Bank of America is holding firm on its bullish Meta stock target as the tech giant races to launch new AI models and custom chips. Investors now face a window of opportunity-and a list of real risks.
Bank of America maintains its bullish outlook on Meta, reaffirming a $810 price target even as the stock trades well below its 52-week high. This target implies a potential 32% upside from Meta's recent close near $614. However, the central focus is not only on the possible gains, but also on the rapid acceleration of Meta's AI initiatives and the associated risks.
Meta officially released Muse Spark 1.3 on September 2, 2026, making it available through Muse Code and the Meta Model API.
Muse Spark 1.3 distinguishes itself through its capacity to manage longer and more complex workflows within a single conversation. It can integrate context from multiple sources and follow detailed instructions with improved reliability. This capability is significant as Meta is developing a consumer AI agent, codenamed Hatch, which could become a flagship product if the underlying technology proves effective. While The Information first reported on Hatch, Meta has not yet announced a launch date or commercial name.
Meta described Muse Spark 1.3 as a model trained for long-horizon coding and agentic workflows, with improved reliability on complex engineering tasks and less verbose outputs. The company also stated that "max reasoning" capabilities would be introduced later, following additional safety testing.
From an investment perspective, Meta's valuation is notable. At approximately $617, the stock trades at about 18 times projected 2027 GAAP earnings and roughly 15 times 2028 estimates. Historically, Meta's price-to-earnings multiple has averaged near 21, while the S&P 500 is around 20. Bank of America's $810 target is based on a 24-times multiple for 2027 earnings-a premium the bank attributes to Meta's growth trajectory and the scale of its AI initiatives.
Nevertheless, the optimistic outlook is tempered by several risks. Bank of America identifies five key concerns: Meta's significant dependence on digital advertising, the impact of rising AI investment on profit margins, an expanding fixed cost base due to infrastructure development, increasing competition from AI-native platforms, and persistent regulatory threats-particularly regarding teen safety and litigation. Each of these factors has the potential to affect Meta's financial performance or delay its AI strategy.
Regulatory challenges are already material. Meta continues to face legal scrutiny, and any reduction in this pressure could provide further upside. As outlined in a recent Bank of America review, evolving regulatory frameworks in Washington could reshape innovation and risk for the entire sector.
Investors monitoring Meta should focus on several milestones: the commercial launch of Hatch, adoption rates for Muse Spark 1.3, and the commencement of MTIA chip shipments. The outcome of these developments will either support or challenge the bullish thesis. Meta's ability to deliver on these fronts will determine whether the stock approaches Bank of America's target or if the identified risks materialize.
Recent S&P Global data indicates that the S&P 500's average price-to-earnings ratio was 20.1 as of August 2026, with the technology sector averaging 27.4. Meta's current valuation is below both its historical average and the broader tech sector, reflecting market skepticism about the company's capacity to achieve its AI objectives.
Meta's commitment to developing proprietary AI models and custom chips represents a significant bet on self-sufficiency and long-term competitiveness. While the company is moving rapidly, speed alone does not ensure success. Investors are presented with an opportunity to acquire a leading technology stock at a discount relative to its own history and the market, but this discount is a direct reflection of substantial execution and regulatory risks. The coming six to twelve months will be critical in determining whether Meta's AI strategy delivers the anticipated results or if market caution proves warranted.
Custom AI chips such as Meta's MTIA are engineered to optimize both training and inference workloads, reducing reliance on external suppliers and potentially lowering long-term costs. For large technology firms, this strategy can create a structural advantage as AI demand increases, but it also entails significant upfront investment and exposes the company to supply chain and engineering risks. Investors should carefully weigh the potential for greater control and efficiency against the reality that not all custom chip initiatives achieve their intended outcomes, particularly in a rapidly evolving sector where today's innovation can quickly become obsolete.
