The Apple of our AI
OpenAI, Anthropic, Google, NVIDIA and even Microsoft have made headlines during the AI revolution.
Apple has been an afterthought.
Apple Intelligence is far from intelligent. Siri is clueless. And Apple investment in AI compute hardware is 5 to 7 times less than tech competitors Microsoft, Meta and Google.
And yet, Apple has one golden delicious AI advantage: its M chip.
Why was John Ternus appointed the successor to Apple’s CEO Tim Cook? Because he led Apple Silicon, which designs the M chips for Apple devices, now considered the best performing chips for personal computers.
More importantly, the M-series chips are designed for AI.
As venture capitalist, Atlas Berry said, the “next battle is going to be at the intersection of hardware and AI. And the M chip is one of the most efficient when it comes to AI inference on device, running models locally, privately. I mean, that’s Apple’s whole lane, and nobody understands the Silicon better than Turnus.”
Year of the Lobster
The first signs of this shift are not coming from corporate IT departments. They are coming from power users who want AI agents running beside them, and not in another company’s cloud.
When Peter Steinberger put his OpenClaw system out in the wild, millions immediately turned his software, symbolized by a red lobster, into their own personal assistant.
Alex Finn is one of the most enthusiastic power users of personal AI Agent systems, and showed off his multi-agent set on Peter Diamandis’ Moonshot podcast, one that featured a series of Mac Minis and Mac Studios.
Finn articulated what many in the industry understood – Apple is a leading chip designer that is ideal for Agentic AI systems.
“People discover OpenClaw and what does everyone do without thinking twice?” asked Finn. “They go to the Apple Store and buy Mac Minis. They didn’t go buy GPUs and memory and power supplies and fans and build computers. The market just gave this massive signal.”
“[Apple has] been viewed like the loser in the AI race for a very long time,” said Finn. “This is their opportunity to flip that entire thing and be the winner of the AI consumer race because clearly when people want to run AI locally, their brain just goes to Mac minis.”
The consumer and enterprise markets rarely move in synch. But in this case, they appear to be asking the same question: why send sensitive work tot he cloud if a local machine is good enough.
Enterprises Picking up the Signal
AT the ATx Enterprise conference in Singapore in late May 2026, one statistic captured the gap between AI enthusiasm and AI integration: 28.5% of Singapore enterprises had started their AI journey, but only 3.8% have meaningfully integrated AI.
To accelerate meaningful adoption, I noticed that experts in AI implementation at this conference were emphasizing the benefits of on-premise AI systems, with an emphasis on Apple hardware.
Johnson Poh, Assistant Chief Executive of IMDA, a government agency driving AI adoption in Singapore said that they have a critical relationship with Apple.
“Some of your most critical operations happen where the cloud fails,” said Poh. “But Apple has changed that equation. With the latest advancements in Apple Silicon we can now run large language models entirely on device and on premise with full data sovereignty, lower latency, and lower energy costs.”
Kenneth Tan of AI consultancy, MyLearnZone, explained in his AI Adoption workshop at the ATX conference that all of his AI demos happened on his own laptop, using an open source large language model, disconnected from the internet.
Tan emphasized this point. “You need to own your own AI. You have to use on prem AI from a security, cost, and most important, scalability perspective. I recommend that you use Apple Studio because their power consumption is much lower than Nvidia chips. A lot of open-source AI works very well on Apple Macs.”
When Your Data is Not Your Data
For 15 years or so, the cloud was the obvious answer.
IT managers used to oversee air-conditioned rooms filled with stacks of servers, often in the company’s HQ, on premise. Then enterprise leaders realized that they could significantly lower upfront IT costs and quickly scale up or down in storage or application usage by shifting their data centers to the cloud.
But today, enterprise leaders are realizing that the various cloud services their businesses rely on is coming at a cost. In the age of AI, increasingly the most important asset a business has is its own data. And while it is easy to connect to the API of Google’s Gemini, Anthropic’s Claude or OpenAI’s ChatGPT, leaders are becoming more sensitive to where data goes, how it is retained, who has access, and whether sensitive workflows should depend on external model proviers.
Venture capitalist, Chamath Palihapitiya said on his All-in Podcast that companies are going to need to reverse direction and head back to on-premise IT infrastructures, or they may not survive.
“I tweeted it this morning, but is on prem the new cloud,” he asked. “Which is weird to think that that could even be possible, but we’ve spent since 2008 migrating everything to cloud because there were these economies of scale.”
“The counterpoint is that in the AI revolution, companies…will be fighting for their lives. And I think it’s unclear whether it makes sense for a company to allow the natural leakage of their edge and their confidential and proprietary information out into the wild, versus the control that they would get if they ran on prem.”
Palihapitiya went on to make a critical point about confidentiality in the US, citing a February court case when a judge ruled that documents generated by an AI tool and shared to one’s lawyers are not protected by attorney-client privilege.
He laid out the conundrum for business leaders: You need AI to compete and survive but you cannot give up control, security and confidentiality of your data.
“The only solution is to have the pendulum swing all the way back and have private provision networks, which increases cost, but then if you save a bunch of money because of AI, maybe it all balances out. That is the big question that I’m wrestling with right now.”
