The AI gold rush is in full swing. Like every gold rush, the real winners are those selling the picks and shovels. In this case, those picks and shovels are measured in gigawatts.
Oracle and OpenAI just inked a deal to add 4.5 gigawatts of power capacity for data centers. That’s enough to power over 3 million U.S. homes. That infrastructure will support a $30 billion/year partnership, 3x OpenAI’s annual recurring revenue.
It’s all part of “Stargate,” the rumored $500 billion initiative to redefine AI infrastructure from the ground up.
From what we can tell, Oracle’s bet seems to be paying off: its cloud infrastructure unit now generates 43% of its total revenue, growing over 50% last quarter and expected to accelerate to 70% this year. Its stock is up 46% YTD and trades at 56x earnings.
But this isn’t just about Oracle. It’s about a much broader shift.
AI doesn’t run on your phone. It runs in data centers, packed with GPUs, liquid cooling systems, and power-hungry compute nodes.
Big Tech knows it. Microsoft, Amazon, Google, and Meta are on track to spend $340 billion on capex this year, more than the entire GDP of Finland.
Capital investment in AI infrastructure has already outpaced the peak of the dot-com telecom boom and 5G buildouts as a share of global GDP.
This is no longer just a software story. It’s infrastructure.
This shift, from cloud as commodity to cloud as core differentiator, opens space for a new layer: the cloud performance layer.
This is where TAHO comes in. The software intelligence that legacy orchestration was never built to handle.
While hyperscalers battle to build the physical substrate, power, cooling, GPUs, racks, the software layer that runs on top must evolve too. Simply stated: legacy orchestration doesn’t cut it anymore. AI workloads are dynamic, bursty, cross-cloud, and deeply dependent on system-level optimization.
TAHO is building the infrastructure software for modern-day computing. The connective tissue that turns fragmented infrastructure into a coherent, adaptable platform.
Not another cloud. Not a dev tool. A system-level intelligence layer that makes today's $500B AI infrastructure legible, usable, and efficient.
TAHO turns the AI infrastructure surge from raw horsepower into winning performance.
We are living through the most significant technological arms race of our time, however this race is not fought with weapons, but with machine intelligence. Every breakthrough, every leap forward in AI capability, pushes the boundaries of what we can imagine. But there’s a silent truth nobody wants to talk about: raw power alone will not win.
History is littered with examples of people who believed bigger was always better. More machines, more hardware, more brute force. But real innovation has never come from piling on complexity, it comes from rethinking the architecture, from asking not “how much more can we add?” but “how can we make it profoundly better?”
The challenge of AI is not just about training ever-larger models. It’s about sustaining them. Running them. Delivering them to billions of people at once, instantly, without excessive waste. This is where architecture matters, because as the arms race accelerates, inefficiency becomes the silent killer.
The world cannot afford a future where the most advanced technology sits throttled by outdated software foundations. If the cost of innovation is runaway energy consumption, idle resources, and needless friction, then the future slows down, and slowing this train down is simply not an option.
When we look into the future, we don’t see the winners defined by those who have the most GPUs, or the largest clusters. We believe it will be those who design the leanest, most elegant, most efficient architecture to unleash those resources with zero compromise. Efficiency is not a constraint, it is a multiplier. It is the only way to move fast without burning out.
The arms race is real, and it is only just beginning. The question that remains, is will we stumble forward under the weight of inefficiency, or will we architect a foundation that allows intelligence itself to scale effortlessly?
The future belongs to those who choose the latter.
The rise of artificial intelligence isn’t just a story of clever models or vast data sets. It’s a story of supply chains. Beneath every language model, every autonomous system, and every intelligent agent lies a foundation of chips, cooling systems, power grids, data centers, and cloud-scale software.
The AI supply chain is becoming as vital to the 21st century as electricity was to the 20th century. The infrastructure behind AI will set the pace of innovation and define global leadership in the decades ahead.
According to Deloitte, U.S. AI data center power demand is expected to grow more than 30x by 2035, from 4 GW today to 123 GW. That’s a leap from a minor share of current demand to 70% of the total power needs of U.S. data centers in just over a decade. Infrastructural projects on the scale of $500 billion hyperscale campuses and gigawatt-class AI installations are already underway.
Yet the story isn’t just about scale - it’s about urgency. Utilities are straining to forecast and supply this new AI-driven load. Hardware manufacturers are scrambling to meet demand. Even hyperscalers are beginning to face the limits of capital expenditure. And as the growth curves bend upward, the complexity of coordination across power, real estate, manufacturing, and software ecosystems becomes an enormous challenge.
This is where TAHO enters the conversation.
If the next decade of AI is defined by physical scale, the next generation of computing will be defined by software that can scale just as dynamically. TAHO is being built to power this next wave, not just by offering incremental improvements, but by reimagining software infrastructure from the ground up to meet today’s challenges head on.
TAHO’s mission aligns with the moment. The convergence of AI and electricity demands infrastructure that is:
Adaptive to modern day computing. Orders of magnitudes more efficient than competitors today.
, which orchestrates energy, compute and data in a unified intelligent layer that learns and adapts.
Sovereign and Secure. By offering resilience baked into the product, not only for business continuity, but also for security at the enterprise level.
TAHO isn’t just a platform - it’s an enabling force multiplier for builders and businesses designing tomorrow’s AI-native products and services. As utilities plan to cross $1 trillion in capex over the next five years and hyperscalers project half a trillion dollars in annual AI infrastructure investments by the early 2030s, the missing layer is clear: a modern operating substrate that meets the demands of today and the future.
As AI becomes as vital as electricity, the question shifts from “can we build the infrastructure?” to “how fast can we activate it?” And activation doesn’t just depend on concrete and copper - it also depends on the software that binds it all together.
Speed to power is the new competitive frontier. And TAHO is here to ensure that those building in this new era don’t just scale, but thrive.
Every once in a while, a wave hits the tech industry so hard it forces everyone to stop and ask: What the hell are we doing?
Right now, that wave is infrastructure. Not the kind you can see or touch. But the kind that silently powers everything: cloud servers, GPU clusters, energy-hungry data centers. It’s growing, and it’s happening faster than people can metabolize. And it’s costing us more than just money.
It’s costing us innovation.
You see, when I was managing my previous R&D facilities in San Francisco between 2010 and 2020, we believed that investing in ideas didn’t need to have an immediate payoff. Whether it be building with virtual and augmented reality. Interactive holograms. Motion capture, immersive media and computer vision. These weren’t projects built for quarterly earnings, they were bets on our future and they allowed us to maintain a competitive edge in the market.
But today, too many companies are being forced to make the opposite decision: Play it safe. Keep the servers running. Scale the cloud bills. Cool the AI racks. Just stay alive.
The problem is, that isn’t a vision. That’s survival.
And survival isn’t why most of us got into this business.
Let me be clear: infrastructure is essential. But when it becomes the lion’s share of your budget, it turns into a silent killer. It chips away at the time, money, and freedom to chase crazy ideas and it can be argued that now is the most important time in human history to THINK BIG.
Right now, companies are slashing R&D. Laying off engineers. Cancelling moonshots. Not because they’ve lost their ambition, but because their infrastructure bills are devouring their future with no end in sight.
Executives call it “cost discipline.” I call it fear. And fear kills creativity. Every. Single. Time.
Leadership teams today face very hard decisions: do we keep spending to stay in the game? Or do we invest in what might change the game?
You can't do both, not the way things are currently structured. But here’s the thing, you must, or else you will fall behind and become irrelevant.
You have to find a way to build and dream at the same time. That might mean firing mediocre projects to save one great one. It might mean using AI to do in minutes what once took months. It definitely means saying no. A lot.
But remember: saying no is how you say yes to the right things.
We’re entering a decade that will demand more innovation, not less. AI isn’t slowing down, in fact we’re seeing some of the most explosive growth of our lifetime, and anticipating a 10x growth in the next 5 years compared to what we’ve already witnessed. This is wild.
The other consideration is that your competitors will also be riding the very same wave. If your entire budget is going into keeping the lights on, someone else will build the next lightbulb.
The companies that win will be the ones who remember what they’re here to do. Not just run infrastructure.
But we need to have businesses that are willing to build something bold. Something that fundamentally changes people’s lives. Something that still makes you feel like a pirate.
So ask yourself, are we investing in maintenance, or are we investing in magic?
Because if we forget how to dream, all we’ll be left with is a very expensive status quo.
If you’re like most teams, when your infrastructure dashboards show everything “fully utilized” youi take that as a win. It means your cloud resources are being put to work, right?
But here’s the uncomfortable truth: utilization doesn’t equal value.
In fact, many organizations with “green” dashboards are quietly wasting millions. The numbers may look good, but they’re measuring the wrong thing.
This problem has roots in the old way we used to think about infrastructure. Back when servers sat in your own racks, idle hardware meant wasted capital. So teams learned to treat utilization like a performance metric: if the machines were busy, the business must be efficient.
But in the cloud, that logic breaks. You’re not paying for hardware ownership anymore, you’re paying for time. You’re billed for every second a machine is doing work, whether that work is useful or not.
So when dashboards show high utilization, what are they really telling you?
Sometimes, it means your CPUs are chewing through lock contention or spin cycles. Other times, it means your GPUs are technically “allocated” but spending most of their time waiting for bottlenecked memory. Or maybe your app is so bloated it takes 3× the compute to do the same work as before.
It looks like progress. But it’s just activity. And activity ≠ efficiency.
If utilization is about how full your machines are, efficiency is about what you get from them.
It asks harder questions:
These aren’t exotic metrics. They’re just the ones we’ve ignored because dashboards don’t show them by default. And they require seeing beyond the input, toward the output.
Why does this mismeasurement persist?
Partly because our tools don’t help us see it. Most observability platforms were built to show resource usage, not workload quality. They tell you if something is working, not whether it's working smart.
There’s also an incentive mismatch. Cloud providers make more money when you use more. They’re not going to flag that your fully utilized VM is doing low-value work.
And most of all, there’s inertia. Engineering cultures still operate on mental models shaped by the on-prem era. The goal was to keep machines busy. But in the cloud, that goal has become expensive and misleading.
Once you stop tracking “busyness” and start measuring value, the path to savings becomes obvious.
Teams that move from utilization to efficiency often see immediate impact. The best part? You don’t need to rewrite everything. A single piece of software can change everything.
TAHO is a computational efficiency layer designed to eliminate invisible waste.
It sits below the orchestration layer and sees what your other tools miss: where compute is being consumed, where it's being squandered, and how to reallocate it toward actual results.
TAHO doesn’t focus on usage. It focuses on smart, efficient, usage.
It’s built for modern teams who want to run leaner, faster, and smarter.
Your cloud costs aren’t high because your systems are broken.
They’re high because too much of your compute is busy doing nothing.
Ready to see what your stack is really capable of delivering?
Let’s talk.
Your dashboards are all green. CPU graphs show busy servers. Everything seems fine.
But under the hood? You’re burning money on pointless work.
We call this Dumb Computing: when your systems stay busy doing things that don’t actually deliver value. It’s invisible on every utilization chart but painfully obvious on your cloud bill.
Think: a car engine revving in neutral. Lots of noise, zero movement.
Dumb Computing is like that: your infrastructure looks active, but it’s not getting real work done.
It’s not caused by bugs, but by design choices and blind spots in how we build and operate systems today.
Here are six ways your cloud stays “busy” while wasting money:
Code that endlessly checks if something changed. The CPU looks 100% utilized, but achieves nothing.
Example: One GPU job held a CPU core hostage 24/7 just checking a flag, wasting ~$17,000/year.
Fix: Use event signals or blocking waits instead of polling.
Microservices often make too many small calls, spending CPU cycles just turning data into JSON and back.
Example: 25%+ of CPU time wasted on (un)marshalling data. One company halved API calls and saved $75,000/month.
Fix: Batch requests, use efficient data formats, and monitor RPC overhead.
Running lightweight jobs on heavyweight VMs.
Example: Cron jobs on GPU boxes, or dev scripts on massive instances. Leaving one P3 GPU VM running for a month can cost ~$2,200.
Fix: Right-size your instances by default and use cost observability tools.
Tools like Kubernetes and service meshes often sneak in extra costs.
Example: Envoy sidecars can consume 500MB in pods meant for 100MB apps. System daemons can fight your app for CPU.
Fix: Audit sidecar usage and optimize autoscaling.
Broken retry logic can cause self-inflicted DDoS events.
Example: A single chain reaction increased load on a service 512x. Most traffic was failed retries.
Fix: Implement retry budgets, cap backoffs, and use circuit breakers.
Non-production environments often run 24/7, even when nobody’s working.
Example: ~44% of cloud spend is for non-prod. Turning off dev at night/weekends can save 33%+ of that spend.
Fix: Use auto-snooze and kill switches to shut down idle resources.
Most monitoring tools show activity, not value.
A pod at 80% CPU looks fine… but what if 60% of that is serializing JSON?
These tools weren’t designed to measure efficiency. They just show that something is happening, not whether it’s smart or useful.
We created TAHO as a way to dramatically increase the efficiency of your compute, to get maximum value from every dollar and watt spent? It works on a foundational level, going far beyond the examples above, completely rethinking orchestration and beyond to save you time and money.
Your cloud bill isn’t high because your systems are broken. It’s high because too much of your compute is revving in neutral.
Stop paying for busy work.
Start measuring value.
Eliminate Dumb Computing.
Let’s talk.
Modern compute infrastructure is being crushed under its own weight.
Despite enormous investment in cloud, edge, and AI systems, organizations face diminishing returns.
Why? Because the software that governs modern infrastructure is outdated, inefficient, and increasingly unfit for purpose. Containers, orchestration tools, and virtual machines stack abstractions are driving up complexity, energy use, and cost.
Infrastructure teams keep buying more hardware to keep up. But hardware isn’t the bottleneck. It’s software inefficiency.
The Compute Efficiency Layer is a new abstraction in modern infrastructure stacks, purpose-built to reclaim wasted resources, maximize performance, and minimize cost.
It’s not an upgrade to containers. It’s not an alternative to Kubernetes. It’s a foundational shift in how infrastructure is orchestrated beneath the operating system, at the thread level.
CEL sits below containers and orchestrators, providing fine-grained, federated control of compute, memory, and storage across all nodes, local, cloud, or edge. It doesn’t rely on traditional resource isolation models. It eliminates them.
CEL enables real-time, stateless execution across a decentralized, adaptive mesh of compute.
In plain terms: it’s the missing layer that makes modern infrastructure truly efficient.
A new layer is required. One that collapses unnecessary abstractions, maximizes thread-level execution, and federates compute across every node and device.
CEL is not just another orchestrator or PaaS. It’s a new compute primitive: a rethinking of how work is dispatched, run, and completed across distributed systems.
Instead of abstracting over the mess, CEL removes the mess.
It provides a common, adaptive interface for all infrastructure to behave as one: every node becomes a peer in a cooperative, decentralized system that thinks globally and acts locally.
The CEL is purpose-built for:
TAHO is the first implementation of the compute Efficiency Layer. It’s not a rebrand. It’s a product of necessity.
TAHO installs on existing hosts without interfering with workloads, integrates via adapters with known languages and tools, and delivers:
TAHO is CEL in action. But the category goes beyond one implementation. Just as containers gave rise to orchestrators, CEL will give rise to a wave of primitives purpose-built for the compute-constrained era.
AI has changed the rules of infrastructure. Now we must change the software that powers it.
The Compute Efficiency Layer is not a feature, it’s a foundational rethinking. A new lens on how infrastructure can be organized, optimized, and unleashed.
It’s time to stop stacking inefficiencies. It’s time to run fast, light, and free.
Welcome to the era of compute efficiency.
Join today to lock in early access program pricing.
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