On October twenty a major AWS incident in US East 1 rippled across the internet. Popular apps stalled. Status pages lit up. Engineers chased errors for hours. Reports point to DNS trouble that made a core database service hard to reach in that region, which then cascaded into other services and customer apps.
Why it mattered was not only the root cause. It was the concentration of risk. US East 1 is the most used AWS region, and a large share of apps and data live there. When it coughs the world feels it.
Recovery is good. Continuity is better. The goal is service that keeps moving while pieces fail. Treat availability like the product experience, not an afterthought.
What nearly everyone now agrees on
When a region falters your app keeps answering. Some features may take a brief pause. Most do not. Pages load. Messages send. Orders place. Your status page explains a localized slowdown rather than a full stop. That is what customers remember.
Many teams want continuity by default but do not want to stitch multi region and multi cloud into one system. TAHO's autonomous fabric treats your clouds and data centers as a single pool and routes work to healthy places automatically.
During an event like the AWS outage this means
You still own your architecture. The fabric supplies the intelligence that routes around trouble and turns big static capacity into reliable performance.
Outages like October twenty are uncommon yet the blast radius is wide when so much lives in one place. Experts called out how dependence on a few providers raises systemic risk, and officials asked for stronger diversification. A fabric that spans environments and routes around trouble is a practical answer you can stand behind.
Even very high uptime still allows real hours of trouble each year. When continuity is your default state those hours shrink in impact. Users keep moving. Teams stay calm. The business earns trust.
Every week there is another headline about artificial intelligence transforming an industry. Behind the scenes, most projects never make it past the pilot stage.
Studies from Gartner and McKinsey put the failure rate at around 70 to 85 percent. The problem is not usually the algorithm. What works in a lab often cannot survive the leap into the messy, costly, and regulated world of real operations.
The projects that survive share three qualities:
Most AI pilots fail not because the technology is weak, but because the bridge from lab to production was never built. The projects that succeed focus less on flashy demos and more on viability. That means AI that is applicable, deployable, and sustainable in the real world.
Training GPT-3 once consumed enough electricity to power 120 U.S. homes for a year. Inference now burns through that much energy every single week.
The story is no longer just about breakthroughs. It is about bills. And across industries those bills are reshaping margins, business models, and entire markets.
Trading firms run models at millisecond speed on oceans of data. The result is monthly power bills in the hundreds of thousands of dollars, before they even think about refreshing hardware.
Compute can predict markets, but it is devouring margins.
Medical imaging and drug discovery rely on enormous training runs. Hospitals and startups often discover their cloud bills outpace their research budgets.
The bottleneck is not data or talent. It is the invoice.
Games that promise infinite worlds and lifelike characters sound like the future of entertainment. But when millions of players log on at once, every interaction carries a real compute cost.
Some studios learn the hard way. Viral success brings hosting bills that explode faster than revenue. In gaming, success can bankrupt you faster than failure.
Search used to cost fractions of a cent per query. Generative systems made it ten to one hundred times more expensive. Microsoft admitted it plainly: from now on the gross margin of search is going to drop forever.
Search just became smarter and instantly less profitable.
The industry keeps reaching for band aids. Quantize the model. Distill it smaller. Rent GPUs on the spot market. Shuffle workloads between clouds.
Each slows the bleeding. None touch the underlying wound. These tricks make balance sheets look tidier for a quarter, but the economics keep breaking underneath.
We are putting band aids on a hemorrhage of compute cost.
The race is no longer about the biggest model, but about the cheapest intelligence per watt and per dollar.
The future is not won by stockpiling hardware, but by making intelligence affordable everywhere, in every industry, at every scale.
Compute costs are rising faster than returns. Band aids will not save us.
The winners of the next era will not be the companies that simply add more capacity. They will be the ones who make intelligence affordable, sustainable, and available at scale.
OpenAI signed a staggering $300 billion cloud deal with Oracle. The contract requires 4.5 gigawatts of power capacity: about the same electricity used by four million homes.
This is one of the largest cloud contracts in history. On the surface, it looks like a bold growth play. But look deeper and it reveals how far the economics of AI have stretched into uncharted territory.
OpenAI disclosed around $10 billion in annual revenue. Yet this deal will lock it into paying roughly $60 billion per year for compute. That gap is the definition of a compute bubble risk.
Oracle, meanwhile, is tethering a huge portion of its future to one customer, while carrying one of the heaviest debt loads among cloud providers. Both companies are betting that AI adoption and monetization will scale fast enough to justify the spend.
But history tells us bubbles form when investment races far ahead of realized returns. We may be seeing that dynamic play out in AI infrastructure.
Generative AI isn’t just “in the cloud” anymore. It is measured in Hoover Dams.
The OpenAI–Oracle contract alone requires 4.5 GW of power capacity. That’s not just an accounting line item, it has real-world consequences. Local grids are already straining under data center growth, and new capacity often requires years of permitting and billions in investment.
We’ve crossed into a world where AI demand is shaping the energy market.
The narrative is shifting. For years, the focus was on breakthrough demos and the magic of generative AI. Now, the conversation is about cost, power, and sustainability.
The companies that win in the next chapter won’t simply be those training the biggest models. They will be the ones who figure out how to make AI efficient, sustainable, and affordable at scale. That means new chips, better orchestration, and smarter business models.
So what does a $300B bet on compute really represent?
Is this a bold long-term play that cements OpenAI’s role as the platform of the future? Or is it a sign that AI infrastructure costs are inflating faster than the business case?
Either way, the true cost of compute is now front and center. And for enterprises, investors, and policymakers, ignoring it is no longer an option.
We are building data centers that eat the same power as small countries. And for what? To run software that wastes most of it.
The truth is simple. Hardware stopped giving us free performance years ago. But the industry keeps pretending otherwise.
Yes, chips are still improving. But at what cost?
CPUs barely move the needle anymore. Clock speeds flatlined years ago.
GPUs look like rocket ships on paper. Nvidia shouts about 5x gains every generation. But under the hood it is brute force. More power, more silicon, more money. A single GPU can cost $30,000 and draw 500 watts. Entire racks now run at 600 kilowatts. That is the same as powering hundreds of homes.
This is not innovation. It is desperation.
For decades, software did not need to care. You shipped code, the next chip ran it faster, and everything looked fine. That era is gone.
Today, real performance comes from smarter software. And the proof is everywhere:
These are not edge cases. They are proof that software is now the biggest lever.
The industry’s answer has been simple. Buy more hardware. Throw more watts at the problem. But that path comes with a brutal bill.
And at the end of the day, if the software is wasteful, the hardware is just an expensive heater.
This is the shift most people do not talk about. The real frontier of performance is software.
Algorithmic breakthroughs outpace silicon. Optimized code routinely beats new hardware. And John Carmack, one of the most respected engineers alive, has said that if hardware production stopped today, much of the world could still run fine if we actually bothered to optimize.
We are not out of performance. We are just not looking in the right place.
Here is the good news. The pain companies are feeling, from skyrocketing cloud bills to GPU shortages to sustainability concerns, does not have to be the norm.
The path forward is already here. Smarter algorithms. Leaner code. Better use of the hardware we already have.
The companies that embrace this mindset will move faster, spend less, and scale further. The ones that do not will keep buying their way into bigger bills and bigger bottlenecks.
Hardware may be hitting a wall. Software is the way through it. And the sooner we admit that, the sooner we stop burning billions for nothing.
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.
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.
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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