The Biggest Mistake Business Leaders Are Making About Edge Computing
Here's the thing about revolutions: they never look the way people expect them to.
When personal computing arrived, the establishment thought it was about smaller machines. When the internet emerged, they thought it was about faster communication. When cloud computing took hold, they thought it was about cheaper servers. Every single time, the industry fixated on the hardware - the physical thing you could point at, and each time it completely missed the fundamental shift happening underneath.
I believe edge computing is experiencing that exact same misunderstanding right now. And it's costing businesses dearly.
The pervasive narrative that I am hearing frames edge computing as a hardware story. Devices. Gateways. Physical locations. Racks in closets and micro data centers tucked behind factory floors. It's neat, it's tangible, and it's almost entirely beside the point.
I've spent my entire career at the intersection of design and technology, from pioneering sophisticated immersive environments with fortune 100 brands, to building custom software solutions and novel IoT environments using edge intelligence. Throughout most of my career, I've designed systems where the technology had to disappear into the background so the experience could come alive in the foreground for the end user. And if there's one pattern I've seen repeat itself across nearly every project I've worked in, it's this: The moment you define a technology by its physical form, you've already limited what it can become.
This is the reason that I believe that edge computing isn't a hardware topology. It's a business topology shift. And that distinction changes everything.
Intelligence Belongs Where Value Is Created
Think about where value actually happens in your business. It's not in a data center two thousand miles away. It's in the moment a customer interacts with your product. It's at the transaction boundary where a decision gets made. It's on the factory floor, at the retail shelf, inside the vehicle. It exists at the point of care. With this as our frame, we must recognize that the truest value is fundamentally created at the edges of your organization, and for far too long, we've been routing all the intelligence back to some centralized brain and asking it to make sense of what already happened.
That's like mailing a letter to your own brain every time you need to pull your hand off a hot stove. By the time the signal arrives, the damage has been done.
The work that our team is focusing on today is helping to address this, by delivering the computation where the context is the richest. Not where it's most convenient for IT departments to manage, but where the data is freshest, where the latency matters most, and where the outcome has the greatest impact.
This is the very principle that sparked TAHO, and with the rise of AI and high-performance computing workloads - we believe this needs to be addressed, and our team is working on this with a great sense of urgency.
Three Things Edge Actually Unlocks
When you stop thinking about edge as infrastructure and start thinking about it as an execution philosophy, three massive opportunities come into focus.
First: Experience. Lower latency doesn't just mean faster response times on a spec sheet. It means fundamentally better engagement for that end-user who is interacting with your brand. It means that the interaction feels alive, responsive, and intuitive. In a past life when I would design and build next-gen experiences, a fifty-millisecond delay could shatter an illusion, and miss an opportunity to drive trust and meaning, when it was fundamentally built to demonstrate innovation. During that period of time, I learned that latency isn't a technical metric. It's actually a human one. People don't consciously notice when things are fast, but they absolutely feel it when things aren't. Edge processing, when executed well, creates experiences for the user that feel almost magical in its responsiveness. The thing is - that magic isn't accidental. It's architectural.
Second: Compliance. This is something that many business leaders underestimate. When you process data locally, at the edge, you dramatically simplify your regulatory exposure. Data doesn't have to traverse jurisdictions. It doesn't have to pass through third-party hands. It stays close to its origin, which is exactly where most privacy frameworks want it to be. In an era where regulatory landscapes are shifting constantly and the penalties for non-compliance are becoming existential, local execution isn't just operationally elegant. It's strategically essential.
Third: Resilience. Centralized systems have a single point of truth, which also means they have a single point of failure. Distributed edge architectures don't just improve performance - they fundamentally reduce systemic risk. When one node goes down, the system adapts. When connectivity drops, local processing continues. This is exactly the kind of architecture we're building at TAHO, with our distributed execution layer using our Magnetic PeerMesh ™technology. We set out to build TAHO because we kept seeing the same pattern: the cloud was supposed to liberate us, but it chained too many organizations to complexity, fragility, and spiraling costs. TAHO is designed so that powerful computing - AI, HPC, or whatever the workload is, can ultimately run closer to where it matters, on existing hardware, without the overhead and waste that legacy orchestration demands.
The Real Mistake: Treating Edge as an Add-On
Here's where most leaders go wrong, and I say this with the utmost respect from the lens of someone who has watched brilliant people stumble on this exact point… Many technical teams are treating edge as an addition to their existing architecture rather than a rethinking of it.
They bolt edge devices onto cloud-centric execution models and wonder why costs balloon and utilization stays low. They deploy edge nodes but still route decision-making back to centralized systems, effectively negating the entire point. They invest in the hardware without redesigning the software, the workflows, or the organizational logic that sits on top of it. And as a result, they get beat up by the technical debt.
This is like buying a sports car and then towing it behind your fucking minivan. You've spent the money, but you've captured none of the value.
The companies seeing transformative returns from edge computing are the ones treating it as a first-class execution environment. Not secondary. Not supplementary. Strictly first-class, because again - they know where the true value lives. That means rethinking how applications are built, how workloads are distributed, how your data is flowing, and how decisions get propagated. It means trusting the edge to do real work; not just collect data for somewhere else to process.
At TAHO, this is a core philosophy. We built our Magnetic PeerMesh™ technology because we believe workloads should intelligently flow to wherever they can be processed most efficiently. The infrastructure should adapt to the work, not the other way around. And the results speak for themselves. We’re seeing dramatically more output from the same machines, lower energy consumption, and a platform that fits into existing stacks without demanding a wholesale rip-and-replace.
The Future Disappears Into the Background
I've always believed that the most profound technologies are the ones that eventually become invisible. The best interface is no interface. The best infrastructure is the kind you never think about because it simply works… Elegantly, efficiently, and at the speed of your ambition.
That is the real promise of edge computing. Not more boxes in more places, but a world where intelligence is simply there, wherever and whenever it's needed. Where the processing happens so close to the point of value creation that the gap between insight and action effectively disappears.
We're truly standing at the beginning of something remarkable. The convergence of edge computing, artificial intelligence, and distributed systems is going to reshape how every industry operates into the future. But only for the leaders who see edge for what it truly is… Not as an infrastructure decision, but a strategic one for their businesses. Not a deployment, but a redesign for their customers. Not an add-on to the old world, but the foundation for the future.
The question isn't whether your business needs edge computing. The question is whether you're willing to rethink your entire execution model to actually capture its truest value.
From everything I've seen, across immersive media, IoT, enterprise infrastructure, and now distributed HPC, the answer should be an unequivocal yes. The future belongs to those who build intelligence at the edge. And that future is closer than most people realize.
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Get Compute Out of Traffic: Federated Orchestration Beats the Control Plane
The problem with modern infrastructure
Modern infrastructure is powerful but inefficient. Stacks rely on layers of orchestration that add latency, waste resources, and slow teams down. A central control plane makes every decision. That creates queues, bottlenecks, and idle capacity that you still pay for.
From dispatchers to a compute fabric
Traditional scheduling works like an old taxi service. A dispatcher sits in the middle and tells each driver where to go. It is reliable but rigid. When traffic spikes, you get a long line of waiting passengers. Cars sit nearby with fuel in the tank, yet the line does not move faster.
TAHO works like a modern rideshare network. There is no single dispatcher. Every node participates. When a request appears, available nodes advertise themselves, and the system picks the best fit. Fastest. Least busy. Closest to the data. If one node cannot take the job, another does without delay.
This creates a compute fabric. A mesh of secure peers that discover each other and cooperate as one. The result is self-healing, self-scaling, and efficiency across data centers, edge sites, and multiple clouds. There is no single point of control and no central bottleneck.
What makes the fabric work
Portable components
Applications are built from lightweight, portable components that can run anywhere in the fabric. A component can be referenced and invoked from any node without the caller needing to know where it lives. Once a component is published, it can start, move, or scale near-instantly as demand changes.
A simple example
One node needs a report. Another node already has the data and the tool. The system lets the second node do the work and share the result with the rest. From that moment on, there is no coordination overhead. Only results that any node can use.
Security by design
Every component runs in its own secure sandbox. There is no shared memory and no implicit access to files or networks. If one component fails or is compromised, it stays contained while the rest of the system keeps running.
Instant hot reload
When you push new code, the fabric swaps in new components without restarting. Existing versions finish their current work while the new ones take over. If something goes wrong, the fabric automatically rolls back to the last known good version.
Peer-to-peer networking
Nodes use secure decentralized protocols for service discovery, workload distribution, and shared state. There is no need for centralized load balancers or control planes. Coordination and performance emerge from the collective behavior of all nodes working together.
How it compares to a central control plane
A central control plane concentrates decision-making in one place. That creates a queue and a single locus of complexity. You scale the controller. You tune the controller. You wait on the controller. The system is stable, but work piles up when demand spikes.
A compute fabric distributes decision-making. Nodes advertise capacity, claim work quickly, and keep work moving. There is no queue to jam up. There is no master to fail. Work finds the fastest path through the environment every time.
The takeaway
A central control plane was a useful step for the last decade of cloud. The next step is federation. If you want to see the fabric in action, start with a small service and let TAHO run it across a few nodes in your environment. You will feel the difference the first time you push code and watch it go live with no blip.
Engineering Breakthroughs That Crush AI Bubble Fears
Amid fears of an AI bubble, these advancements in AI infrastructure are concrete engineering wins and will form the basis of a sustainable AI-driven economy in the U.S.
In the swirling market excitement that has defined the AI era, it is natural to be concerned that investors may be inflating a bubble. Many of us who lived through dot-com mania look at Nvidia surging past a $5 trillion in market cap with a skeptical eye. One prominent voice pegged the current AI hype as 17 times larger than the dot-com boom, fueled by trillions in projected spending that may never yield commensurate returns. OpenAI's revenue forecasts tripling to $12.7 billion next year sound triumphant, but come amid warnings from firms like Ark Invest's Cathie Wood about potential market corrections. The BBC has spotlighted a "tangled web of deals" in Silicon Valley, where valuations do not match up to profits.
Yet amid these valid concerns, infrastructure advancements based on hard science and engineering are taking AI’s inflated expectations and shifting them to a robust productivity engine, particularly in the United States. Innovations in both compute hardware and infrastructure software promise to address the core bottlenecks of scaling: energy-hungry data centers, memory walls that choke model performance, and supply chains vulnerable to geopolitics. To give just two examples from different parts of the stack: startups like Substrate are working on X-ray lithography techniques that could reclaim U.S. semiconductor dominance, while TAHO, a U.S.-engineered compute software platform, unlocks far more data-center capacity and reduces inference costs on existing infrastructure without new silicon.
By 2030, global data centers could demand $3.7 trillion to $5.2 trillion in investments, but with U.S.-led efficiencies, this spend translates into productivity gains that could add trillions to GDP, echoing McKinsey's early projections for AI's potential. When energy demands are projected to rival entire nations' power consumption, these concrete wins are setting the stage for the U.S. to take a leading role in the transformation of the global economy.
Hardware Advancements
Today, it’s widely assumed that AI's scaling challenge lies primarily with the speed and cost of chip production. For years, the U.S. has ceded ground in semiconductor manufacturing to Taiwan's TSMC and the Netherlands' ASML, whose extreme ultraviolet (EUV) lithography tools hold a near-monopoly on producing chips at the 2-3 nanometer scale essential for AI.
Enter Substrate, a San Francisco startup that emerged from stealth this month with an audacious claim: the ability to use particle accelerators to etch features finer than 2 nanometers, surpassing the state of the art. The new technique also costs a tenth as much as in-market solutions, costing $40 million per tool versus $400 million. Backed by over $100 million from Peter Thiel's Founders Fund and In-Q-Tel, Substrate has successfully etched silicon wafers at U.S. national labs like Oak Ridge in my home state of Tennessee.
However, to compete in the global AI race, chips alone will not suffice. Data centers will form the backbone of daily productivity, and data centers are hungry – for energy, water, and real estate. Energy constraints loom large, with AI's power consumption possibly hitting 123 gigawatts in the U.S. by 2035. That would be enough to power about 100 million U.S. homes simultaneously. There’s a limit to how chip design can maximize energy efficiency, at which point software architecture becomes a key lever.
Software Advancements
While energy and hardware provide raw potential, it is the software we run on it that ultimately decides whether we are maximizing the use of scarce compute cycles. As an example, TAHO, a stealthy infrastructure software layer that claims to increase effective compute without new hardware, could slash inference costs by 90% and launch processing jobs 30 times faster by creating a shared memory fabric across fleets.
Unlike Kubernetes, which often leaves 70% to 80% of cloud capacity idle due to orchestration overhead, suboptimal scheduling, and queuing delays, TAHO acts as a compute-efficiency layer that eliminates redundant work and cold starts, reclaiming capacity into coherent AI pipelines.The framework sits atop existing stacks, turning $371 billion in annual data center spend into twice the ROI by optimizing for the AI supercycle's underbelly.
As hyperscalers like Meta project capital expenditures to grow notably larger in 2026, software-side innovations will ensure these investments yield higher returns. Innovative architectures like TAHO could transform Substrate's already dense chips into supercomputers, making compute "feel infinite" without ballooning power consumption. Deloitte predicts that over 50% of data will be generated at the edge, and performance optimization software like TAHO will facilitate that trend, ensuring efficient scaling and reducing supply chain risk.
Concrete hardware and software advancements are shaping a path to sustainable growth in the AI sector, and these gains are quantifiable regardless of whether AI investment is momentarily overheated. When foundational technologies like Substrate's lithography, TAHO's efficiency alchemy, and others are combined, trillion-token models that don’t fry the grid become practical – leading to AI abundance that will improve the quality of life for all.
For more, follow Dave Birnbaum @ contrarymo on X.
Why the Future of Software Is a Living System
We’ve spent decades treating software like machinery. Something to be built, deployed, and maintained. But that metaphor is beginning to break down as HPC forces us to flex in ways we’ve never needed to before.
At scale, software no longer behaves like the same machine that it used to. With the advancement of HPC, and AI - it will likely need to behave more like an organism that is adaptive in nature.
Organic systems naturally evolve through feedback loops, replication, and adaptation. They self-heal, reroute, and reconfigure based on signals from their environment.
Think about it: Human biology is a perfect example of how complex systems can work together seamlessly without conscious coordination. The muscular, skeletal, and nervous systems collaborate to produce movement. Muscles contract, bones provide structure, and nerves deliver signals in milliseconds, all without deliberate thought.
The respiratory and circulatory systems synchronize to ensure oxygen flows precisely where it’s needed, while the endocrine and immune systems quietly regulate balance and defense. Each subsystem is autonomous yet interdependent, continuously communicating through chemical and electrical signals. The result is harmonious, and it just works.
The current model is deployment pipelines and fixed architectures, combined with traditional redundancy measures, which feels increasingly primitive in a world where intelligence, data, and computing is constantly in motion. We engineer systems that require back up machinery and switching mechanisms built in, and even those are often done through a lens of reactive maintenance, as opposed to proactive support.
At TAHO, we’re exploring what happens when you design infrastructure in a similar way to that of a living system. Our runtime treats compute as a fluid - something that can dynamically move, replicate, and support anywhere in the network. And we’re doing this with no containers. No orchestration. No traditional scheduling. Just autonomous components forming a self-healing mesh, much like living organisms do.
This isn’t about uptime; it’s about adaptation. When a node fails, others reorganize and adapt. When demand spikes, the network flexes. The system learns - not because it’s programmed to, but because it’s designed to respond accordingly.
This is how we see the future of software infrastructure. Software that doesn’t just run, it evolves with the future of modern day computing, and HPC workloads.
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