Usually, if you want a model to know your specific writing style (like your twitter voice) or your specific niche (mine is financial data analysis), you have to upload that data to a cloud provider like Microsoft Azure, which powers OpenAI’s infrastructure.)

To receive persaonlized responses from an LLM provider like OpenAI, your data must be sent to and stored on their remote servers.

QVAC changes this as the framework allows data to stay on your device, and bypass the cloud entirely because the computation happens locally on your hardware.

While for most things, it probably doesn’t matter for the cloud to have access to all your data. But for sensitive stuff, like financial audits, or proprietary investment research, using a local framework like QVAC will allow you to retain 100% custody.

A pre-trained LLM like Llama, Qwen is like a super-smart but generic university professor who knows everything about the world in general. If you want this professor to become your personal doctor AI (that understands your specific health history, diet, symptoms, and speaks in your style) you need to teach them your private medical notes, blood tests, Whoop data etc.

teaching = fine-tuning = updating the model’s knowledge with your data.

In the old way, we would retrain the entire professor from head to toe, and change very single connection in their brain. For a 13B parameter model, that’s billions of numbers to update. This needs huge servers, costs a fortune in electricity/GPUs, takes days/weeks, and your private data has to leave your phone.

This is where LoRA (Low-Rank Adaptation) is genius. LoRA says don’t touch the professor’s core brain at all. Instead, give them a tiny set of clip-on cheat sheets or post-it notes that they can consult everytime they answer.

These post-its only contain a very small number of new instructions (often just 0.1% to 1% of the original model’s size - up to 99% fewer parameters to actually train.)

The original professor stays frozen (unchanged, safe, no risk of “forgetting” general knowledge.)

When the professor speaks, they combine: original brain (frozen) + tiny LoRA post-its (your personal tweaks).

The answer feels fully personalized to you, but almost no extra compute/memory is needed.

Mathematically its clever. The update to any big weight matrix is approximated as the product of two much smaller matrices (low rank = skinny rectangles instead of huge squares). So instead of changing 10,000 x 10,000 numbers, you only train something like 100 x 10,000 + 10,000 x 100 = ~2 million numbers. Huge savings.

BitNet already makes the base model tiny and fast on phones (weights are just -1/0/+1 instead of full floating point numbers which already saves ~70-90% of memory & compute).

But fine-tuning BitNet normally was still hard/impossible on phones because even “updates” were too heavy.

LoRA slashes the update size so dramatically that now the whole personalization process fits in a phone’s limited RAM and memory.

Think of your phone’s AI as a very compressed zip file of a genius librarian (BitNet makes the zip super small so it fits on phone.)

LoRA = keep the zipped library untouched, just add a tiny bookmark file with your personal notes/index. Your phone can handle adding/using the bookmark easily, and when you ask questions, the librarian reads the main zip + your bookmark instantly.

It turns AI from “rented cloud services that sees all your data” into “your own private brain extension that lives on your device and learns only from you.

No more paying $20/month to OpenAI, no data sent anywhere, no censorship, works offline.

Tether/QVAC solved making LoRA + BitNet work cross-platform (AMD/Intel/Nvidia/Apple/Mobile GPUs) for the first time.

LoRA is the unlock button that lets billion parameter personalization escape data centers and live in your pocket. That’s why Paolo and the team are calling it the start of “Stable intelligence”. If this clicks now, the rest of the announcement will feel much more exciting and logical.

Now that you understand LoRA, the post should no longer feel “cool tech jargon + big numbers” shill. Impressive but abstract!

Now that you grasp LoRA as the “tiny post’it notes” trick that freezes the huge core model and only trains a minisule add-on layer (up to 99% frwer paramters to update), the whole thing snaps into place and becomes genuinely revolutionary.

The “billion-parameter training on a phone” claim stops sounding impossible.

Fine-tuning a 13B model normally means updating billions of numbers which needs massive VRAM and power that no phone has.

But with LoRA, you’re only training maybe millions (or fewer) of tiny adapter parameters. Add BitNet’s extreme compression (weights as simple as -1/0/+1), and suddenly that tiny update process fits in a phone’s limited RAM/battery.

The iPhone demo (13B fine-tine), and Samsung/Pixel 3.8B ones aren’t hype, they’re the direct result of LoRA slashing the workload by orders of magnitude.

The “biggest unlock” line hits hard. Paolo calls heterogeneous GPU fine-tuning the biggest unlock because LoRA + BitNet now works everywhere (not just Nvidia CUDA). Before, even if BitNet existed, personalizing it required expensive Nvidia rigs or cloud. Now your AMD laptop, Intel PC, Apple Silicon Mac, or flagship phone can do it.

LoRA is the key that democratizes that persaonlization step azcross hardware - no more “sorry, only works on $10K GPU”.

Privacy and “serve the people” vision feels tangible, not fluffy.

Without LoRA, “local private AI” would mean running a generic frozen model (no real personalization) or shipping your data to the cloud for fine-tuning.

LoRA lets the model learn deeply from your emails/docs/health data entirely on-device, with almost no extra cost. Your AI becomes truly “yours” - customized to your life, offline, private forever.

That’s why Paolo frames it as the “first real-world signal of a local private AI that can truly serve the people.”

“Era of Stable Intelligence” lands as a real shift.

Tether AI is the stablecoin moment for decentralized artificial Intelligence

“Stable” here echoes stablecoins: reliable, decentralized, user-controlled, and no central gatekeepers. LoRA + BitNet + cross-platform Fabric = the technical foundation to escape cloud dependency. What used to be locked in data centers (personalized frontier AI) now “escapes” to your pocket.

It’s not just faster/cheaper but it’s a philosophical unlock toward AI sovereignty.

In short, LoRA isn’t just a technique, it’s the unlock button that turns theoretical edge AI into practical reality today.

The announcement isn’t about raw model size anymore. It is about who controls the intelligence (you, on your device) instead of renting it from Big Tech.

With this lens, the post reads like a declaration of independence for personal AI, and the demos prove it’s not vaporware.

Super exciting if you’re into decentralization, privacy, or just hating monthly API bills.

Today we follow that heat all the way up the stack; from a single chip to a full-scale data center running AI workloads around the clock.

The Wattage Waterfall

A single logic operation at the level of a chip generates heat that demands cooling. A cluster of ten thousand chips demand a power grid. A training run demands that the grid run without interruption for weeks, and once a model deploys, inference demand runs the grid forever.

The Chip: The micro-unit of compute

A single H100 draws 700W of power at peak. One H100 server (8 GPUs) draws 10.2kW of total system power. A single GB200 NVL72 rack - Nvidia’s current Blackwell configuration draws ~120kW. A standard office building draws roughly 50-100 kW which means one AI training rack consumes more power than an entire office building. (1), (2), (3)

The Cluster: Where scale meets physics

A frontier model training cluster runs 10k to 25k+ GPUs simultaneously. At 700W per H100, a 10k GPU cluster draws 7MW continuously. xAI’s colossus cluster in Memphis is ~100,000 H100s drawing ~150-200 MW continuously. (1), (4), (5)

The training run level: The wattage it takes to bring GPT-4 to life

GPT-4 is estimated to consume ~50 GWh of power for training. This is equivalent to powering ~4,800 average US homes for a full year. For xAI’s Grok 4, estimates show ~310 GWh of power consumed for training, a 5-6x increase in magnitude over GPT-4. As models become bigger, the training compute will continue to expand, and Epoch AI estimates training compute to scale ~4x per year and by extension the energy cost of training scales at roughly the same rate. (5), (6), (7), (8)

The inference level: Where energy demand becomes permanent

OpenAI CEO Sam Altman disclosed in a June 2025 blog post that a single ChatGPT query uses ~0.34 watt-hours. Data compiled by Epoch AI independently corroborated Altman’s own estimates and calculates ~0.3 Wh per query. Altman equated this to an oven in usage for a little over one second, or a high-efficiency lightbulb in use for a couple of minutes in his blog. (6), (9), (10)

At 2.5B daily queries (or prompts) reported by TechCrunch citing OpenAI in July 2025 allows us to infer that the daily energy demand from inference alone costs 0.34 x 2.5B watt hours of electricity. In other words, ~850 MWh per day, which works to an annual run rate of 850 MWh x 365, ~310 GWh per year. (11)

As noted above, GPT-4 model training consumed ~50 GWh of electricity. A simple division shows that inference would consume roughly the equivalent of the electricity consumed in the overall training process in ~59 days (50,000 MWh ÷ 850 MWh/day = ~59 days). MIT further states that Inference’s share of the total AI lifecycle energy is ~80-90%, and reasoning models (o1/o3-class models) only escalate this consumption as data from Epoch AI shows 2.5x more tokens consumed by reasoning models over regular models. (6), (10)

The Data Center Level: Where it all aggregates

IEA estimates global data center energy consumption to double from ~415 TWh (c.1.5% of global electricity) to 945 TWh as the base case between 2024 and 2030. For context, this is equivalent to the entire current annual electricity consumption of Japan. A Goldman Sachs report further adds that data centers will command 8% of the total US power consumption by 2030 against ~3% in 2022. This massive demand for power explains the approx. $720B grid spending estimates by 2030 by Goldman Sachs, and an estimated ~$736B capex announcements by the top 5 hyperscalers in 2025-26 alone as outlined above. (12), (13)

Conclusion

The wattage waterfall makes the constraints of physics (thermodynamics) structural requirements. This brings us to the three primary conclusions relevant for all investors trying to build a foundational understanding of the AI trade.

First, power must be generated. Second, that power must be effectively transmitted across the racks, and third, these AI workloads will generate significant heat which must be removed from the environment.

These three requirements not only mandate large upfront investments in the build-out for power, grid, and cooling infrastructure; but also force them as structural, not cyclical requirements irrespective of who wins the model race.

Now that we’ve explained the constraints of thermodynamics in Part 1, and the current energy & cooling requirements for SOTA chips powering massive AI workloads, the next question is who builds it, who powers it, and who profits from it.

In Part 3, I'll map the exact companies whose business models exist to solve the physical constraints outlined above. The investment case for each one is anchored in physical laws that don’t change regardless of who wins the model race. Stay tuned!

If you found this useful, subscribe. Part 3 drops next week.

Citations

1. NVIDIA Corporation, "NVIDIA H100 GPU — Product Specifications," accessed February 2026.

2. NVIDIA Corporation, "NVIDIA DGX H100 Datasheet," March 2022. Document number A4-2146027-R3.

3. NVIDIA Corporation, "DGX GB200 NVL72 Datasheet," NVIDIA Documentation, accessed March 2026.

4. Pilz, K., Rahman, R., Sanders, J. and Heim, L., "AI Training Cluster Sizes Increased by More Than 20x Since 2016," Epoch AI, October 23, 2024.

5. James Sanders, Luke Emberson and Yafah Edelman, "What did it take to train Grok 4?" Epoch AI, September 12, 2025.

6. James O'Donnell and Casey Crownhart, "We did the math on AI's energy footprint," MIT Technology Review, May 20, 2025.

7. U.S. Energy Information Administration, "Electricity Use in Homes," updated December 18, 2023. Derived estimate: 50 GWh ÷ 10,500 kWh/household/year = ~4,762 households. Rounded to ~4,800 in text.

8. Sevilla, J. and Roldán, E., "Training Compute of Frontier AI Models Grows by 4-5x Per Year," Epoch AI, May 28, 2024.

9. Sam Altman, “The Gentle Singularity,” blog.samaltman.com, June 11, 2025.

10. You, J., "How much energy does ChatGPT use?" Epoch AI, February 7, 2025.

11. Dastin, J., “ChatGPT users send 2.5 billion prompts a day,” TechCrunch, July 21, 2025.

12. International Energy Agency, “Energy and AI — Energy Demand from AI,” IEA, April 10, 2025. https://www.iea.org/reports/energy-and-ai/energy-demand-from-ai

13. Goldman Sachs Research, "AI to drive 165% increase in data center power demand by 2030," Feb 4, 2025.

A Physics first lens to playing the AI trade

Physics is the baseline for the natural world, and also the absolute ceiling for any problem we solve (including artificial intelligence.) Everything above the ‘physics layer’ are human choices that can theoretically be changed. Confusing human choices for physics laws is what most get wrong.

The analysis that follows is grounded in the constraints and opportunities physics imposes. Hard science, not media narratives, will decide how to find the winning opportunities in the AI trade.

The physics that follows exists to serve the investment thesis, not academic completeness. The concepts most applicable are chosen to highlight the constraints, build the analytical foundation, and establish a new lens to evaluate the AI trade.

This framework also serves as a filter to fact-check every tall claim about AI disruption. physics is the reality check that prevents costly mistakes born of following the crowd rather than grounding analysis in reality.

The Physics behind the AI trade

At its core, the entire AI lifecycle from inception to end is governed by five primary physical fundamentals. Each manifests at various layers of the AI assembly line.

1. The law of Thermodynamics (Landauer’s Limit and Heat Dissipation)

2. The law of speed of light

3. The principles of Quantum Mechanics

4. The laws of information theory (Shannon’s theorem)

5. The law of Wave physics (Diffraction Limit)

Thermodynamics is already the dominant cost driver in AI infrastructure. The energy and cooling buildout it demands is leading capital allocation decisions across every stakeholder in the race to dominate AI. (1)

Data compiled by Goldman Sachs shows the five largest hyperscalers in the US will deploy a cumulative $736B in Capex for the AI buildout in 2025 and 2026 alone. Going further, McKinsey has projected a total investment of ~$5.2 trillion for AI data centres by 2030. (2)

Of that $5.2 trillion, McKinsey estimates $1.3 trillion flows directly into the energisers; the utilities, cooling system manufacturers, and electrical infrastructure providers whose entire business exists to solve the heat and power problem thermodynamics creates. This is the thermodynamics trade expressed in dollars.

The IEA projects the total demand for energy from data centres to double from ~415 TWh in 2024 to ~945 TWh by 2030, this is the equivalent to adding the entire electricity consumption of Japan in 2024 to the global grid in just six years. These figures establish the scale of what thermodynamics demands in capital terms, and the physics below explains why this demand is permanent. (3, 4)

Thermodynamics imposes physical energy constraints on chip compute capabilities

If you strip away AI to the precise point where physics shows up, it boils down to a single factor: transistors switching states. Billions of times per second, per chip. Those switching events are what execute the matrix multiplications underlying every model, and every switching event costs energy and generates heat.

Every compute operation is subject to the constraints imposed by the second law of thermodynamics. A constraint neither engineering nor software can solve. This is nature imposing its will on every transistor switch on every chip at every data centre ever built by humanity.

The physical law creates two distinct constraints on compute, one theoretical, and one practical. Understanding both builds the foundation for our thesis that the energy infrastructure trade isn’t cyclical capex but a permanent structural requirement.

Constraint 1: Landauer’s Limit - The Theoretical Floor of Compute

First introduced in 1961, the Landauer’s limit provides the baseline of the minimum quantity of heat dissipated in the environment to erase a single bit of information. At room temperature (~300K), the minimum heat released is 2.8 × 10⁻²¹ joules per bit operation. This is thermodynamics enforcing absolute limits no material, architecture, optimization or engineering can go beyond. This is the theoretical floor of all compute, including AI.

Peer reviewed literature on this topic argues that modern chips release roughly a million times more energy per logic operation which exceeds the Landauer’s limit by many orders of magnitude. (5)

While this sets the physics enforced ceiling on compute per watt, a better lens to understand this is backed by solid research published by Ho, Erdil & Besiroglu (2023) which ignores the Landauer floor and instead tackles the more important and immediate question, “how much more efficient can silicon transistors realistically get before hitting their own engineering limits?” (6)

The current state of the art CMOS architectures, the transistor-based chip designs running today’s AI GPUs are operating ~207x above the maximum efficiency Ho et al’s research outlines. In other words, there is capacity to improve chip efficiency to the order of 207x before we hit the practical limits of how much energy per logic operation these chips will consume.

While this may sound a lot, current AI compute demand is scaling 4-5x per year, and all the efficiency gains are consumed by demand growth before they can actually reduce energy infrastructure requirements. This explains the massive grid capex and hyperscaler expansion efforts in line with the estimates and data compiled by McKinsey and Goldman Sachs outlined above. (7)

Constraint 2: Heat Dissipation - The practical constraint of compute

Landauer’s principle is evidence of physics imposing its will on compute. Every switch event is generating heat as a consequence of executing logic operations within the constraints of physics.

Take the Nvidia H100 for instance, which draws ~700W per chip. The next-gen GB200 NVL72 rack draws nearly 120kW for 72 Blackwell GPUs, in comparison, a single H100 rack consumes 10-30kW. This is 4-12x increase in rack power density in a single generation of GPU offerings. This increase across generations isn’t a one-time thing either, mapping the energy consumption across four generations of chips starting with V100 (~300W in 2017), A100 (~400W in 2020), H100 (~700W in 2022), and the latest generation of GB200 (~1,000W in 2024) shows how each generation demands meaningfully more power and cooling than the last. (8)

Note: Citations 9-12 links to the original source material for the table data above.

At these rates of power consumption, liquid cooling isn’t just optional infrastructure, it is mandatory, and scales permanently with every watt deployed.

To sum up, Landauer’s limit establishes the physical cost of compute which forces generation and consumption of electricity, and heat dissipation mandates cooling infrastructure as heat scales permanently with compute. These aren’t cyclical capex driven by a temporary infrastructure gap. They are structural requirements imposed by physics.

Thermodynamics has already decided what the AI infrastructure stack must look like. The next question is who builds it, who powers it, and who profits from it. In Part 2, I'll map the exact companies whose business models exist to solve the physical constraints outlined above. The investment case for each one is anchored in physical laws that don’t change regardless of who wins the model race. Stay tuned!

If you found this useful, subscribe below. Part 2 drops next week.

Citations

1. Goldman Sachs Research, "How AI Is Transforming Data Centers and Ramping Up Power Demand," July 16, 2024.

2. Jesse Noffsinger et al., "The cost of compute: A $7 trillion race to scale data centers," McKinsey Quarterly, April 2025.

3. International Energy Agency, "Energy and AI — Energy Demand from AI," April 2025.

4. Enerdata via Statista, "Electricity consumption worldwide in 2024, by leading country," July 2025.

5. Chattopadhyay, P., Misra, A., Pandit, T., and Paul, G., "Landauer Principle and Thermodynamics of Computation,", June 2025.

6. Ho, A., Erdil, E., and Besiroglu, T., "Limits to the Energy Efficiency of CMOS Microprocessors," 2023 IEEE International Conference on Rebooting Computing, December 2023. . H100 today achieves ~1.4×10¹² FP16 FLOP/J. The estimated maximum CMOS efficiency ceiling is ~2.9×10¹⁴ FP16/J. That gap is approximately 207x.

7. Sevilla, J. and Roldán, E., "Training Compute of Frontier AI Models Grows by 4-5x Per Year," Epoch AI, May 28, 2024.

8. NVIDIA Corporation, "DGX GB200 User Guide — Hardware," NVIDIA Documentation, August 2025.

9. NVIDIA Corporation, “NVIDIA V100 Tensor Core GPU Datasheet,” January 2020.

10. NVIDIA Corporation, "NVIDIA A100 Tensor Core GPU Datasheet," May 2022.

11. NVIDIA Corporation, "NVIDIA H100 GPU — Product Specifications," accessed February 2026.

12. Lenovo, "ThinkSystem NVIDIA HGX B200 180GB 1000W GPU Product Guide," Lenovo Press, LP2226, January 13, 2026. https://lenovopress.lenovo.com/LP2226 — citing NVIDIA GPU specifications, Table 2.