“AI is progressing a little more slowly than many expected, but it’s still a long-term threat to Apple. This is going to be very serious over the long term.” This scathing assessment from big tech founder Alex Kantrowitz on CNBC’s Squawk Box shattered the celebratory mood surrounding Apple’s strong holiday season. Kantrowitz, a seasoned observer of the technology industry, worked with CNBC anchors to analyze the contrasting AI strategies of Apple and Alphabet (Google), two of the world’s most influential technology companies.
The immediate outlook for Apple certainly looked positive. Kantrowitz noted that early sales data for the iPhone 17 was “definitely very encouraging.” He shared Tim Cook’s prediction that this quarter will be the “best quarter in Apple’s history, with revenue expected to exceed $130 billion.” This surge in hardware sales, especially for the latest iPhone models, shows strong consumer response to Apple’s core products and is a welcome recovery from the iPhone 16 disappointment.
But this apparent market strength helps obscure a widening strategic chasm. While Apple continues to outperform in established hardware and services areas, progress in artificial intelligence remains noticeably slow. Kantrowitz put this bluntly, saying that Apple is “still behind the eight ball when it comes to AI.” The readiness of advanced AI capabilities once widely predicted by Apple, Google, and Amazon has not materialized at the pace many expected, giving Apple a temporary reprieve from immediate competitive pressures in the space.
The long-term implications for Apple are significant. Without a clear and compelling AI strategy integrated into its ecosystem, Apple risks falling behind competitors that are actively investing and innovating in this innovative space. Relying on incremental hardware improvements, while currently successful, may not be sustainable as AI increasingly redefines user experience and product usability across technology sectors.
In stark contrast, Google has orchestrated a strategic resurgence in the AI space. Kantrowitz praised Google’s “great work in AI” and attributed much of this success to a decisive organizational shift. Google has centralized its AI operations, moving “approximately 250 engineers from search, Google’s most important product division,” to Google DeepMind. This bold move brings together different product areas, or “fiefdoms” as Kantrowitz calls them, under a unified AI “engine room,” facilitating a more consistent and efficient development environment.
This centralization not only accelerated Google’s AI development, but also strengthened its powerful advertising business. Despite the emergence of powerful new players like OpenAI, Google’s core search business remains “unshakable,” largely thanks to its unparalleled ad tech ecosystem. OpenAI boasts a huge user base for ChatGPT, but currently lacks a comparable advertising product or strong advertising network. This fundamental difference in business model means that while new AI services may attract user attention, they do not yet pose a direct financial threat to Google’s main revenue stream.
Related books
In fact, Kantrowitz suggested that the evolving AI landscape could become more “additive” than a winner-take-all scenario. He argued that generative AI applications are likely to complement rather than replace traditional search, creating new use cases and expanding the overall market. This perspective allows for the coexistence of “a very big Google business and a very big OpenAI business,” each carving out its own niche in the expanding AI economy.
Returning to Apple, the situation remains unclear. Apple’s AI division is reportedly losing talent, with engineers moving to companies like Meta, highlighting the persistence of challenges. Kantrowitz confessed, “I don’t really know what they’re doing there. I don’t know if they know.” This ambiguity surrounding Apple’s internal AI initiatives, combined with its historical penchant for secrecy, has given the market speculation about whether it is truly ready to compete in the generative AI era. While it may be prudent to not commit capital to struggling AI initiatives, it also means a lack of visible, active investment and concrete product roadmaps in an area that is rapidly becoming the epicenter of technological innovation.
