How Distributed Computing is Restoring Personal Digital Liberty

Is that AI in your pocket or are you just happy to see the shift away from Big Tech monopolistic elites raining misery into everyone’s life?

The computing landscape is undergoing a tectonic AI shift that everyone should look at closely. While headlines focus on Nvidia’s meteoric rise and its seeming stranglehold on AI market players, the perfect storm of technological breakthroughs is arriving as usual to fundamentally reshape how and where computation happens. This transformation could democratize AI in ways thought possible since the 1950s.

Nvidia Ain’t Like Intel Inside

At first glance, Nvidia’s dominance in processing chips shifted for AI might seem reminiscent of Intel’s iron grip on the PC market. But the comparison falls apart with a few observations. Intel owned its manufacturing facilities, creating true vertical integration. Nvidia doesn’t, instead needing TSMC for manufacturing. Any competitor can access the same manufacturing quality. This crucial difference opens the door for innovation from every quarter, as perhaps it should always have been seen.

Unmistakable Forces of Decentralization

1. Efficiency Breakthroughs
   Recent developments by companies like DeepSeek, a company with just a few hundred employees, have demonstrated that AI models can run with 45x greater efficiency than current approaches. This isn’t just an incremental improvement – it’s a paradigm shift that could make AI compute feasible on much smaller, cheaper hardware. When you can do with one chip what previously required 45, the economics of AI deployment change dramatically.
2. Software Abstraction
   Nvidia’s real exit moat (reverse moat, hotel California, also known as the silicon valley prison) has been its CUDA software ecosystem, not its hardware. But new high-level frameworks like MLX, Triton, and JAX are abstracting away hardware dependencies and restoring the market. This mirrors how programming evolved from assembly language to C++ fortunately making specific hardware less relevant. Soon, AI workloads may run efficiently on virtually any capable hardware.
3. Custom Silicon Proliferation
   Unlike the PC era, where manufacturers were content to use standard chips, today’s tech giants are investing billions in custom AI silicon. This is about optimizing for specific workloads that unlock diverse processing needs, reducing dependence on centralized providers, more than it is about saving cost.

Put Your AI Where You Are

This convergence of forces points to a very near future realizing the concepts talked about since the heady Hadoop days of the 2010s, where AI compute moves closer to where data lives, meaning where you actually live – on your phone, your laptop, your home server, your family cloud, your work infrastructure. Consider the implications:

• Privacy: Personal data never needs a boundary you define, such as your devices or your trusted partners
• Cost: Dramatic reductions in compute costs make personal AI infrastructure feasible today
• Latency: Local processing eliminates network delays, disruption and capture
• Customization: AI models increasingly fine-tune to individual needs for higher integrity (important!) without compromising privacy

What This Means for Leaders

First, infrastructure planning requires at minimum a hybrid approach, combining edge computing with traditional data centers. We’ve known this for years with regard to key management, and it’s more true now than ever. A move entirely into cloud is like selling your home and land to live on a camel that wanders the desert in search of water it will be allowed to drink. Pastoralists built marvels of civilization nomads could only ever dream about. Second, data strategy must recognize our world is better when processing happens closer to collection points. That’s the revolution in philosophy known as enlightenment. I think, therefore I’m not shipping all my data to some weird politically toxic wizard of Oz for them to do it for me. Third, investment priorities need to consider how efficiency improvements will reshape AI infrastructure needs like a motorcycle with a shoulder fired rocket versus a Russian tank. Finally, privacy design must consider AI compute will happily grow at the edge. Castles stopped being built for some simple economic reasons, as Magna Carta tells us amidst language about power, and farmers were actually far better off for it.

The next few years will likely see an acceleration of human rights trends I’ve laid out here (assuming anti-human OpenAI Stargate nonsense doesn’t try to erase society). Cerebras, led by Andrew Feldman, and Groq, founded by Jonathan Ross (formerly of Google’s TPU team), are already shipping hardware that challenges traditional architectures. Open-source innovations from DeepSeek made headline news with efficient AI computation accessible to all because that’s what everyone wants. The real question is how quickly AI compute will decentralize and be distributed.

The message is clear from inside the data centers: the future of AI isn’t in big and central anymore than you are about to buy a mainframe to run your concentration camps. Smarter, more efficient compute that lives wherever you and your data does is the righteous and right path. Those who plan for this transition now will be best positioned to capitalize on the natural democratization that AI is finally maturing into.

Key Insights

The centralized data center model for AI is dated and unnecessarily inefficient, like a teenager refusing to learn how to feed and bathe themselves. New hardware and software innovations are enabling dramatic efficiency improvements by distribution of power. The future of AI compute is enabling more private, more personal, architectures close to data source owners. Business strategies must prepare for this shift in compute paradigm.

This obviously isn’t just about technology – it’s about democratizing processing of data in a way that preserves privacy, restores integrity, reduces costs, and puts power into the hands of individuals and businesses of all sizes. The era of having to send all your data to massive, centralized data centers may be coming to an end sooner than the dangerous monopolists want you to believe. Can you work from home? Standing in an assembly line being flogged to speed up seems so last century. Can you shop from home? Waiting for hours in the cold to get a loaf of bread isn’t anyone’s best use of time. Do you prefer owning your own home and choosing among a dozen local cafes and restaurants to a huge centrally planned barracks with cafeteria mess hall that has only gruel and hard tack to eat? See the immediate future yet? It’s in our past.

Note: as much as I was tempted above to trot out the mostly strawman argument of evil feudalism, I must admit that such a uniform system was largely constructed during the French Revolution as propaganda to characterize and criticize the ancien régime.

Its initial effects in discourse were the horrible collapse into violence, opening the door to national capture by a demented “Emperor” who destroyed reason. History actually is a much more complex historical reality with diverse local arrangements and power relationships, which means invoking dangerous polarizing disinformation narratives of feudalism isn’t a great idea unless we are ready to deal with another ruthless psychopath stepping into the political breach. That’s why…

• I use pastoralists vs nomads to illustrate the value of sustainable local infrastructure.
• Home ownership vs wandering camels shows the risks of complete cloud dependence.
• Local cafes vs mess halls invokes common experiences of how decentralization enables choice that nutures quality.
• Work-from-home vs assembly lines hopefully connects the importance of data processing location to human dignity, for everyone.