JP Morgan Upgrades Tesla to Neutral, Lifts PT to $475
JP Morgan analyst Rajat Gupta upgraded Tesla from Underweight to Neutral with a price target of $475.00 (from $145.00).
“Following our recent assumption of coverage, we move to Neutral from Underweight on TSLA shares and establish a Dec 2027 PT of $475: TSLA is at the forefront of physical AI, entering uncharted TAMs, and their ability to execute will be key to accelerating adoption and increasing the size of these TAMs themselves (the Jevons paradox).
The unique advantage TSLA has, and unmatched at an industrial level scale, is the degree of vertical integration (and increasing further over time) across all the hardware + software products it builds, combined with the efficacy and speed of technology development.
We believe this aspect, while largely known at a high-level, is still somewhat under-appreciated and misunderstood, for the sheer starting-point advantage it brings.
Using cell and vehicle production factories as a test bed for Optimus/Humanoids should not only lower COGS for the base automotive business, but more importantly, help validate the product at an industrial scale (and also drive down its cost) for enterprise and commercial adoption (US/Global TAM of ~5 mn/~30 mn Humanoids by 2040E) — a classic flywheel effect, somewhat analogous to AWS and Kiva at AMZN.
Same with robotaxi — while data is already present (~10 bn miles recorded) + personal fleet scale (~9 mn on road today), rolling out AVs to more regions and cities, capturing more edge cases and improving the efficacy of camera-only vision should help generate significant network-effects across both robo-taxi adoption/ pricing, but also personal FSD adoption (~35 mn personal TSLA fleet and ~40 mn TSLA robotaxis by 2040E).
The ensuing returns on R&D and increased manufacturing efficiency (potential for ~5% reduction in COGS from Optimus) could then allow for significant pricing competitiveness in the base automotive business, providing optionality to re-accelerate EV adoption and supercharger utilization, with feedback loops into FSD and robotaxi adoption.”
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