Tesla’s advancement in rolling out an unsupervised robotaxi fleet is expected to be the most significant driver of its stock performance in 2026, according to a note from Morgan Stanley released on Wednesday.
Analyst Andrew Percoco emphasized that Tesla’s ability to scale its robotaxi operations without human supervision represents the key catalyst for the company’s shares this year.
Morgan Stanley noted that recent discussions at a technology, media, and telecom (TMT) conference, along with a visit to Tesla’s Giga Texas facility, have made analysts more optimistic about the outlook for robotaxi development and Cybercab production. The start of production is still expected to begin in April.
Percoco highlighted that Tesla’s vertically integrated structure and innovative manufacturing approach for its Cybercab vehicles support strong unit economics. He added that Tesla is fundamentally transforming how vehicles are produced.
A major factor behind this optimism is the impact of robotaxi usage on Tesla’s Full Self-Driving (FSD) technology. Each additional mile driven by robotaxis contributes valuable data, accelerating improvements in the autonomy system.
Morgan Stanley explained that increased unsupervised driving data strengthens Tesla’s FSD model, which could lead to higher adoption rates among customers and help boost vehicle demand. This, in turn, may support stronger cash flow generation over time.
The bank also identified several upcoming milestones. Tesla is expected to unveil its Optimus Gen 3 humanoid robot in the coming months, with production planned for the second half of 2026. Meanwhile, Tesla’s energy storage business continues to expand, although margins may face pressure this year due to increased competition and the impact of tariffs.
Despite higher capital spending and an estimated near-term cash burn of around $8 billion, Morgan Stanley believes that continued progress in personal FSD technology will play a crucial role in driving auto sales, improving margins, and supporting Tesla’s long-term ambitions in physical artificial intelligence.
