The Age of Edge

A third hemisphere in your pocket

My pal Michael Michalchik recently pointed out where the next great trove of AI training data might be coming from. A year ago, the artificial intelligence community was abuzz with concerns about the apparent exhaustion of internet-scraped training data. This scarcity, many feared, would impede further progress in AI development.

Sam Altman then made cryptic statements about novel approaches to the data issue (coinciding with high-profile partnerships between OpenAI and tech giants like Microsoft and Apple), which hinted at a solution that went beyond traditional web scraping methods.

The answer may be a new kind of data: recordings of people actually using computers, down to mouse movements, button clicks and menu navigation. Scraped text gives a model language and general knowledge, but it cannot show it how to operate a machine. It is the difference between reading a manual and receiving hands-on instruction.

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Microsoft’s Recall feature, which takes a snapshot of the screen every few seconds so that users can search back through everything they have seen, is touted as an innovative tool for productivity. Yet it raises serious concerns about privacy, security, and the future of work. This seemingly innocuous addition to Windows 11 may, in fact, be a harbinger of a more insidious trend in data collection and AI development.

At its core, Recall presents a multitude of security vulnerabilities. By creating and storing detailed snapshots of user activity, it opens up new avenues for potential exploits. The feature’s very existence undermines existing security protocols and could violate non-disclosure agreements and copyright laws. Reports suggesting that these snapshots are easily accessible and stored in unencrypted plain text only underscore how premature this release was.

Beyond security concerns, Recall imposes a significant burden on system resources, potentially degrading performance. Planning to switch such a feature on by default, for users who may not fully comprehend its implications, was at best irresponsible. Microsoft has since made Recall opt-in, but history suggests that such options often become increasingly difficult to disable over time, as we’ve seen with other telemetry features.

The most alarming aspect of Recall may be its true purpose. This feature appears to be a thinly veiled attempt to gather an extraordinarily rich dataset for training the next generation of AI systems. By capturing detailed user interactions, Microsoft is effectively turning its user base into unwitting annotators for complex tasks. Professionals across industries may inadvertently be training their own AI replacements through their daily activities.

The potential value of this dataset is enormous. It could be worth more than Microsoft’s current market capitalisation, providing the company with an unprecedented advantage in developing sophisticated, tool-oriented AI systems capable of acting as digital concierges for complex tasks. In a time when the company seems to be hollowing out its empire with questionable decisions such as embedded ads and bloat, as it drifts from its core competence, the temptation to seize this opportunity is likely to grow.

Combined with AI that can read and interpret these snapshots, Recall could lead to a catastrophic breach of privacy on a global scale: a potential “Cyber 9/11” of doxxing and cyberwarfare.

This overreach cannot go unchallenged. Regulators, corporations, and end-users should unite to stop such unnecessary and potentially harmful technologies.

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Dark compute

Nvidia briefly surpassed Apple as the world’s most valuable company, riding the wave of AI-driven demand for its powerful GPUs. However, Apple, long perceived as lagging in the AI race, appears poised for a dramatic resurgence that could redefine the tech industry.

The key to Apple’s potential dominance lies in its vast, untapped reservoir of “dark compute”: the largely dormant Neural Processing Units (NPUs) embedded in its extensive ecosystem of devices. Since the iPhone 8, Apple has been quietly integrating these AI-capable cores into its products. The M-series chips, in particular, boast impressive capabilities of up to 38 TOPS, hitherto utilised primarily for image enhancement.

This dormant potential is on the verge of awakening. Apple is positioned to deploy its AI technologies across an unprecedented scale of systems, enabling instant, high-performance, low-latency hybrid interactions that smoothly blend on-device and cloud processing. These applications will extend far beyond simple proofreading or image generation.

Like its tech giant counterparts, Apple appears to be setting its sights on agentic AI systems: sophisticated digital assistants capable of crafting complex solutions for multifaceted problems. These AI concierges could reshape personal and professional task management. Apple’s intimate knowledge of user preferences, habits, and boundaries gives it a significant edge in developing AI models that are both helpful and agreeable to users.

Perhaps most intriguingly, Apple has the potential to transform its vast network of consumer devices into a distributed compute cloud. By harnessing the power of fully-charged, idle devices, Apple could create an aggregate computing cluster that dwarfs even the most advanced supercomputers. This immense computational power could rapidly propel Apple’s AI models ahead of competitors, at minimal direct cost to the company.

Apple has been playing a long game, and the pieces are now falling into place. The company’s strategy of integrating powerful NPUs into its devices over the years is set to pay dividends, potentially catapulting Apple back to the top of the tech hierarchy.

As this strategy unfolds, we can expect other tech giants to scramble to follow suit. However, Apple’s head start in hardware integration and its vast, loyal user base may prove to be decisive advantages.