares with it as "top pick".
Morgan Stanley raised its 12-18 month target on Tesla's shares by 60% to $400 - the highest among Wall Street brokerages, as per LSEG data - which, it estimated, would give the EV maker a market capitalization of about $1.39 trillion.
That is about 76% higher than Tesla's market value of about $789 billion, based on the stock's close of $248.5 on Friday. The stock climbed about 5.7% to $262.70 on Monday.
Jonas expects Dojo to drive the most value in software and services.
Morgan Stanley raised its revenue estimate for Tesla's network services business to $335 billion in 2040 from $157 billion earlier.
Jonas expects the unit to account for more than 60% of Tesla's core earnings by 2040, nearly doubling from 2030.
"This increase is largely driven by the emerging opportunity we see in third-party fleet licensing, increased ARPU (average monthly revenue per user)," the analyst said.
Tesla's 12-month forward price-to-earnings ratio of 57.9 is well ahead of legacy automakers Ford (NYSE:F) at 6.31 and General Motors (NYSE:GM) at 4.56, according to LSEG data.