Michael Nathanson explains that consumer demand for new AI-related hardware products, like wearables, is currently lacking, making it difficult for companies like OpenAI to succeed in this space. He also notes that established tech giants have strong market positions, which pose significant barriers for newcomers, and warns of potential overinvestment in AI leading to industry consolidation.
In the discussion, Michael Nathanson from MoffettNathanson highlights that consumer demand for new hardware products related to AI, such as wearable devices, is currently lacking. He emphasizes that despite some companies unveiling innovative gadgets, the market does not yet see a strong need or desire for these devices, making it challenging for new entrants like OpenAI to gain traction in the hardware space. Nathanson suggests that consumer needs and preferences are not aligned with the current offerings, which could hinder the success of such products in the near term.
Nathanson also discusses the competitive landscape among major tech giants, noting that companies like Apple, Google, Amazon, and Meta have established significant moats around their core businesses. He points out that OpenAI, which is not yet publicly traded, faces difficulties breaking into this entrenched ecosystem. The existing players have strong market positions and internal challenges, making it tough for newcomers to disrupt the status quo. He indicates that the current environment favors established companies with large, protected market shares.
The conversation shifts to the broader AI industry and the potential winners. Nathanson expresses a preference for Alphabet (Google’s parent company), which he considers the most attractive investment at the moment, despite Meta also being a strong contender. He reflects on the industry’s tendency for companies to operate in their own lanes, but notes that recent developments suggest increasing overlap and competition. Companies like Amazon, Alphabet, Meta, and Apple are expanding into new areas such as advertising, devices, and AI search, which could lead to intensified rivalry and a more crowded market.
Nathanson raises concerns about the industry’s investment strategies, warning that there may be overpaying for AI capabilities and related assets. He compares the current AI race to historical tech bubbles in telecom, media, and radio, where excessive investment eventually led to diminished returns and industry consolidation. He suggests that many companies are pouring money into AI without clear indications of sustainable profitability, which could result in a shakeout similar to past tech industry cycles.
Looking ahead, Nathanson predicts that scale will remain a critical factor in determining industry winners over the next decade. He believes that dominant players like Alphabet and Microsoft are likely to maintain their positions, while Apple’s future in AI appears more uncertain due to concerns about its slower pace of investment and weaker moat. Ultimately, he foresees a future where a few large companies will emerge as the primary beneficiaries of AI advancements, with smaller firms either consolidating or exiting the market as the industry matures.