This is 6th episode in the series of Build vs Buy vs Rent: The AI Infrastructure Decision Tree for Startups with 2 more to go!. Listen to the full episode here, in Substack app, or Apple, Spotify / youtube.

In practice, the money you spend on AI in the cloud tends to leak out in four specific areas – idle compute, oversized models, wasted tokens, and hidden data transfer fees.

Idle compute:

GPU instances left running between jobs, notebooks never shut down, development endpoints serving zero traffic. The fix is auto-shutdown after two hours of idle time for development environments, and scheduled shutdown overnight for staging.

Model over-provisioning:

defaulting to a frontier model for every task.

A routing layer sending simple queries to small, cheap models can cut inference costs by 40 to 70 percent with no user-visible quality degradation.

Token waste:

long system prompts, over-retrieved context, over-long generated outputs. At scale these add tens of thousands of pounds per month. and

Egress:

data transfer costs between regions or providers, typically 80 to 90 dollars per terabyte, adding 20 to 40 percent to apparent compute costs.

FinOps basically means “know where every AI pound is going, then fix the leaks.”

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First, you track every GPU, every AI API call, and every batch of tokens so you can see simple numbers like cost per user and cost per AI request. If it costs you ten pence to run an AI feature and you only charge five pence, the math will never work, no matter how impressive the top-line revenue looks

Then you run a 90‑day clean‑up: month one is wiring up the tracking, month two is killing the three biggest sources of waste, month three is locking in discounts and automatic safeguards.

You usually spend just a few percent of your AI budget on FinOps tools, and they can earn themselves back within the first quarter.

QuantOpinion

The Window of Light: Why Silicon Photonics Is Becoming Strategic Infrastructure for AI and Quantum

Silicon photonics now sits at the intersection of three powerful currents…

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3 days ago · 2 likes · Poonam Parihar

AI Unfiltered

When a Startup Reaches Orbit, the State Is in the Story!

India’s Vikram‑1 launch (Skyroot Aerospace) has been framed as a feel‑good story about a young team, a private rocket, and a nation joining the US and China in having a startup reach orbit. As founders watching from the sidelines, it’s easy to read the headlines, compare them to…

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4 days ago · 7 likes · Poonam Parihar

GeoIntel Wargamer - How We Built using Qwen Cloud

GeoIntel is a multi-agent AI “debate room” for geopolitics, where a Historian, Strategist, and Economist argue over real-world conflicts and then synthesize probability-weighted forecasts grounded in live web search. It’s inspired by how real intelligence teams use red-teaming and structured analysis, but packaged as software you can query like any other AI tool.

Let us know your feedback here :

GeoIntel Wargamer

does this feel useful, and

what kinds of questions or

scenarios would you want to throw at a system like this?

I did this deep dive around GTC 2026 (mid-March), in the immediate wake of Jensen Huang’s post-conference podcast blitz i.e. Ben Thompson, All-In, and a string of long-form interviews that reframed NVIDIA as a vertically integrated “AI factory” company rather than a GPU vendor. The I believe I did another update in april but din’t get a chance to publish then. At that time, the whole thesis seem to have rested on the Vera Rubin platform announcement, the DSX AI factory reference designs, the emerging agentic stack (NemoClaw/OpenClaw), and a handful of “what we missed” additions covering networking dominance, DGX Spark, BioNeMo, Omni-verse, energy/grid strategy, and the telco AI Grid pivot.

Four months on, the essay needed updating in three ways.

First, two full quarters of actual financial results have landed, replacing forecasts with hard numbers.

Second, the roadmap has hardened since, from “expected” to “confirmed and shipping,” with Vera Rubin now in full production. and,

Third, a genuinely new competitive and structural picture that has since emerged: custom silicon, not AMD, is now the more interesting threat, and NVIDIA seems to be restructuring its own reporting to reflect a broader physical-AI and edge ambition than the original report I did captured.

ANd, Few other points were about the circular-financing risk running through NVIDIA’s own investments in its customers, and the antitrust/regulatory pressure building across three continents, that I noticed, so I am adding them all and extending the map into everything that has happened since, including the telco story in full, and giving it bit more contested picture that I didn’t look in to earlier.

Table of contents

• What NVIDIA actually delivered since GTC (the numbers)

• The roadmap, confirmed: Rubin, Rubin Ultra, Feynman

• The competitive picture: who is really challenging NVIDIA

• The new businesses nobody was watching (networking, edge, life sciences)

• The telco angle in full: partners, holdouts, and the fight over AI-RAN silicon

• The quantum and robotics angle: where your own work intersects

• Circular deals and the financing risk underneath the boom

• Anti-trust and regulatory scrutiny across three continents

• What has changed for the worse: China and margin corrections

• The founder playbook: five plays and a decision framework

• What is likely next

What NVIDIA actually delivered since GTC (the numbers)

The clearest signal that the “AI factory” thesis was not hype is the Q1 FY2027 print, reported May 20 for the quarter ended April 26, 2026. Revenue hit a record $81.6 billion, up 85% year-over-year, with Data Center revenue of $75.2 billion, up 92%.

Net income surged 211% to $58.3 billion, and gross margin held around 74.9%, directly reversing the margin-compression narrative that circulated at GTC time. For full fiscal 2026, revenue reached $215.9 billion (up 65% year-over-year) with Data Center revenue of $193.7 billion, now representing 90% of total revenue, up from 78% two years earlier. The board also authorized an $80 billion buyback and lifted the quarterly dividend fivefold, from $0.01 to $0.25 per share.

Jensen Huang stated at GTC that the company had $1 trillion in committed orders through 2027 and that NVIDIA “is going to be short” on supply, thats a claim now largely borne out by the pace of bookings and the buyback confidence.

The roadmap, confirmed: Rubin, Rubin Ultra, Feynman

It is now a fact on the ground:

NVIDIA confirmed Vera Rubin entered full production at GTC Taipei on June 1, 2026, with partner shipments scheduled for the second half of 2026. The annual cadence has also been made explicit and re-confirmed as recently as July.

Blackwell now,

Vera Rubin H2 2026,

Rubin Ultra H2 2027, and

Feynman in 2028, with

a new Rosa CPU replacing Vera on that later timeline.

Rubin Ultra will use an NVL576 rack configuration (four compute chiplets per GPU, 1TB of HBM4E memory) delivering roughly four times the compute of the Rubin NVL144 generation, paired with Groq LP35 LPUs supporting the NVFP4 data format.

Feynman in 2028 is described as a “qualitative leap” -

that’s introducing 3D die stacking, custom HBM beyond HBM4e, the new Rosa CPU (designed to cut CPU development cycles from four years to two), and co-packaged optics for NVLink switches that will let racks scale to 576 or even 1,152 GPU packages, addressing the physical bandwidth limits of copper interconnects.

This optics shift is arguably the most underrated technical detail in the entire roadmap - it is NVIDIA’s answer to the physical ceiling on scaling further with copper.

The competitive picture: who is really challenging NVIDIA

I wanted to look at the competition a bit here.

NVIDIA holds roughly 80% of the AI accelerator market, with FY2026 Data Center revenue of $193.7 billion against AMD’s estimated $7–8 billion in AI GPU revenue (5–7% share). AMD’s relative position has grown fast - from under 1% to 5–7% in three years, but the absolute gap is widening, not narrowing, because NVIDIA’s base is so much larger.

The more interesting threat however is custom ASICs:

Broadcom’s AI ASIC revenue alone exceeded $20 billion in FY2025, and custom silicon shipments from cloud providers are projected to grow 44.6% in 2026 versus 16.1% GPU shipment growth.

Analysts increasingly describe the market settling into a three-tier structure by 2028:

NVIDIA retaining 60–75%, AMD reaching 10–15%, and custom silicon capturing 15–25%, concentrated mostly in cloud-locked inference workloads. and NVIDIA’s response to this? is to deepen its moat past silicon:

a $2 billion investment in CoreWeave, open-sourcing its Dynamo inference software and Nemotron models, and licensing Groq technology directly into its own roadmap rather than competing against it.

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The new businesses nobody was watching!

Several verticals that i thought were footnotes are now material. specially NVIDIA’s networking segment that’s built on the 2020 Mellanox acquisition generated over $31 billion for the full fiscal year and $11 billion in a single quarter (up 263% year-over-year), making Jensen Huang’s claim that NVIDIA is “the largest networking company in the world” defensible rather than promotional.

NVIDIA has also formally restructured its reporting into two market platforms -

Data Center (split into Hyperscale and ACIE: AI Clouds, Industrial, and Enterprise) and

Edge Computing (PCs, robotics, automotive, AI-RAN base stations).

On the consumer/prosumer edge, NVIDIA unveiled RTX Spark at Computex in June, extending the $3,999 DGX Spark “personal AI supercomputer” concept into a mainstream laptop and compact-desktop tier bundling CUDA, RTX, DLSS and TensorRT for “personal AI agents.”

Automotive has also expanded well beyond the original scope, with new partnerships across Hyundai/Kia, Uber’s full-stack autonomous vehicle stack, BYD, Geely, Isuzu, and Nissan on the DRIVE Hyperion platform, alongside a new Halos OS for AI-driven automotive safety.

The telco angle in full: partners, holdouts, and the fight over AI-RAN silicon

At Mobile World Congress in early March, NVIDIA secured a formal commitment from a much broader coalition - Booz Allen, BT Group, Cisco, Deutsche Telekom, Ericsson, MITRE, Nokia, SK Telecom, SoftBank, and T-Mobile - to build 6G on open, AI-native platforms, positioning NVIDIA at the center of a genuinely global standards push rather than a US-only story. NVIDIA is also a founding member of the AI-RAN Alliance, which has grown past 130 participating companies, and separately launched the AI-Native Wireless Networks (AI-WIN) project with Booz Allen, Cisco, T-Mobile, MITRE, and the ODC to build an all-American AI-RAN stack aimed specifically at accelerating the path to 6G ahead of other blocs.

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The performance gap between open-source and proprietary frontier models has collapsed.

DeepSeek V3 offers performance comparable to GPT-4o at 27 cents per million input tokens, compared to roughly 2.50 dollars for GPT-5. DeepSeek’s reasoning model costs 55 cents per million input tokens - 96 percent cheaper than equivalent OpenAI reasoning models.

The practical spending threshold is 15K pounds per month in API costs. Below that, the engineering overhead of self-hosting open-source models is not worth it.

Above it, the economics justify a proper evaluation.

Above 100 million tokens per month with adequate engineering capacity, open-source self-hosting is almost always significantly cheaper.

Proprietary models give you access to frontier capability with zero deployment overhead.

The trade-offs are vendor lock-in, no control over model behaviour or training data, and complete dependency on a vendor who can change pricing or deprecate model versions on their timeline. Open-source models give you full control, fine-tuning capability on proprietary data, and deployment flexibility including air-gapped environments.

Fine-tuning pays back when you exceed roughly ten thousand requests per day - below that, prompt engineering with a frontier model is more economical. Modern prompt optimisation techniques have been shown to outperform reinforcement learning fine-tuning by 6 to 19% points on benchmark tasks while using up to 35 times fewer compute resources.

Fine-tuning wins for very high volume token cost reduction and for hard-to-prompt output formats.

The model choice and the infrastructure choice are the same decision viewed from two angles.

Get clear on your volume, your compliance requirements, and your engineering capacity, and

the right choice usually becomes obvious.

This is 5th episode in the series of Build vs Buy vs Rent: The AI Infrastructure Decision Tree for Startups Listen to the full episode here, in Substack app, or Apple, Spotify / youtube.

Latest On AIUnfiltered -

GenAI Was the Warning Shot, Agentic AI Is the Next Test

Different angles, same battlefield: trust, safety, and control over information.

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Thinking Machines has released Inkling, its first open-weights frontier model - a 975-billion-parameter multimodal Mixture-of-Experts system trained on roughly 45 trillion tokens, paired with fine-tuning tooling and day-zero support from ecosystem partners. Well Great, But I am slightly confused.

Last Dec I wrote an essay on Thinking Machines, on their for extraordinary $2 billion seed round at a $10–12 billion valuation for Tinker, a fine tuning API / training service, which wasn’t even released.

$12 Billion for a Fine‑Tuning API?

They actually din’t stop on that. there were rumors of a follow-on raise at $50–60 billion valuation.

• Bloomberg reported in November 2025 that Thinking Machines was in early talks to raise a new round at around $50B, with some sources saying up to $55–60B.

• Multiple follow‑ups ( such as Dealroom, Reddit, SiliconAngle, StartupHub) repeated the same story: post‑seed, they explored a follow‑on round targeting roughly $50–60B, which ultimately did not close.)
  

“My issue? – Tinker looked like a narrow wedge into enterprise AI.”

• Tinker aimed at simplifying multi‑GPU fine‑tuning for LLMs, exposing low‑level primitives while hiding infra complexity.

• Coverage framed it as an infrastructure tool for researchers/startups rather than a broad enterprise platform: a way to customize models, & not a full stack product with its own demand engine

This was when there was no commercial traction was visible so nothing really justified that valuation jump that was being floated.

Since then the company has added -

1. a gigawatt-scale NVIDIA compute partnership,

2. expanded Google Cloud infrastructure,

3. a genuinely novel real-time interaction architecture,

4. a January 2026 personnel exodus, and

5. now a frontier-scale open‑weights model, whose own launch materials admit it is “not the strongest overall model available today, open or closed.”

So from a AI lab, is Thinking machines now a story about whether a company can build a durable business by owning the control layer of AI?

- the tuning, interaction, and deployment layer that sits between raw foundation models and enterprise workflows - and btw using architecture largely borrowed from DeepSeek, without a single valuation number moving in a year of real shipping.

Table of content

• Thinking Machines, Revisited: What I Got Right (And Missed)

• The Six Months That Changed The Thesis

• What Thinking Machines Says It Is

• The DeepSeek Lineage: Borrowed Brains, New Wrapper

• It’s All Branding, And That’s The Game

• The Real Battle: Control Plane vs Chat App

• Why They Didn’t Build Their Own Model

• Valuation Reality Check

• Can Packaging Be A Moat?

• The Cleanest Way To Explain All Of This

Part 1: The December thesis, and why it was right but incomplete

For those who’ haven’t read the last deep -dive, Let me do a short recap on their past years or few months history. The starting point was pretty straightforward. Mira Murati, OpenAI’s former CTO, founded Thinking Machines Lab in February 2025. Within roughly five months the company closed a $2 billion seed round, the largest in the AI boom to that point, with reported post-money valuations ranging from $10 billion to $12 billion across different outlets. TechCrunch reported $10 billion, while Reuters and Wikipedia cite $12 billion. Andreessen Horowitz led the round, with NVIDIA, AMD, Cisco, ServiceNow, Accel, and Jane Street participating.

At that point the company had exactly one product:

Tinker, a cloud-based fine-tuning API positioned to make multi-GPU tuning feel like working on a single device, exposing a Python interface and hiding distributed orchestration from developers.

There was no publicly released foundation model, no clear revenue disclosure, and a mission statement about “human-centered” AI that read more like branding than strategy. My point? - investors were pricing a company aggressively on a single, narrow product with no visible traction data or just on a Famous Founder.

Worst part? The market was already reading Tinker as more than a standalone product; it was being read as a wedge into a bigger “control layer” strategy for enterprise AI, and later reporting only strengthened that interpretation. What I likely missing in my December essay was the sequencing » the fact that Tinker was never going to be the end state. It was the entry point into a much larger stack that would only reveal itself over now?. But I am still not fully sold on this thought process here, but I’d go with whats visible so far of their journey. I also don’t see lot of people doing a deep dive, except a Contrary Research’s in‑depth business breakdown, Sebastian Raschka and other practitioners writing technical notes on Inkling’s architecture and benchmark behavior comparing it with Deep Seek’s by why exactly? when its already known to be “inspired” from it?

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Part 2: What happened in between - the timeline from Dec to Now.

Four major developments filled the gap between December and July, and each one deserves separate treatment because each changes what kind of company Thinking Machines actually is.

The NVIDIA deal, March 2026. Thinking Machines announced a multi-year, gigawatt-scale partnership committing to deploy at least one gigawatt of next-generation NVIDIA Vera Rubin systems, plus collaboration on training and serving architectures, alongside a significant additional NVIDIA investment on top of its earlier seed participation. On the surface this looked like standard NVIDIA playbook which is - invest, promise compute, strengthen hardware dependence and is exactly why it felt unremarkable at first glance. Strategically, though, it anchored Thinking Machines as a serious infrastructure-and-model company rather than an app/API startup, giving it the compute credibility needed to train and serve frontier-scale systems.

Google Cloud expansion, April 2026. The company expanded its use of Google Cloud’s AI hyper-computer, becoming one of the first customers for A4X Max instances built on NVIDIA Blackwell through Google’s infrastructure. This mattered less as a headline and more as confirmation that the compute story was not a one-partner dependency; Thinking Machines was diversifying its infrastructure base the way a company planning to operate at genuine scale would.

Interaction models, May 2026. This is the one genuinely original piece of technical work in the entire timeline. Thinking Machines published “Interaction Models: A Scalable Approach to Human-AI Collaboration,” introducing an architecture that processes multimodal input and output in roughly 200-millisecond micro-turns, breaking from the classic turn-based chat paradigm toward full-duplex conversation - a system that can listen while it talks. This part is NOT a DeepSeek clone or a borrowed pattern, it is actually a real research contribution, and it is the strongest evidence that Thinking Machines is building toward something beyond tooling and open weights.

Inkling, July 2026. and but finally, the model - 975 billion total parameters, roughly 41 billion active per token, a one-million-token context window, trained on approximately 45 trillion multimodal tokens, released with open weights and strong performance on audio benchmarks. TechCrunch called it the company’s “first public proof point after a year and a half building AI infrastructure largely out of public view.”

Sitting between these milestones was a fifth event worth naming precisely, because the loose version overstates it and the previous drafts underweighted it. In January 2026, several team members, reportedly including co-founder Luke Metz left and returned to OpenAI within the same week. Public reporting ties the departures to a disagreement over research direction and values, with one person leaving over an ethical disagreement and taking a handful of colleagues with him, rather than a mass exodus or evidence the underlying strategy had failed. This is a legitimate distinction -

the product roadmap did not stall. Interaction models, the NVIDIA and Google Cloud expansions, and Inkling all shipped on schedule during and after this period. So It was likely a personnel and culture story, not a strategic one, and it should be recorded honestly rather than folded into an indictment of the business model.

Put this all together, the story sequence now starts with

• tuning,

• layer in high-scale compute and deployment,

• weather a personnel disagreement without missing a product beat,

• publish a genuinely novel interaction architecture, and

• finally ship a model whose weights you can actually own and adapt.

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Part 3: But What Thinking Machines actually said it wanted to be

Thinking Machines says it wants to make AI systems more widely understood, customizable, and generally capable, and to build AI that extends human will and judgment rather than replacing it.

Translate this into product strategy, and this reads as three pillars.

First, train strong models - frontier-level multimodal systems like Inkling that reason across text, image, and audio and serve as a base for further adaptation.

Second, build tools for customization - Tinker and related APIs as the layer where enterprises and developers fine-tune, post-train, and shape model behavior to their own tasks, policies, and data. and

Third, develop richer interaction and deployment interfaces - the micro-turn, full-duplex architecture that lets humans and models collaborate in real time rather than through static one-shot prompts.

That combination is not “we have a model plus an API.” It is closer to a control plane for AI:

a place where organizations tune, monitor, and interact with models in ways that encode their own values, operational constraints, and workflows.

The underlying bet is that the bottleneck in AI adoption is shifting from raw capability to fit. -

• fit to domain,

• fit to policy,

• fit to human judgment.

and if I am right here, the most valuable layer in the stack is not necessarily the largest model but the system that makes models adjustable and deployable in the real world.

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Part 4: The DeepSeek lineage - is borrowing the architecture a the strategy, not a failure of it

Honestly, I am skeptical on this one. This is the detail that reframes everything else, and it deserves to be stated plainly rather than softened.

Deep Seek v4 was making much news earlier in March. the whole drama about release timing, geopolitics, and pricing pressure rather than just raw benchmark which btw made lot of folks in West nervous. It’s become a kind of Rorschach test for open‑weights, China’s AI ambitions, and the sustainability of the current model‑provider business.

DeepSeek V4

so now when Inkling has arrived, which is an example of what you can build on top of a good-enough V3‑lineage base. DeepSeek V4 has already sucked most of the oxygen out of the room:

a noisy preview cycle, hand‑wringing in Washington and Brussels about “AI dumping,” and a model that mattered less for its evals than for what it signaled about China’s ability to ship cheap, near‑frontier open‑weights at scale. But still Lets give it some time -

Inkling’s parameter profile mirrors V3 lineage almost exactly in shape:

None of this is hidden.

It is, in fact, the strategy working as designed.

Thinking Machines has never claimed it wants to win the architecture-invention race. Its own launch materials admit Inkling “is not the strongest overall model available today, open or closed” - an unusual admission for a frontier lab, and one that only makes sense if the business does not depend on topping leaderboards.

Wired’s coverage adds a stranger footnote from training:

the model at one point spontaneously decided that writing out its reasoning in natural language was inefficient overhead and stopped doing it, forcing researchers to reinstate legible chain-of-thought so outputs stayed explainable - a reminder that even borrowed architectures produce genuinely unpredictable behavior at scale, and that the post-training and safety work required to ship them is real engineering, even if the base design is not novel.

Spending years trying to out-architect DeepSeek would have delayed the thing that actually matters to this business model?

for example, getting a credible open base into developers’ hands quickly, then monetizing the tuning, evaluation, and infrastructure layered on top of it.

DeepSeek did the hard, path-breaking architectural work.

Moonshot is pushing the same open lineage further with Kimi K3, which now trades blows with Claude and GPT on coding leaderboards.

Thinking Machines is not trying to compete at that layer. It took a proven architecture, scaled it, trained and post-trained it at significant engineering cost, and wrapped the result in tooling, infrastructure, and an interaction layer that is, notably, its own invention.

That is not frontier invention at the model layer.

It is coherent, deliberate assembly at the platform layer - and the distinction matters enormously for how the business should be judged.

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Part 5: This is all branding - and that is the point!

A fair objection raised throughout this deep dive is that “human-centered,” “trustworthy,” and “open” tags, all of which are largely PR wrappers, and the pattern is visible across the entire industry, not just at Thinking Machines, so they are just following the herd.

Anthropic markets trustworthiness while repeatedly navigating security controversies.

OpenAI marketed “open” while becoming one of the most closed labs in the industry and absorbing assets like OpenClaw into a tightly controlled ecosystem.

The useful move is to separate messaging from mechanics entirely: “human-centered” means “we want the product framed as augmenting people,” but tells you nothing about the actual moat.

“Trustworthy” is shorthand for safety positioning or compliance posture. “Open” in AI has become especially slippery.

The real questions that cut through the branding are structural:

who controls the weights,

who controls fine-tuning,

who controls deployment,

who controls the data flywheel, and

who captures the enterprise relationship.

That is why “open” companies can still be very closed in practice, and “safe” companies can still be aggressively competitive. the branding exists to reduce friction with users, talent, regulators, and investors, while the business model does the actual work underneath it.

For Thinking Machines specifically, the interesting part is not the slogan but the structure:

Anthropic enters through safety, OpenAI through breadth and convenience, Thinking Machines through controllability and leverage, but all are trying to own the same critical layers: model ownership, customization, deployment, distribution, and enterprise lock-in.

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Part 6: The competitive map - control plane, not chat app

I was wondering if Tinker was ever a flagship product or would Inkling be. and I’ll explain why. It din’t have a fancy launch. and a limited release to few research orgs. Now Compare this with Anthropic and Open AI, the fellow AI labs. Claude and ChatGPT solved a fundamentally different problem than the one Thinking Machines is solving. They became things people ask for by name - habits, defaults, verbs. Thinking Machines shows no visible effort to build anything like that:

Compared against xAI, the contrast is instructive.

xAI is pursuing a vertically integrated, full-stack strategy:

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This is 4th episode in the series of Build vs Buy vs Rent: The AI Infrastructure Decision Tree for Startups.

TL;DR

• Above ~70% GPU utilisation, owning hardware usually beats the cloud on cost.

• The real break‑even sits roughly between 55–75% utilisation, depending on power, amortisation, and cloud pricing.

• Lenovo’s TCO work: ~8x cheaper than cloud infra and up to ~18x cheaper than frontier APIs per million tokens.

• At 10B tokens/month, three‑year savings vs pure APIs can exceed £2M.

• 8x H100 server: $250k–$400k upfront plus $3k–$5k/month to run; ~$11k/month effective cost over three years.

• Equivalent cloud H100 capacity: roughly $14k–$20k/month.

• You must factor in power (6–10 kW per rack unit), UK colocation (£500–£2,000/rack/month), and infra engineers (£80k–£140k/year).

• A hybrid model (own the baseline, rent the spikes) can cut AI infra spend by ~40–60% vs pure cloud.

  Intelligent Founder AI is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.

At around 70 percent GPU utilisation, owning on‑prem infrastructure is usually the better deal for AI inference. Below that level, the cloud’s flexibility earns its premium because you’re not paying for expensive hardware that sits idle when traffic drops. In practice, the break‑even lives somewhere between 55 and 75 percent sustained utilisation, depending on things like your electricity rate, how many years you plan to amortise the kit, and which cloud pricing tier you’re comparing against.

Lenovo’s 2026 total cost of ownership work puts real numbers to this. They found that self‑hosted GPU infrastructure can be about 8 times cheaper per million tokens than raw cloud infrastructure, and up to 18 times cheaper than using frontier model APIs. Once you’re at around ten billion tokens a month, the three‑year cost gap between owning hardware and living entirely on cloud APIs can easily exceed two million pounds.

The sticker price on an 8‑GPU H100 box is not small. You’re looking at roughly 250,000 to 400,000 dollars upfront for the server itself. Then you add 3,000 to 5,000 dollars a month in operating costs for colocation, power, cooling, and maintenance.

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Buying similar H100 capacity on‑demand in the cloud typically ends up between 14,000 and 20,000 dollars a month, so the saving is real and it compounds over time.

Where founders often get caught out is in the hidden line items.

You need proper co-location, and in the UK that runs about 500 to 2,000 pounds per rack per month. You also need engineers who can run GPU infrastructure safely and reliably, and UK market rates put that at roughly 80,000 to 140,000 pounds per person per year. On top of that, you’re carrying hardware risk:

once you buy, your performance ceiling is locked in for the amortisation period while cloud alternatives quietly keep improving in the background.

That’s why most serious AI teams don’t go “all cloud” or “all on‑prem” for long. The model they converge on is hybrid: own enough hardware to cover your predictable baseline workloads, and use the cloud when you need to absorb burst traffic.

It’s where most AI companies end up once their volume of inference forces them to care about infrastructure as more than a line item.

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This week Intelligent Founder AI and AI Unfiltered broke into Substack’s “Rising in Technology” leaderboard at #88—together, after already hitting #98 in Business earlier this year. Thank you for all the support so far.

AI Unfiltered is where we track the latest AI news, investigations, and “what just happened?” moments and, more importantly, what they actually mean for operators, founders, and buyers. Think model‑on‑model training fights, national‑security angles, regulatory shifts, and how all of that moves the ground under your product roadmap.

Intelligent Founder AI goes deeper: long‑form dives into technical and business architecture, operator‑grade GTM, and governance playbooks for getting AI into real enterprises and critical infrastructure without getting lost in the hype. It’s where we turn those headlines into concrete frameworks you can actually run inside your own stack.

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one keeps you on top of what’s changing week to week, the other helps you re‑wire your architecture, strategy, and sales motion around it. If that sounds useful, check out both, hit subscribe, and tell us what you want us to dissect next.

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Most people do not really care about Grok as a product or at least they openly claim to? and I say that from personal experience. But they actually do care about what Grok reveals, for example -

how AI systems gain attention without earning trust,

how founder ideology can shape model behaviour,

how weak guardrails turn product differentiation into public risk, and

why the gap between cultural popularity and institutional credibility is becoming one of the defining fault lines in AI.

And this is exactly why I feel Grok matters more right now than anyone could or would give it credit too.

For founders, operators, and anyone building with language models, the real subject is not Grok alone. The real subject is the future operating system of AI products:

what makes them trusted,

what makes them dangerous, and

what design choices quietly decide which one they become.

and hence this deep dive. so lets begin!

Table of Contents

1. The setup

2. Why this became a story at all

3. Why Grok feels unpopular outside X

4. Famous, infamous, or both

5. The bad-boy model question

6. What is actually wrong, and what can be fixed

7. Why Grok still helped the AI field

8. What most people still miss

9. Lessons for model builders and founders

10. A minimal safety stack for small or closed-loop models

11. What intelligent founders should take away

The setup

Grok has become one of those rare technology products that can only be understood properly once the labels start piling up. First it is “unpopular.” Then it is “famous.” Then “infamous famous.” Then the “bad boy of the model world.” And then, the conversation shifts again and the more serious questions arrive:

what is actually broken, what is merely messy, what cannot be fixed by tuning alone, and why did so many of its failures end up teaching the rest of the AI industry something useful about guardrails, governance, and the cost of learning too late.

This sequence matters because each label captures a different layer of the same phenomenon.

“Unpopular” points to weak trust in serious workflows.

“Famous” points to distribution and cultural reach.

“Infamous” points to the fact that notoriety, scandal, and moral discomfort became part of the product identity.

“Bad boy” points to a deliberate market posture built around lower restraint, sharper edges, and rebellion against the norms shaping rival models

Put all these together, and those labels describe more than a chatbot. They describe a product that became a brand, a governance stress test, a culture-war object, a regulatory catalyst, and a live founder lesson all at once.

Seen from a founder’s perspective, the interesting question is not really whether Grok is simply good or bad, and I am not really doing it for that reason either. The question I am curious about is how a model with real capability, massive distribution, intense cultural presence, and obvious technical momentum still managed to create such a large gap between attention and trust. That gap is where the real story lives.

New on AIUnfiltered - Apple vs Open AI.

The Great AI Heist

Why this became a story at all

Grok became a serious story not because it was the best model, but because it sat at the convergence of three forces:

Elon Musk’s distribution and personal brand, the unique realtime affordances of X, and repeated controversies that pushed it into mainstream awareness. Many AI products are competent. Very few are culturally loaded. Grok became culturally loaded almost immediately.

The Tesla angle also sharpened this story. recently there were reports of Tesla engineers preferring Claude over Grok, despite internal AI-spend caps that exempted Grok and internal pushes to use more AI, made the question less about hype and more about revealed preference. and that somehow also prompted me to pick it up for deep dive. because when technically sophisticated users choose a rival product even under incentive pressure, that says something deeper than internet commentary does.

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Why Grok feels unpopular outside X

I dont’ spend much or any time on twitter, except for some distribution, but from I do see grok is frequently referred / referenced with in the platform. Outside X however, Grok seems to have three linked disadvantages:

weaker standalone distribution,

a less trusted brand, and

a value proposition that does not map cleanly onto the highest-value enterprise or engineering workflows.

Historically, access was tied to X subscriptions, which made the product feel like a social-network feature rather than a first-class AI platform, and that alone may have created some friction relative to ChatGPT, Claude, and Gemini, I presume.

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At the same time, users and organisations worried about privacy and data boundaries because X used public posts to help train Grok and required users to opt out rather than opt in. For many enterprise buyers, that sort of data posture immediately raises concerns about governance and control.

Then there is also the product-fit issue.

But outside X, most users evaluate AI products for coding, writing, research, planning, or enterprise support, where Grok is often seen as merely competitive rather than category-defining.

Famous, infamous, or both

Grok is unquestionably famous in the awareness sense. It is constantly associated with Elon Musk, amplified by X, and repeatedly discussed across news, social media, and AI commentary. But the emotional valence of that fame is often negative or at least unstable.

That is why “infamous famous” is such a useful phrase.

Grok is known by far more people than regularly use it for serious work, and much of that awareness stems from harmful or bizarre incidents rather than admiration for steady performance. Anti-Semitic outputs, conspiracy references, sexualized image generation, and public debates over unsafe “unfiltered” AI all fed the notoriety loop.

Consumer trust data reinforces this distinction.

In one major survey summary reported by CNET, Grok sat at the bottom tier of trust among major AI systems, tied with Perplexity and behind Gemini, Copilot, Claude, and ChatGPT.

So the public knows it, but does not trust it proportionately/.

The bad-boy model question?

We evaluated it against five other orchestrator configurations on WANDR. It scored higher than every other configuration at roughly half the cost of Opus 4.8.

Calling Grok the “bad boy in the model world” is not just internet exaggeration. It reflects a real market identity built on irreverence, lower guardrails, cultural provocation, and the promise of saying things other systems would avoid.

Musk and xAI repeatedly framed Grok in opposition to supposedly censored or over-sanitised AI rivals, which gave it a rebellious identity by design.

That said, the label can obscure the seriousness of what happened. “Bad boy” sounds kinda playful, but some of Grok’s failures were not. They included harmful image-generation behavior involving vulnerable people, virulent hateful outputs, and incidents serious enough to trigger legal, regulatory, and ethics commentary at a national level. So the phrase is descriptively useful but morally too soft if used on its own.

What is actually wrong? and what can be fixed.

Output filters, image moderation, model wrappers, app UX, enterprise controls, rate limiting, admin features, audit trails, and safer defaults are all standard engineering and product tasks.

Other vendors have shown that much better behaviour is achievable in practice, which means many operational defects are not intrinsic to the model category itself.

Its raw model quality is also not the whole issue. By 2025–2026, comparative reviews increasingly treated Grok 4 and related variants as capable frontier-class systems in some contexts, even if they did not clearly surpass Claude Opus or GPT-5-level models overall.

Grok is not a toy.

and The deeper problem is that capability without disciplined governance magnifies rather than solves risk.

What is structurally harder to fix is the governance architecture.

Grok’s most serious controversies repeatedly pointed back to a combination of founder override power, a philosophy of reduced constraint, and weak evidence of independent safety vetoes or durable institutional checks

If the system is governed by someone ideologically motivated to periodically remove or weaken constraints, then technical fixes remain vulnerable to reversal.

The X integration compounds this. A model tightly coupled to a live social platform can turn failure into mass distribution immediately, and the incentives of virality or political signaling can conflict with the slower discipline required for safety engineering.This is exactly why Grok became a case study not only in model behavior but in “ungoverned AI.”

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Why Grok still helped the AI field, and it should get credit for!

One of the strongest insights in this entire story is that Grok has, in a painful way, helped the broader AI field by making latent safety arguments concrete.

Researchers, policy experts, and legal commentators treated Grok as a wake-up call because it showed that weak or intentionally reduced guardrails create highly visible harms at scale. In that sense, Grok functioned like a public stress test for the industry.

It demonstrated that safety is not a cosmetic layer.

Guardrails can decay over long conversations.

image systems can become abuse tools at network speed.

founder-driven retuning can rapidly destabilise outputs, and

moderation cannot catch up once harmful generation becomes easy and viral

Those lessons are valuable and now shape how many people talk about regulation, oversight, and model release discipline/

But the moral asymmetry matters.

The lesson was useful to the field, but the cost of learning it fell unevenly on real people whose likenesses, identities, or public discourse environments were used as the proving ground. So Grok was useful as a cautionary tale, but not in a clean or admirable way.

What most people still miss

One thing many people miss is that Grok is not simply “unpopular.” On some consumer metrics it has been meaningfully successful, including strong US mobile-app share growth during periods when mainstream commentary was overwhelmingly negative.That means notoriety may have helped consumer growth even while damaging enterprise trust.

Another under- appreciated point is the financial strain.

xAI has reportedly been spending at extreme levels on infrastructure and training while still showing modest revenue relative to valuation and burn. This means Grok is not just a product controversy story; it is also a capital-intensity and monetisation story.

The SpaceX and Tesla empire dimension is also often overlooked. Tesla invested in xAI and nudged internal usage toward Grok while some staff reportedly preferred Claude, and SpaceX later acquired xAI, tying Grok more closely to one of the most strategically sensitive private companies in the world. This transforms Grok from a chatbot narrative into a group-governance and cross-company power story.

There is also the cultural dimension. Grok became a character in meme culture, social reels, and companion-style discourse in a way that few AI systems have. That mythology can sustain attention and adoption, but it also locks the product into a dramatic identity that makes calm trust-building harder.

Values, corpora, and hidden priors

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The global GPU rental market grew from 3.2 billion dollars in 2023 to a projected 9.8 billion by 2025. That growth created a new category of infrastructure provider called neoclouds or GPU-as-a-Service.

CoreWeave is the largest, with trailing revenues around 3.5 billion dollars. Lambda Labs, Crusoe, RunPod, and Spheron cover different segments of the market, from enterprise multi-year contracts to developer-friendly hourly access to sustainability-focused compute. and what they all have in common is that they are cheaper than hyperscalers for GPU-intensive AI workloads.

Current pricing for H100-class compute runs between 1.80 and 3.50 dollars per hour on specialist providers, with spot instances as low as 1.20 dollars per hour. AMD MI300X alternatives often come in 30 to 40 percent cheaper than Nvidia H100 for equivalent inference throughput.

There are Four pricing models available:

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1. on-demand by the hour,

2. reserved instances with 30 to 60 percent discounts for committed periods,

3. spot instances that can be interrupted, and

4. bare metal for teams at significant scale.

The strategy maps directly to workload type.

Variable traffic: on-demand.

Stable production inference: reserved.

Training and batch jobs: spot.

High-throughput inference: bare metal.

The most commonly missed cost is egress.

Moving data out of a cloud environment typically costs 80 to 90 dollars per terabyte. For applications with larger inputs or outputs, egress can add 20 to 40 percent to the apparent cost of GPU rental. and It is almost never included in headline pricing comparisons.

SO, the right way to use GPU rental markets is as a bridge.

Validate on APIs, build observability into your utilisation patterns, then move to reserved GPU rental once your traffic is predictable enough to commit.

That is the staircase: API to reserved rental to owned hardware, moving up each step only when the data justifies it.

Listen to the full episode here, in Substack app, or Apple, Spotify / youtube.

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Telecom is crossing a threshold that will define the next decade of the industry. Operators have used automation, analytics, and isolated AI use cases to squeeze efficiency out of legacy operations, but those efforts have largely left the underlying operating model intact. What is happening now is different:

The old model was built for a slower, more predictable world. Network operations centers relied on static rules, manual triage, ticketing systems, and human escalation because traffic patterns and service behavior changed at a manageable pace.

Today, networks span radio, transport, core, cloud, edge, enterprise, private infrastructure, and increasingly non‑terrestrial systems, with traffic and service demands shifting in real time. Enterprises expect guarantees rather than best effort. In that environment - manual operations do not just become expensive; they become too slow to stay competitive.

Table of contents

1. Why telecom is at an inflection point

2. From AI-enabled to AI-native

3. Why the old operating model is breaking

4. Data as the foundation of autonomy

5. Humans, agents, and the new operating model

6. How AI-native operations improve opex and resilience

7. From connectivity to outcome-based services

8. Enterprise use cases: slicing, SD-WAN, and non-terrestrial networks

9. The shift from reactive operations to proactive assurance

10. What changes inside the organization

11. New metrics for an AI-native network

12. What operators and vendors should do next

13. “what to do on Monday” playbook

From automation to AI-native

The industry has responded with automation for years, but automation alone is no longer enough. Traditional automation executes predefined tasks faster, yet still depends on human‑designed rules and brittle workflows. AI changes the equation because it can interpret context, identify patterns, predict failures, and recommend or even execute actions dynamically.

This is why the conversation has moved through three phases:

from automation, to AI‑enabled operations, and now to AI‑native operations.

• AI‑enabled operations treat AI as an add‑on to existing processes.

• AI‑native operations rebuild the operating model around intelligent systems.

• In AI‑native environments, AI agents are not peripheral tools; they form part of the execution layer.

• Agents monitor, analyze, decide, act, and learn, in closed loops.

That distinction matters. In a traditional model, humans are the primary operators and AI assists them.

In an AI‑native model, humans supervise intelligent systems that perform much of the operational work themselves.

People still matter, but as overseers, exception handlers, policy designers, and strategic decision‑makers rather than first responders to every fault and deviation.

Why the legacy operating model breaks

Two structural pressures are driving the shift. First, network complexity is growing faster than human teams can scale. Second, telecom economics are tightening ie cost, margin, and retention pressures, all are rising simultaneously.

Legacy operations struggle because:

• Every new service and domain adds non‑linear operational overhead.

• Fragmented OSS/BSS stacks make end‑to‑end visibility and correlation difficult

• Manual workflows cannot match the speed at which events and dependencies propagate.

• “More people and more scripts” does not scale in a world of dynamic traffic and programmable networks.

AI‑native operations are attractive because they address complexity and economics in one move: they reduce toil, improve service quality, and unlock new revenue opportunities grounded in assured outcomes.

Data: the real foundation of autonomy

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AI‑native operations begin with data architecture, not model choice.

For AI to be trusted in execution, operators need:

• Unified data fabrics spanning network, IT, service, and customer layers.

• Clear governance for ownership, quality, validation, lineage, and security.

• Shared semantics so different systems and agents interpret telemetry and events consistently

• Real‑time access so models act on the live state of the network, not stale snapshots

Without this, autonomy collapses into isolated automation pockets. With it, AI agents can reason over a coherent view of reality and coordinate decisions across domains

Governance is not just a compliance box to tick. Once operators allow systems to make decisions or trigger actions in real time, data governance becomes the safety layer that underpins trust, auditability, and explainability.

From human-led to agent-led operations

AI‑native operations also change the relationship between humans and digital employees.

Traditional telecom organizations are siloed: network, IT, field operations, customer care, and product teams often work with different systems, metrics, and incentives

Agentic AI begins to cut across those boundaries.

• Agents correlate events from multiple domains, not just one stack.

• Field engineers use AI copilots for troubleshooting instead of relying solely on back‑office calls.

• Customer‑facing bots increasingly resolve issues end‑to‑end rather than handing off to multiple teams.

• AIOps platforms automate detection, triage, and remediation in operations centers.

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In this environment, AI becomes the connective tissue of the operating model. It does not replace humans, but it changes where human effort is most valuable.

• Routine orchestration and correlation are handled by agents.

• Humans focus on policy, escalation, architecture, and complex judgment calls.

• Workflows become more fluid and less dependent on manual handoffs.

BT’s “Dark NOC” initiative with AWS is a concrete example:

agentic AI and generative AIOps monitor the network, automate management and diagnostics, and proactively handle issues on the path to zero‑touch network operations.

Quantifying the business case

The economics of AI‑native operations are increasingly backed by hard numbers. Across studies and live deployments, autonomous and AI‑native approaches show consistent impact on opex, resilience, and customer outcomes.

Evidence points to:

15–20% opex reduction at early automation levels, rising to 25–30% at more advanced stages, and 40–50% at full Level 4 autonomous networks, especially in energy and power cost reduction.

Autonomous networks delivering 1.7–3.4x ROI with payback in roughly 1.5–2.9 years, and potential opex savings of $150–300 million over five years per operator.

AI‑native BSS/OSS and autonomous support contributing to an average annual benefit of $800 million per CSP, including $350 million opex reduction and $144 million revenue uplift.

AI‑driven help desk bots cutting cost per call by 35% and increasing resolution rates by about 60%.

Hyper‑personalized upsell campaigns driven by AI delivering 5–15% ARPU uplift, depending on segment.

Early AI‑enabled services (e.g., anti‑spam/anti‑scam) driving measurable ARPU growth for operators like Indosat Ooredoo Hutchison.

These are not just pilot numbers, they are production‑scale signals that AI‑native operations can attack cost, protect margins, and selectively grow revenue

Connectivity is being replaced by outcomes

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Telecom has long struggled with commoditization.

Basic connectivity is hard to price at a premium when every competitor offers similar bandwidth. Customers, especially enterprises, will pay more for certainty, performance, and outcomes. AI‑native operations make those outcomes deliverable and defensible.

Consider three domains:

1. Enterprise connectivity and slicing

   • Static, preconfigured services can connect sites but struggle to adapt to changing loads and SLAs.

   • AI‑native operations enable intent‑based slicing, predictive resource allocation, and dynamic QoS guarantees

   • The value proposition shifts from “we connect you” to “we guarantee this experience under these conditions.”

2. SD‑WAN and hybrid enterprise networks

   • Conventional SD‑WAN focuses on routing but often treats performance as best effort.

   • AI‑native SD‑WAN anticipates load, adapts to real‑time conditions, and assures performance dynamically

   • For operators, that creates room for premium, SLA‑backed offerings for customers, it also creates confidence in business‑critical applications.

3. Non‑terrestrial and remote connectivity

   • Satellite and terrestrial systems are frequently managed separately, limiting intelligent handover and optimization

   • Agentic AI and digital twins can make switching decisions context‑aware, factoring in weather, cost, and link quality.

   • That unlocks viable service models for remote, maritime, and underserved regions that previously carried high operational risk.

In each case, connectivity becomes the input. The product is an assured outcome. Assurance becomes the differentiator.

The commercial layer is the experience that can be guaranteed and monetized.

Architecture for AI-native operations

Under the hood, AI‑native operations converge several architectural elements into a cohesive system.

Key building blocks include:

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There are now over 69 providers offering LLM inference. Prices range from 14 cents per million input tokens at the cheap end to 180 dollars per million output tokens at the frontier. That is a 1,280-times gap.

Most founders are somewhere in the middle without a clear reason to be there.

The calculation is somewhat straightforward once you do it.

lets take your daily active users, multiply by interactions per user per day, multiply by tokens per interaction - split into input and output, because output tokens are typically two to four times more expensive, and run that number through the per-token prices of three or four providers.

The result is often a shock.

Three things determine whether the API route makes sense.

Volume: below fifteen thousand pounds per month in API spend, the engineering overhead of self-hosting is not worth it.

Predictability: APIs absorb traffic spikes without operational overhead, and that flexibility has real value for early-stage products.

Model quality requirements: not every task needs a frontier model. Routing simple queries to smaller, cheaper models can cut inference costs by 40 to 70 percent with no user-visible quality loss.

This is how the vendor lock-in risk gets real and underestimated.

When you build on a single API provider, you are locked in not just contractually but through accumulated prompt engineering work, evaluation datasets tuned to that model’s behaviour, and observability tooling built around that provider’s response format. The exit cost from a fully-loaded enterprise AI stack has been estimated at between two hundred thousand and one million dollars in re-engineering.

The mitigation is simple: use an abstraction layer between your application code and the vendor API.

LiteLLM and Portkey both do this. It adds minimal overhead and lets you switch providers with a configuration change.

The cheapest route from day one is not the cheapest route at scale. Start tracking your cost per inference from launch.

Listen to the full podcast episode for the full breakdown.

This episode is second in the series Build vs Buy vs Rent.

see you in the next episode.

Note : APIs are sometime pronouced APees, apologies for that, I let Elevenlabs run wild.

It looked like a meme. Elon Musk is seen “vibe coding” with an AI model, the internet does its usual thing, and then almost immediately SpaceX moves to buy Cursor in a $60 billion all-stock deal. If that is all you saw, the obvious interpretation is that Musk discovered a new toy, got excited, and did what Musk does best: turned fascination into acquisition. but no deals happen overnight. so,

I’d say its a good clever PR. 👍

The Cursor move makes far more sense, when I view it as a follow-up to SpaceX’s xAI acquisition I wrote about here a few of months ago.

SpaceX acquired xAI for $1.25 trillion

SpaceX had already merged with xAI in a record-setting transaction that combined compute ambition, frontier models, physical infrastructure, and the global distribution under one roof. Cursor is not some random add-on to that stack.

This is why this story matters. Not because “vibe coding” suddenly became cool, but because it reveals where the next strategic control point sits.

The first wave of AI competition was about who owned the model.

The next wave is about who owns the environment where models actually do useful work.

A quick recap to what i wrote in feb about : The Five-Layer Cake

In tthe last post my argument was straightforward: Musk was not actually building a chatbot company, but he was assembling a vertically integrated AI empire. The five-layer cake looked something like this:

1. Energy and logistics

2. Chips and manufacturing

3. Compute and data centres

4. Models and AI platforms (xAI, Grok)

5. Distribution: rockets, satellites, robots, social, government contracts

The point of that framework was never just that Musk owned a lot of impressive assets. The point was that these layers reinforced each other.

Energy powers compute.

Compute trains models.

Models gain leverage through distribution.

Distribution feeds back data, contracts, and cash flows into the rest of the machine.

That is how a stack stops being a collection of businesses and starts acting like a strategic system.

That earlier thesis also came with a warning for founders, that the middle is very likely getting squeezed. Generic AI wrappers and broad horizontal tools would find themselves trapped between giant integrated stacks above them and high-trust, domain-specific players below them. That was true when the conversation was mostly about models. It is even more true now that the control surface is shifting higher up the stack.

What makes the Cursor development so interesting is that it does not break the five-layer thesis. It extends it.

The original cake was about owning the ingredients, the kitchen, and the distribution chain. Cursor adds the person standing at the stove.

Table of Content:

• The Sixth Layer: The Agency IDE

• Was This About New Love for Vibe Coding?

• From Full Stack to Agent Stack

• Governance, Pentagon, and Agents With Root

• The Squeeze Stack, Updated

• What This Means for Your Startup

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The Sixth Layer: The Agency IDE

Cursor is not just another AI coding assistant. Cursor describes itself as an AI coding agent, and both product positioning and outside analysis increasingly frame it as a system that can understand a codebase, plan changes, execute tasks, and behave more like an operator than a passive autocomplete tool.c That distinction matters because once the tool moves from suggestion to execution, it stops being a convenience feature and starts becoming a strategic interface.

and what is The Agency IDE ?

the environment where humans, models, and tools co-create software and, by extension, the systems that run companies, infrastructure, and economies.

In old language, this is the IDE. In 2026 language, it is closer to an operating system for agents. It is the place where:

• Global context lives - repo history, infra scripts, tests, configs, habits.

• Natural language intent gets translated into real software work.

• Agents can plan, edit, verify, and increasingly ship multi-step changes.

That is a much bigger deal than most commentary suggests.

For the past two years, most of the AI debate has focused on the model layer:

who has the smartest frontier model,

who is ahead on benchmarks,

who can train at scale.

But once agents become competent enough to actually perform economically valuable work, the real chokepoint is no longer just the model. It is the environment where the model gets context, receives instructions, and takes action.

This is why Cursor belongs in the same sentence as xAI and SpaceX. If Musk is serious about building an AI-infrastructure empire, then owning the agency layer is obvious. You do not just want the smartest model in the world. You want the surface where that model becomes work product.

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Was This About New Love for Vibe Coding?

Vibe coding is a useful meme because it captures a real behavioral shift. Instead of writing every line yourself, you describe what you want, review what comes back, tighten the constraints, and let the system do more of the implementation. Cursor became one of the clearest product expressions of that shift because it moved beyond autocomplete into a more agentic workflow where users can delegate real tasks instead of merely accepting suggestions.

So yes, the headline slash campaign works. One day Musk is vibe coding. Next day he buys Cursor. It sounds impulsive, intuitive, almost funny. But as with most Musk narratives, the public meme is usually the last visible layer of a much older strategic pattern.

Look at the sequence. SpaceX and xAI were already merged months earlier into a corporate structure that combined compute ambition, AI models, physical infrastructure, and broad distribution. Cursor had already been shifting toward agentic execution. Reuters then reported that SpaceX secured an option structure that let it either acquire Cursor for $60 billion later in the year or pay $10 billion for the partnership if it chose not to proceed. That is not the move of someone reacting to an internet trend. That is the move of someone identifying and securing a strategic control point.

This is the crucial distinction founders should pay attention to.

Vibe coding is the consumer-facing story.

Cursor is the infrastructure move.

The meme says coding is getting easier. The deal says control of the software production environment is now strategically important enough to justify tens of billions in stock.

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From Full Stack to Agent Stack

The most useful way to update my 5 months old xAI thesis is that, the full stack was the first act; and the agent stack is the second.

That revised stack now looks something like this:

• Hardware and orbits - rockets, satellites, vehicles, robotics, and potentially orbital or tightly integrated compute infrastructure

• Compute and models - xAI, Grok, large-scale clusters, and the systems needed to train and serve them

• Agent environment - Cursor as the interface where software work is delegated, inspected, and iterated.

• Applications and distribution - the businesses, channels, and contracts that convert technical capability into economic

The reason this matters is simple.

The best model in the world still needs a place to work.

It needs context. It needs permission boundaries. It needs an interface where humans express objectives and where systems can translate those objectives into code, workflows, and outcomes. That is what the agency IDE becomes.

Seen this way, Cursor is not exactly “a dev-tool acquisition”. Its actually a move to own the cognitive edge of the stack. The point is not just to generate better code. The point is to control the environment in which economically meaningful software decisions get made.

That is also why this should matter to founders outside pure software.

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Governance, Pentagon, and Agents With Root

The first xAI deep dive highlighted an uncomfortable tension. On one hand, xAI and Grok were associated with a looser attitude toward guardrails and public controversy. On the other, the broader Musk ecosystem was increasingly tied to government-facing infrastructure, strategic communications, and defence-adjacent relevance. That tension already mattered when the conversation was about models and media. It matters even more when the conversation shifts to coding agents.

An AI coding agent with deep repo access is not just a writing aid. It can touch infrastructure-as-code, deployment scripts, authentication logic, testing pipelines, and system configuration.

In a startup building an internal analytics app, that may be mostly a productivity question.

In aerospace, rail, energy, telecoms, or government systems, it becomes a governance question very quickly.

This is where a lot of founder commentary still feels behind the curve. Teams talk about agentic coding mainly in terms of speed:

more features,

fewer engineers,

faster sprints.

Those benefits are real. But once an agent sits close to the operational surface, speed is only half the story. The other half is trust architecture.

What approvals are required?

What can the agent touch directly?

How are changes traced, rolled back, or quarantined?

How do you separate low-risk automation from high-consequence systems? T

all these are not boring compliance details. They are the design constraints that will decide which agent-native companies can sell into serious markets and which remain toys for low-risk workflows.

So when SpaceX moves deeper into the coding-agent layer, it does not just signal ambition. It also raises the stakes on governance too.

The same ecosystem that wants to own the stack now has stronger incentives to own the operational interface to that stack. For founders, the implication is clear:

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The Squeeze Stack, Updated

The original “squeeze the middle” thesis feels much more stronger now than it did when it was first written. The market is increasingly sorting itself into two poles.

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Every AI startup hits the same wall eventually. The product is working, users are growing, and then the infrastructure bill arrives and nothing makes sense anymore. The question is not which model to use or which framework to build on. The question is

where your AI actually lives,

who owns it, and

what happens to your margins as you scale.

There are three positions available to you. You can build on hosted inference APIs, paying OpenAI or Anthropic or one of the cheaper alternatives per token. You can rent GPU compute by the hour from neocloud providers like Lambda, CoreWeave, or Crusoe. Or you can buy hardware and operate it yourself. Build, rent, buy. Three positions, very different economics at different scales.

Most founders treat this as a one-time decision.

It is not.

It is a continuous optimisation problem.

and, the right answer changes as your traffic grows, as GPU prices shift, and as your model strategy matures.

H100 cloud rental rates have fallen from eight dollars an hour in early 2023 to around one eighty to three fifty per hour in mid-2026. API prices have fallen too, but unevenly - there is now a 640-times gap between the cheapest viable LLM API and the most expensive frontier option. That spread is enormous, and most founders are not actively managing it.

The decision is driven by three variables:

your utilization rate,

your workload predictability, and

your engineering capacity.

High sustained utilization, predictable traffic, and a team that can operate infrastructure - when all three are true, owning your compute makes economic sense. When any one is missing, you want flexibility.

Here is the number that changes how you think about this.

That means your infrastructure choice is being made primarily for production workloads, not for training runs. And for regulated sectors like aerospace, transport, healthcare, financial services - where your data goes is not a preference. It is a legal requirement.

This series runs eight episodes. We cover inference API economics, GPU rental markets, the on-premises case, open source versus proprietary models, AI FinOps, sovereign AI and compliance, edge inference, and the Nvidia compute wars story.

Listen to the full episode here, in Substack app, or Apple, Spotify / youtube.

I’ll add a companion cost calculator and spreadsheet at intelligentfounder.ai soon.

Last year, around this time, Mary Meeker released a 300-pages AI report and everyone in my circle was talking about it. That reaction was not just about her name. It was about what her work has represented in earlier technology cycles - a serious attempt to turn a noisy market into a readable map. so it was obvious that the market paid attention straight away. I also noted then that she does not publish these heavyweight trend decks every year, and when she returns with one, people expect more than commentary. They expect a frame for what is actually happening.

I did a youtube video on it then which covered the findings in detail.

Looking back at it, that timing was quite interesting. By mid-2025, AI was no longer just a product story or a lab story. It had become an infrastructure story, a capital allocation story, a work story, and increasingly a geopolitical story.

The market did not need another stream of launches. It needed a synthesis. and Meeker’s report gave that synthesis.

What it did not fully answer however, at least from my impression of it, was the founder question.

It showed scale. It showed speed. It showed momentum. But it did not really tell founders where value was settling, what was already becoming a commodity, or how to think clearly in a market where every week now produces fresh claims about breakthroughs, agents, competition, and disruption.

A year later now, the broad thesis still looks strong. Adoption has kept rising. Enterprise usage has broadened. AI budgets remain elevated. The newer data suggests Meeker was broadly right on the direction of travel and, in some areas, conservative on the pace. But that is exactly why this essay is worth writing now. The useful question is no longer whether her report was “right” in the abstract.

The better question is whether it still helps founders make sense of what is happening now, and where a founder needs a sharper lens than a macro deck can provide.

In this deep dive or long essay I am taking Meeker’s report as a starting point, and not end point. and looking at

why the report landed the way it did, what it actually captured, what it left open, whether the thesis has held up after a year, where the market has moved faster than expected, where it is still behind, and what founders should watch next.

Note - for those looking for Anthropic playbook part 3 - with so much happening, I thought i’d push it a bit further down the line, and closer to IPO time and have written a short take on Fable on AIUnfiltered here.

Agents, Meters and Bans!

Table of Contents

1. Why Meeker’s report landed the way it did

2. What the report actually captured

3. What it left open for founders

4. Has the thesis held up after a year?

5. Where the market moved faster than expected

6. Where the market is still behind

7. What founders should take from it now

8. What to watch by next year

1. Why Meeker’s report landed the way it did

Mary Meeker’s report arrived with unusual weight because it felt like the return of a format the technology world still trusts:

the big trend deck that tries to explain the cycle, not just the week.

Her earlier Internet Trends reports mattered because they gave people a language for understanding transitions that were already underway but not yet widely organized in public thinking. One of the other and main reason the 2025 AI report landed in a similar way was because

That is what made the report highly anticipated. By the time it came out, the public conversation around AI had become fragmented. One part of the market was fixated on model releases. Another was focused on GPU supply and cloud capex. Another was watching regulation. Another was already talking about agents and workflow automation. These were all parts of the same system, but they were rarely described together. Meeker’s report tried to put them on one page, or rather 300-plus pages.

Her own framing makes that clear. In the report’s context section, she writes that a collection of datapoints became “this beast,” and that the charts kept needing to be updated because the moving pieces were all affecting one another. That is a useful line to hold onto because it captures the real shape of the market.

AI is not moving as one clean curve. It is moving as a set of connected curves: users, compute, capital, products, competition, and geopolitics.

That why I actually care about it and so does the market imo. The market did not need another bullish report. It wanted orientation. It wanted to know whether AI should be read as a normal software wave, an infrastructure reset, a bubble, a productivity revolution, or something larger.

Meeker’s answer was that it looked larger. and I agree.

ref: page 3, “Context.”
ref: pages 8–9, “Overview.”

( I can’t add too many images because of sizing issue, so am adding the reference here in each section if you want to look deeper, the current maps I have created however should be enough to follow through otherwise. )

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2. What the report actually captured

One of the strengths of the report is that it was broader than a lot of the commentary around it. It was not only about frontier models or consumer adoption. The outline itself shows the full range, for example change happening faster than ever, AI user and usage growth, capex growth, model compute costs, falling inference costs, rising competition, China’s rise, the physical world, global diffusion, and work evolution.

That matters because it means the report was not simply arguing that ChatGPT got big quickly. It was making a larger case that AI had become a compounding system built on internet distribution, accumulated data, model scaling, developer ecosystems, cloud infrastructure, and aggressive capital deployment. The best summary of that appears in the “Charts Paint Thousands of Words” pages, where the report compresses its core argument into a small number of flagship visuals.

A few charts carry most of the narrative load. The leading US-based LLM user chart on page 55 is central because it captures the speed of user growth. The comparison of global adoption outside North America on page 56 matters because it shows that AI did not diffuse the way earlier internet platforms did; it spread globally almost from the start. The Big Six US technology company capex chart on page 97 matters because it makes clear that this is not only an app story but also a capital buildout story. And the inference cost chart on page 138 matters because it shows why usage can keep rising even while training and infrastructure remain expensive.

The report also deserves credit for reaching beyond software. Its physical-world section, while short, points toward automation and autonomy moving into real operating environments, with the San Francisco autonomous taxi market share chart on page 302 acting as a visible proxy for that shift. Its work section does something similar, especially with the AI versus non-AI job postings chart on page 332, which helps show that AI was already changing labour demand, not just future speculation.

If the question is what Meeker captured well, the answer is this:

she captured that AI had become a multi-layer market all at once. Not just a model market. Not just a startup market. A distribution, infrastructure, labour, and geopolitical market too.

ref: page 4 or page 10, “Outline.”
pages 5–7, “Charts Paint Thousands of Words.”
page 55, leading LLM weekly active users.
page 56, global usage outside North America.

page 97, Big Six capex.
page 138, inference cost decline.
page 302, autonomous taxi share.
page 332, AI vs non-AI IT jobs.

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3. What it left open for founders

This is where the founder angle starts to matter. Meeker’s report is strong on scale and speed, but lighter on position.

and I am not criticizing it but rather creating a boundary. It is a macro deck. It is trying to explain the cycle. But founders still need a second layer of interpretation.

The first missing piece is commoditization.

A chart can show that adoption is rising, but it cannot by itself tell a founder whether that adoption benefits new entrants, incumbents, infrastructure providers, or workflow owners. In a market like this, growth does not automatically mean opportunity for everyone. Often it means category crowding.

The second missing piece is stack position.

Public AI discussion often blurs together foundation models, tools, APIs, copilots, agents, vertical applications, and embedded operational systems. Founders cannot afford to blur them. They need to know where they sit in the stack and how value may move between layers. Some parts of the stack are scaling fast but becoming interchangeable. Others are harder to build, harder to sell, and slower to scale, but potentially more defensible.

The third missing piece is operational reality.

Enterprise adoption numbers are useful, but they do not tell a founder how hard it is to integrate AI into an existing workflow, win trust, prove ROI, manage governance, or avoid buying a dependency that later gets flattened by the platform layer.

That is why a founder reading of the report is useful. The macro map says AI is real, large, fast, and capital intensive. The founder version adds a sharper question:

where in this market can a company still build durable advantage, and where is the apparent opportunity already collapsing into infrastructure, platform leverage, or commodity software?

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4. Has the thesis held up after a year?

Broadly, yes. specially the central Meeker thesis.

The market has continued to validate the core claims around speed, adoption, spending, and integration. Stanford’s 2026 AI Index shows that generative AI reached around 53% global population adoption within three years, and private AI investment in the United States reached about 286 billion dollars in 2025. Those figures do not suggest a narrative reversal. They also suggest the opposite that the system Meeker described kept advancing.

Enterprise data points in the same direction.

NVIDIA’s 2026 State of AI materials indicate that around 64% of organizations are actively using AI in operations and that 86% expect AI budgets to rise in 2026. That matters because it shows AI moving further from experimentation into embedded operational use. In other words, the market did not just keep talking about AI. It kept buying it, deploying it, and budgeting for it.

On capability, the broader trend has also held. Stanford reports continued gains in benchmark performance, including frontier systems matching or exceeding human baselines on difficult tasks and coding benchmarks improving dramatically over a short period. This has reinforced one of Meeker’s core arguments

So if the question is whether the report held ground, the answer is yes. But the more useful answer is that it held ground because the forces behind it were structural. AI was not riding only on hype. It was riding on real infrastructure buildout, falling inference costs, expanding deployment, and a market-wide effort to reorganize products and workflows around machine intelligence.

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5. Where the market moved faster than expected

If the broad thesis held, some parts of the market moved even faster than many people expected.

One of the clearest examples is the shift from chatbot framing to agentic framing.

That matters for founders because it changes which product categories feel early and which already feel crowded. It is one thing to build on top of a chat interface. It is another to build around task execution, orchestration, and integration into real operational environments. The latter is harder. It is also where a lot of fresh value may move.

A second area that moved quickly is organizational normalization.

Read more

These themes are everywhere be it press releases, consulting reports, or analyst blog, but they’re stuck at the surface level. But the real story isn’t the themes themselves. It’s the flywheel that emerges when sovereign platforms and open models start reinforcing each other.

Right now, coverage treats sovereign AI as a compliance play and Open Telco AI as a collaboration portal. But neither angle explains the compounding advantage. for example,

how a sovereign stack becomes the default infrastructure that startups build on, or

how shared models get smarter because more operators contribute data.

The numbers backing these trends are undeniable. 89% of telcos will boost AI spending in 2026, and 90% say open-source models are critical, yet the narrative still talks about “ecosystems” in generic strategy language instead of concrete network effects.

Telco AI isn’t about choosing between sovereign or open.

For founders, that changes the go-to-market math. For telcos, that’s the difference between becoming a data conduit and owning the compounding platform layer and thats exactly whats missing right now and what we are discussing in this deep dive.

There is a short high-level essay of this deep-dive on linkedin at the network effect. you can read it here. -

The Platform Flywheel

Table of Content -

1. Real story is the flywheel, not the themes.

2. Open Telco AI is the engine that makes the flywheel spin

3. The flywheel in action for founders

4. Sovereign and open reinforce each other

5. Extending the fabric: quantum and trust

6. Risks, failure modes, and what to watch

7. What to watch in 2026–2028

8. What an intelligent founder should do - the founder playbook

9. The shift: from isolated pilots to compounding platforms

1. The real story is the flywheel, not the themes

The real opportunity in telco AI is not any single theme on its own. It comes from the network effect that appears when sovereign platforms and open models begin reinforcing each other.

If we think of a sovereign telco AI stack as a national AI operating system for networks. It combines shared infrastructure such as data centres and GPUs, shared data such as telemetry and fraud signals, and shared tools tuned for telecom use cases. As more operators join, the stack gains more data, more deployment feedback, and more practical use cases. That makes the shared models better, which makes the platform more attractive to the next operator and to software vendors that want to integrate once and sell to many carriers.

That is the flywheel:

each new participant increases the value of the platform for everyone already inside it. Over time, the cost of building a separate stack becomes harder to justify, and the shared platform starts to become the default.
You can already see the shape of this in emerging telco AI initiatives.

When operators and vendors work on common telecom models instead of isolated in-house systems, every additional contribution, whether data, benchmarks, or deployment feedback, improves the shared base for the next participant. That is what turns sovereign infrastructure from a compliance story into a compounding platform advantage.

For founders, this matters because it changes the go-to-market logic. Instead of building for one operator at a time, startups can build for the stack, reach multiple operators through a common platform, and benefit from models that improve as the ecosystem grows.

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2. Open Telco AI is the engine that makes the flywheel spin

The GSMA’s Open Telco AI initiative is more than a collaboration portal. It’s a shared, open platform where the industry can co-create the foundations of telco‑grade AI. It brings together operators, vendors, and researchers to build new models, datasets, and tools specifically for telecom use cases.

The loop works like this:

1. A core open-source model for detecting network anomalies is released.

2. Multiple operators try it, contribute fixes and better training data.

3. The shared model improves faster than any private, in-house version would.

4. More operators adopt it, and more vendors integrate with it.

5. The community grows, which leads to more contributions, and the cycle continues.

Nine out of ten telecom professionals say open-source models are important. That’s not just about saving license fees, it’s about transparency, adaptability, and the ability to fine-tune models for specific network problems without waiting for a vendor.

Real-world anchor?

Open Telco AI launched at MWC 2026 with founding partners including AT&T, which is contributing a family of open telco models trained on public network data, and AMD, which is providing compute capacity and GPU platforms. Other operators like Deutsche Telekom, Orange, SK Telecom, Telia, and many more have joined as part of the expanded consortium.

3. The flywheel in action for founders

Let’s make this concrete for a startup that needs to decide where to build.

Imagine a European sovereign AI stack where five operators agree on a common way to expose network telemetry and anomaly alerts. On top of that, Open Telco AI hosts an open anomaly‑detection model that those operators have already tuned with their own data. A startup decides to build a fraud and revenue‑assurance product on top of that stack.

On day one, the startup plugs into the shared model and interface, instead of building five separate integrations into five different operator data lakes. The model is already “good enough” because multiple carriers have contributed data, labels, and fixes; every new deployment makes it better for the next one. The sovereign stack provides a predictable path to production, and the open model library gives them a constantly improving brain for free.

Now the flywheel shows up in the go‑to‑market math. Instead of a 12–18 month sales and integration cycle per operator, the startup designs for the stack once and reaches multiple operators with the same core product. Each operator that adopts the product contributes new edge cases and performance data back into the shared model, which makes the product more accurate and the shared stack more attractive to the next operator.

For a founder, the question shifts from “Which single operator can I convince first?” to “Which sovereign stack and open model ecosystem gives me the broadest, fastest route to several operators at once?”.

so you are not just selling into a network, you are building with a network that compounds on your behalf. That is the practical meaning of this flywheel.

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4. Sovereign and open reinforce each other

The most powerful story is that sovereign AI and open-source AI are not competing ideas. They make each other stronger.

Telenor AI Factory is a good example.

Telenor AI Factory is Norway’s first sovereign and secure AI cloud service. It’s designed to support the development and adoption of AI in Norway, with data stored and processed entirely within the country to comply with EU/EEA rules.

The infrastructure runs on NVIDIA-powered systems and Red Hat’s cloud-native stack, explicitly framed as sovereign AI cloud. That’s not marketing, it’s a real, operational sovereign AI platform that telcos can host on, and that startups can build against.

Sovereign AI demands control -

local infrastructure,

clear rules, and

strong oversight.

Open-source AI provides building blocks that can be inspected, adapted, and certified for those local contexts.

Together, they allow a country to say: “We will run AI on our own networks, using models we understand, on infrastructure we regulate.”

5. Extending the fabric: quantum and trust

There’s another layer here that’s worth noticing. the telco infrastructure is becoming not just AI-powered, but also quantum-secured.

In January 2026, T‑Labs (Deutsche Telekom) and Qunnect achieved quantum teleportation over 30 km of commercial fibre in Berlin, alongside normal traffic, with ~90–95% fidelity. The trial was described as a major milestone for deployable quantum tech on existing telco infrastructure.

This isn’t about replacing the network with quantum. It’s about adding a trusted, quantum-secured layer on top of the existing telecom fabric. The more quantum-secured endpoints exist, the harder it becomes for competitors to offer the same level of trust.

And that’s the compounding advantage.

Note - I’ll cover the quantum in more detail in another post here or at Quantopinion.ai.

Read the latest here.

Who's adopting Quantum

6. Risks, failure modes, and what to watch

But all this doesn’t all roll out smoothly. There are real risks and failure modes.

Risk 1: Fragmentation of sovereign stacks

If every country or operator builds its own “sovereign” AI platform that doesn’t interoperate, the network effect gets diluted. Instead of a few large, shared stacks, you end up with many small, fragmented ones. The flywheel slows down.

Risk 2: Governance and incentives

Operators may treat open initiatives like Open Telco AI as marketing rather than actually contributing data and models. If contributors are shallow, the shared models don’t improve as fast, and the flywheel weakens.

Read more

In some cases, the system was meant to optimize output. In others, it was there to monitor the floor, flag risks, or tighten scheduling. But when sensors misread conditions, or the system failed to notice that a process had changed, the result was the same: near‑misses, unsafe decisions, and sometimes real injuries.
On paper, these were “AI productivity tools.”

On the ground, they were software systems making confident decisions with messy data on top of aging equipment and overstretched teams. That matters for more than workplace safety. It matters for the economy too. Because once AI starts showing up in productivity forecasts, labour models, and growth expectations, these “local” failures stop being local. They become part of the real story of how AI affects wages, output, and risk.

This piece looks at how AI shows up in real workplaces and in the big‑picture numbers economists use to talk about growth and GDP.

We are trying to figure out what gets missed when we talk about “AI adoption” but ignore the state of the sensors, infrastructure, and safety practices underneath. This one is aimed at anyone building, buying, or regulating AI systems in the real economy, especially founders working on AI for industry and infrastructure.

What’s inside

• How AI ended up on the economic dashboard

• What today’s AI stats and indices actually measure

• The blind spot: messy data, old infrastructure, and safety incidents

• Why that blind spot changes the AI‑and‑GDP story

• A small, testable claim about AI, sensing, and real productivity

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1. How AI crept into the economic dashboard

Over the last couple of years, AI has moved into parts of the conversation where it simply was not before. Economists, investors, and policymakers now treat it as something that can change the path of the wider economy, not just the fortunes of a few tech firms.

That shift is visible in several places. Researchers have built an AI uncertainty index by scanning news coverage and tracking how AI‑related uncertainty feeds into markets and labour conditions. Stanford’s 2026 AI Index includes a full economy chapter on investment, productivity, and labour exposure. The IMF now describes AI as a macro‑critical transition, something with implications for growth, inequality, and financial stability.

We can confidently say that AI is no longer just a technology story now. It is now part of the macro story.

It sits alongside interest rates, energy prices, and global demand in the way people explain what might drive the next few years.

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2. What the new AI numbers actually measure

To make AI visible at that level, different groups track different signals.

Some track spending:

how much money is flowing into data centres, chips, models, and related infrastructure, and

how that compares with earlier technology booms as a share of GDP.

Some track productivity:

whether AI tools help workers complete defined tasks faster or better. In customer support, coding, and similar settings, some studies now report gains in the rough range of 15–25%.

Some track labour effects:

who is helped, who is squeezed, and how firms reorganize work.

Early evidence suggests some junior roles are hit sooner, while more senior roles are more protected for now.

And some track uncertainty itself.

The AI uncertainty work suggests that spikes in AI‑related uncertainty are linked to weaker equity prices, lower wages, and fewer hours worked, even when headline employment does not move much.

From a distance, this is all sensible. If AI is a large shift, then spending, productivity, labour, and risk are exactly the right places to look.
But this is also where the blind spot starts.

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3. The blind spot: sensors, rust, and real‑world safety

Most of these measures treat AI as if it were one clean category.

A firm either “uses AI” or it does not.

A sector is either “AI‑exposed” or not.

That works reasonably well if the system lives mostly in software. It works far less well when the system is wrapped around physical operations.

In a factory, a rail network, a substation, or a water system, an AI system is not just a model. It is also:

1. Sensors attached to real equipment.

2. Networks carrying those signals.

3. Old control systems and patchy documentation.

4. Human teams balancing safety, maintenance, and output.

That physical layer changes everything.

A model can be excellent and still fail in practice because a sensor drifts, a camera is badly placed, a machine has been modified, or a maintenance team no longer trusts the alert stream.

This is where the safety stories connect to the economic ones.

If the data going in is weak, the decision coming out can be weak too. And if that decision touches physical systems, the cost is not just a wrong answer on a screen. It can be downtime, scrap, accidents, insurance claims, legal exposure, and lost trust inside an organisation.

None of that is well captured by a top‑level AI spending chart or a broad “AI exposure” metric. Yet it strongly shapes whether AI investment turns into real productivity gains or just more operational fragility.

4. Why that blind spot matters for AI and the economy

This is not a minor detail. It goes to the heart of how the AI boom is being interpreted.

Two firms can both appear in the same macro story. Both are in AI‑exposed sectors. Both are investing in AI systems. Both are counted as part of the same productivity wave.

But on the ground, they may be very different.

One firm may have strong instrumentation, decent data quality, clear override procedures, and a safety culture that questions bad recommendations. Another may be running similar software on top of poor sensing, legacy assets, and weak operational visibility.

The first firm may see real gains.

The second may see false alarms, missed hazards, stalled rollouts, and a long list of “promising” pilots that never survive contact with the plant.

At the macro level, both still look like AI adopters.

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HANNOVER MESSE, Germany started in 1947 as a British‑backed export fair to jump‑start West Germany’s post‑war industrial recovery, and has since grown into one of the world’s leading industrial technology shows. The show typically attracts on the order of 100,000–200,000 visitors annually and remains a key stage for industrial policy announcements and partner‑country showcases.

Today, HANNOVER MESSE markets itself as a global platform for industrial transformation, bringing together automation, energy systems, digitalization, and related technologies under one roof, which makes it a natural place to see how ideas about industrial AI and automation turn into concrete architectures and products.

When I was last at Hannover Messe in 2023, the show felt bigger, more spread out and noticeably more crowded, with hydrogen and energy transition everywhere you turned. More than 500 companies were showcasing hydrogen technologies, turning the “Hydrogen + Fuel Cells Europe” area and neighboring energy halls into a dense sprawl of future‑energy infrastructure pitches layered on top of the usual mix of automation, robotics and digitalization. I was there 3 or 4 days and I still couldn’t cover it all. If i recall correctly, I spent almost a day (5-6 hours) all in startups halls 16-17 I think and one day dedicated to IoT sensors etc. AI & Automation was folded in industrial clouds narrative, this was also my first time trying to distinguish and differentiate network automation and industrial automation. I haven’ come across the latter before and have always been focused on Network Automation only. to be fair, industrial automation people don’t know what network automation is either.

This year, I could just be there for a day, and what struck me most was not that AI was everywhere, but that it no longer needed to shout about itself. I was at MWC 2024 exhibiting, and since slowed down on my conference visits and time, so it was likely gradual. but still, as AI felt less like a hype layer and more like part of the operating logic of the systems on show, across all for example automation, energy, industrial software, and connectivity, which are what messe’s focal points constantly are It was interesting to note.

By contrast, Hannover Messe 2026 also felt more condensed and legible, still large, but reorganized into fewer, clearer pillars i.e

• Automation & Digitalization,

• Energy & Industrial Infrastructure, and

• Research & Technology Transfer, with industrial AI threaded through all of them.

Rather than hydrogen dominating the visual field, AI, robotics and connected infrastructure showed up as the common layer across booths and halls, with organizers explicitly positioning “industrial AI” as the competitive game‑changer tying the year’s exhibits together.

Compared with 2023, the fair felt more focused and more walkable. The halls I moved through felt clustered around automation, energy, robotics, industrial software, and connected infrastructure rather than a scatter of disconnected showcases, and that made the whole event feel less like a general technology spectacle and more like a working blueprint for the next generation of industrial systems.

That shift matters.

At Hannover Messe 2026, the more interesting story was that industrial AI is increasingly being presented as infrastructure:

embedded into control loops, quality systems, maintenance workflows, energy optimisation, engineering software, and industrial platforms.

This article is not a full technical unpacking of all of that. I want to use this piece as an anchor:

• first, to capture what I actually saw and felt on the ground, and

• second, to place that against what others are saying publicly,

• third, to say where I agree, where I think the current commentary is too smooth, and what I think is actually happening underneath and finally,

• to draw out what this means for founders and builders who want to work in industrial AI rather than just talk about it, which this blog is all about.

I am also dedicating a full deep dive on industrial AI because it feels like the missing chapter in today’s AI coverage:

everyone sees the chatbots, almost no‑one sees the schedulers, maintenance systems and control loops quietly being rebuilt with AI. Industrial AI is moving from hype to day‑to‑day infrastructure, but that story is only just starting to be told, and there are far fewer deep dives on industrial AI systems than on the latest foundation models or product launches. Underneath those systems sit power, production, and connectivity networks that are already treated as critical infrastructure, which means AI is being woven directly into the machinery that keeps factories, grids, and campuses running. That intersection between AI and critical infrastructure is the layer my own work focuses on, and it is where the technical and strategic questions get sharpest fastest.

Table of Content

1. Why Hannover Messe, why now

2. From hydrogen hype (2023) to AI as infrastructure (2026)

3. What stood out on the ground

4. What others are saying about industrial AI

5. Where I agree – and where the story is too smooth

6. Four themes that actually matter ( I’ll cover these in details deep dive for each )

7. What founders and builders should take from this

8. Where industrial AI is really going next

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3. What stood out on the ground>?

The first thing I noticed was a change in tone. AI was certainly present, but it was not plastered everywhere as a generic slogan. Instead, it was usually attached to a concrete function:

That sounds like a small change in messaging, but it reflects a big change in maturity.

In previous cycles, much of the discussion around AI in industry had the feel of a technology being added on top of industrial systems. At HM26, a lot of the exhibits suggested something else.

AI being absorbed into the stack itself.

Microsoft’s framing around “industrial intelligence” is a good example of that, where data, AI agents, simulation, and digital operations are presented as a layer spanning design, manufacturing, and service rather than as a single application.

AWS used similar language, but from a slightly different angle, talking about physical AI, agentic AI, edge-to-cloud connectivity, and intelligent data foundations as part of a scalable industrial architecture.

NVIDIA’s industrial AI messaging also reinforced that direction, especially where robotics, digital twins, simulation, and cloud infrastructure were presented as one continuous environment rather than separate domains.

That lined up with the feel of the fair itself. Many demos were no longer about “look, here is an AI model.” They were about systems.

A robot cell, a monitoring environment, a digital twin pipeline, an optimisation platform, a connectivity backbone. AI was often there, but as one working component within a broader architecture.

I also found that the fair felt much more coherent around industrial concerns that actually matter over the next decade: automation, energy resilience, software-defined operations, connected infrastructure, industrial data, and workforce support. That coherence matters because it suggests that industrial AI is no longer being positioned as an optional experiment.

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4. What others are saying?

If you look at the public commentary around Hannover Messe 2026, there is a fairly consistent consensus emerging.

The official Hannover Messe line is that industrial AI is now a competitive game-changer and a major force for transformation, especially when combined with automation, software, and energy systems. That is not surprising coming from the organizers, but it is still useful context because it tells you what the fair itself wanted to foreground.

Consultancy and ecosystem commentary broadly reinforces that message.

That phrase captures a lot of what the fair felt like in practice.

The hyperscaler and major platform narratives are strikingly aligned.

Microsoft talked about unlocking industrial intelligence through a unified layer of data, AI, simulation, and operational context across the product lifecycle.

AWS positioned its presence around scaling industrial AI through physical AI, agents, and robust edge‑to‑cloud data architectures.

NVIDIA emphasized industrial AI cloud infrastructure, digital twins, and robotics as a foundation for future manufacturing systems.

SAP focused on embedded AI agents in supply chain and manufacturing workflows, again suggesting that AI is moving inside the workflow rather than sitting beside it.

Taken together, these stories all point to the same direction of travel:

industrial AI as a fabric that spans design, operations, and service, rather than a point solution in one corner of the plant. What they mostly gloss over is how hard it is to make that fabric real inside messy brownfield environments, with real constraints on data quality, control architectures, and who actually owns the resulting “industrial brain.”

Independent and community commentary tends to be less polished, but it points in the same direction. Some review-style posts and journals describe Hannover Messe 2026 as a place where AI, automation, and connectivity were visibly coming together in practical industrial settings rather than being discussed in the abstract. Other voices say more bluntly that “AI is everywhere,” but also note that the real challenge is no longer novelty; it is integration into processes, organisations, and operating models.

Broadly, I agree with that consensus. If the question is whether HM26 felt more mature, more practical, and more embedded than previous years, my answer is yes. If the question is whether industrial AI now looks less like a collection of demos and more like a set of emerging industrial defaults, again, yes.

But I also think a lot of the commentary is still too frictionless.

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5. Where I agree, and where I think the story is too smooth

If 2023 was about talking up industrial AI, 2026 was about AI being hard‑wired itself into industrial systems, data flows and day‑to‑day decisions.

That is where I strongly agree with what many vendors and reviewers are saying. The fair did not feel dominated by speculative promises. It felt dominated by architectures, integration stories, and system-level positioning.

Where I part company with some of the more upbeat coverage is that integration is often treated as if it were the end of the story.

Take closed-loop AI.

It is one thing to show machine data flowing into an AI service and then back into a recommended action. It is another thing to explain how that loop is governed, what happens when the model drifts, how human overrides work, how latency is managed, where decisions are executed, and what evidence exists that the loop is safe enough to trust in a real industrial environment. A lot of public material points toward these systems, but much less of it explains the engineering and governance burden that comes with them.

The same applies to connectivity.

Yes, the 5G and Industrial Wireless Arena and related ecosystem messaging suggest that industrial 5G is maturing and moving toward scalable deployment. Yes, wireless connectivity increasingly feels like part of the assumed backbone for mobile robots, distributed sensing, video, and flexible industrial layouts. BUT, commentary often jumps too quickly from “5G exists” to “real-time intelligent factories are now straightforward,” and that skips over a lot of messy questions about deterministic performance, RF conditions, coexistence with existing OT networks, safety boundaries, and operational ownership.

Then there is the platform and ecosystem story. I agree very strongly that industrial AI is increasingly delivered through ecosystems rather than standalone products. In fact, one of the clearest signals from HM26 was that the market is organizing around combinations of cloud providers, automation vendors, industrial software players, data platforms, integrators, and application specialists.BUT that is not just a story about interoperability and innovation. It is also a story about power, meaning -

who owns the data layer,

who controls the interfaces,

who gets embedded into operations,

who becomes difficult to displace, and

who captures the value.

This is one place where I think founders and technical teams need to pay close attention. A lot of public material celebrates ecosystems as if they are automatically good for everyone. Sometimes they are. But ecosystems also create dependencies, default choices, and new forms of lock-in. The interesting question is not just whether your product can plug into a larger stack. It is whether you are becoming a durable part of that stack or a thin feature inside somebody else’s platform.

Finally, there is the “next wave” layer:

I also think quantum deserves serious attention in industrial contexts like logistics, optimisation, sensing, and energy systems.

What I do not think is helpful is collapsing all of these into one smooth maturity story. Digital twins integrated with operational data are much closer to mainstream deployment than most quantum applications. Simulation-backed optimisation is already part of today’s industrial AI stack in some settings. Quantum is much earlier and should be discussed with more care around time horizons, fit, and readiness.

so what I feel is that the consensus around HM26 is directionally right, but a bit too polished.

Industrial AI is maturing.

It is moving into infrastructure.

It is becoming systemic.

But the real action is now shifting into the layers that are less visible in polished demos:

control architecture,

connectivity engineering,

data platform design,

operational governance, and

realistic pathways for emerging technologies.

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6. The four themes I think matter most

I will go deeper on these in later pieces, but even in a review article they are worth naming briefly because they explain where industrial AI is actually heading.

1. The first is closed-loop industrial AI. The important shift is from systems that observe and recommend to systems that increasingly influence or trigger action. That does not mean fully autonomous factories are around the corner. It does mean that the centre of gravity is moving from analytics toward operational decision loops, and that shift will force much deeper attention to control boundaries, safety cases, and responsibility.

2. The second is industrial connectivity as core infrastructure.

   Private 5G, industrial wireless, edge compute, and mixed wired-wireless architectures are not side topics. They are the enabling layer for mobile robotics, dense sensing, adaptive layouts, and distributed AI workloads. The glamorous AI applications get the headlines, but the network is often the real constraint.

3. The third is ecosystems and industrial data platforms. The market is clearly moving toward shared stacks where industrial data, simulation, AI services, partner applications, and operational systems come together. That creates opportunity, but also strategic questions about interoperability, governance, and who ends up controlling the “industrial brain.”

4. The fourth is simulation-first optimisation, with quantum on the horizon. The stronger signal today is simulation and digital twins as practical optimisation tools. Quantum may become part of that future in selected domains, but the more immediate frontier is a tighter link between real-world data, virtual industrial models, and optimisation workflows that can continuously improve production, logistics, and energy systems.

7. What founders and builders should take from this!

For founders and builders, the main lesson from HM26 is that it is no longer enough to have an “AI product.” The market is moving toward products that can survive inside real industrial architectures.

That means a few things.

Are they monitoring, predicting, optimizing, orchestrating, or acting?

What data do they depend on? What systems do they write back into? What happens when they fail? Those questions are becoming more important than whether the model itself sounds impressive.

If the product depends on high-quality streaming data, mobile assets, low-latency inference, or video-heavy sensing, then network architecture is not somebody else’s problem. It is part of the product strategy.

Industrial AI products increasingly need credible answers on integration with cloud platforms, industrial data models, security frameworks, and existing OT and enterprise environments. A strong technical wedge may still be domain depth or workflow design, but it will have to coexist with larger platform realities.

If industrial AI is really moving from insight to action, then explainability, auditability, rollback, validation, and operational ownership are not “enterprise extras.” They are core design concerns.

And finally, teams should be careful not to confuse forward-looking messaging with near-term value. There is a lot to be excited about in advanced simulation, physical AI, and quantum, but most practical value over the next few years will still come from making industrial systems more observable, more optimizable, more connected, and more reliably governable.

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8. Which direction industrial AI is going

If I had to reduce my entire #HM26 takeaway to one line, it would be- industrial AI is heading away from novelty and toward embedded operational intelligence.

That means less focus on whether a company “has AI” and more focus on whether it can integrate intelligence into the actual mechanics of industrial work.

ie - machines, lines, facilities, engineering processes, energy systems, maintenance routines, supply chains, and digital operating layers.

It also means the real competitive questions are changing. The next phase is less about producing one impressive demo and more about answering harder questions like who controls the data layer, where decisions are made, how safe autonomous behaviour can become, how wireless and edge infrastructure support those decisions, and how software-defined industrial systems evolve over time.

For me, that was the real signal from one day at Hannover Messe, Germany.

Not that AI is suddenly solved.

Not that every industrial company now has a mature AI strategy.

But that the conversation has shifted. Industrial AI is no longer trying to earn legitimacy through spectacle. It is increasingly being presented as part of the baseline architecture of future industrial systems.

That is what makes this moment interesting. Once AI becomes infrastructure, the interesting questions stop being promotional and start becoming architectural, operational, and strategic.

Those are the questions worth following from here.

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Six weeks ago I wrote this below deep dive about DeepSeek V4 when it was about to be released but was held back. we got the specs that were real, it looks like most AI budgets were structurally unprepared for what was coming. Prediction markets were sitting at 88% probability of a March release. But DeepSeek had other ideas.

DeepSeek V4 Is Coming 🚀

Now my whole argument was about how DeepSeek V4 is going to be a real architectural and economic shock to the AI market. how it makes frontier‑level capabilities dramatically cheaper by combining Engram memory, sparse MoE, and 1M‑token context, and that cost collapse will force every serious builder to rethink their stack, pricing, and moat strategy, and I said all this while both Anthropic ( frontier AI) and Nvidia ( open-weight) released 1M-token context models in the same week, pressure or not, we’ll discuss later.

My own strategy and advice to founders? » treat models as commoditizing infrastructure, route everything through an abstraction layer, build your moat around data and workflows, and be ready to evaluate and switch quickly as V4 and its peers reset the competitive floor on AI costs.

DeepSeek V4 finally dropped yesterday morning. its still in preview mode but good enough details available to run an honest scorecard, take the temperature of the room, map the competitive landscape properly, and then go to the angles we discussed before and what I dont’ see most focus on despite of so much chatter on X/twitter. as expected, the spec sheet is the least interesting part of this whole story.

Table of Contents

1. The Scorecard: What I Got Right, What I Got Wrong
   Architecture, pricing, open source, confirmed. Timeline and GA status?

2. The Room: What the Internet Is Actually Saying
   X, Hacker News, Reddit - the human temperature on launch day.

3. The Competition: 1M Context Is Now Table Stakes
   Anthropic, NVIDIA Nemotron 3 Super, Qwen 3.6 Plus, GPT-5.5 - the full landscape.

4. The Chip Story: The Real Reason It Was Six Weeks Late
   Smuggled Blackwells, Huawei Ascend 950PR, Jensen Huang’s warning.

5. The Distillation Controversy: Building on the West’s Shoulders
   Claude and ChatGPT outputs, ToS violations, the legal question nobody has resolved.

6. The Multimodal Blindspot: This Is Not a Text Model
   Native text + image + video from pre-training - what this changes for enterprise workflows.

7. RAG Is Not Dead, But It Should Be Worried
   How a genuine 1M context window flips the default assumption on retrieval.

8. The Benchmark Scepticism: What We Don’t Know Yet
   All V4 numbers are internal only. No independent evals published yet.

9. The UK and European Angle Nobody Is Writing
   ICO guidance, UK GDPR, and why open weights create a new sovereignty option.

10. The Real Strategic Insight: The Race to the Bottom Is Actually a Race to Irrelevance - For the Model Layer
    Commoditization, the value migration to application layer and data, and the question you should actually be asking.

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Section 1 - The Scorecard

SO the technical specs haven’t changed. Engram conditional memory, DSA sparse attention, MoE-at-scale, all are in the final release.

• V4-Pro ships at 1.6T total parameters with 49B active per token.

• V4-Flash at 284B total/13B active.

• The 1M token context window is real and live from day one, not a roadmap item.

The “track Chinese pricing as your absolute cost floor” argument was the right call. Official launch pricing:

$0.03/1M cached input tokens, $0.50/1M output tokens, against Claude Opus at roughly $25/1M output.

And, that’s a structural shift in inference economics. Its no longer projected. its confirmed.

DeepSeek also continued its open-source commitment, so weights are live on HuggingFace, Apache-style licensing, same playbook as V3.

It however arrived 6 weeks late, and this actually matters more than the calendar slip, the reason for the delay is one of the most consequential technical and geopolitical stories in AI right now.

What I got wrong ( not wrong wrong) in my previous deep dive is by omission: the “preview” qualifier. What launched today is explicitly a preview release, not GA. so still holding back? - but the API is live and weights are downloadable, but production SLAs, rate limits, and full regional availability are still rolling out. so If you are making enterprise integration decisions today, treat it as early access, not a stable dependency.

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Section 2 - What the Internet Is Actually Saying

The launch post from DeepSeek’s own Deli Chen is worth reading in full: “484 days later, we humbly share our labor of love. As always, we stay true to long-termism and open source for all. AGI belongs to everyone.”

Couple of statements from twitter -

To reiterate the sentiment in most straightforward way, the launch has reset expectations about how fast the ground can move under working builders, and the implicit conclusion is that “slow cycles” on strategy or shipping are no longer viable.

On the competitive side, commentators are highlighting a deeper asymmetry. for example, a lab operating under export controls has managed to train a frontier‑class model, while better‑funded Western labs have struggled to translate vast GPU budgets into clearly superior state‑of‑the‑art results ( Hold on for the Chip story a minute I am coming to that) . That is being read less as an anecdote and more as evidence that capital and hardware alone are no longer a sufficient moat.

Cost discussions have converged on the same point. V4 is being treated as the new effective price floor for frontier‑grade capabilities, with per‑token economics implying that what looked like a one‑year AI budget on premium closed models now stretches to a multi‑year runway on this stack.

In technical communities, attention is focused on the 1M‑token context window as expected, but whether it delivers usable recall beyond the usual 200K–300K degradation point. If that holds, the consensus is that many retrieval‑augmented generation pipelines become optional rather than mandatory, with a likely shift from “design around RAG” to “justify RAG only where latency, privacy, or corpus size truly demand it.

DeepSeek founder Liang Wenfeng apparently framed the company’s posture simply: “The whole world should use a 1.6T model for free.”

That is either a genuinely held belief or the most effective marketing line in AI this year. Possibly both.

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Section 3 - The Competition

In the six weeks between my March piece and this morning, the entire frontier has moved. so anyone treating the 1M context window as DeepSeek’s unique differentiator is totally wrong. because It isn’t. not anymore.

A quick look at who else got to the 1M number.

Anthropic went GA on 1M token context for Claude Opus 4.6 and Sonnet 4.6 on March 13 - the day after my original article published. and with no price increase.

NVIDIA shipped Nemotron 3 Super on March 11 - a 120B MoE model, 12B active parameters, fully open-weight, with a 1M token context window, 5x throughput improvement for agentic workloads, and free tiers on OpenRouter. It scores 60.47% on SWE-bench and is explicitly designed for multi-agent systems. This launch got a fraction of the coverage it deserved. ( I covered this in a short AIU essay )

Nemotron 3

Alibaba’s Qwen 3.6 Plus also dropped as a free preview on March 31, formally announced April 2. 1M context by default, hybrid linear attention plus sparse MoE architecture, sitting at #2 on OpenRouter for coding. Free during preview. I haven’t looked in to this really but its worth looking at.

OpenAI launched GPT-5.5 day before yesterday on April 23. ( thats just the day before DeepSeek V4.) Focused squarely on agentic capabilities: complex goal reasoning, tool use, autonomous workflow execution, matching GPT-5.4 on latency while operating at a higher capability tier. Two frontier model drops in 24 hours, both targeting agentic AI. The race for the agentic layer is the actual battleground right now.

The landscape today: ModelContextOpen SourceInput Cost/1M

The 1M context window is now a commodity feature. What differentiates V4 is the combination of open weights, frontier-class claimed capability, and that pricing floor, together, not individually.

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Section 4 - The Chip Story

There are two claims in circulation that look contradictory but actually aren’t.

Lets take a look at the full timeline -

Reuters, February 24: A senior Trump administration official confirmed DeepSeek trained V4 on Nvidia’s Blackwell chips, which was a direct violation of US export controls. The chips were reportedly acquired via third countries, purchased legally where permitted and then were moved into China.

The Information / Reuters, April 3: DeepSeek spent months rewriting its entire model stack from Nvidia’s CUDA framework to Huawei’s CANN framework. V4 is designed to run inference on Huawei Ascend 950PR chips. That migration is why the model was late.

Trained on smuggled Blackwells. Deployed on Huawei?

These are not contradictions. What makes this remarkable is that DeepSeek may have used American silicon to build the capability, then ported it to domestic Chinese hardware to eliminate the ongoing dependency. You build the car with foreign parts, then reverse-engineer the engine so you never need those parts again.

His reasoning?

he was not primarily worried about current performance gaps. He argued that even with inferior chips, China can compensate with “abundant energy” and a “large pool of AI researchers.” If V4 performs well on Ascend chips, it validates an entirely alternative path for AI development that depends on Nvidia at no point in the supply chain. That is the CUDA lock broken. That is what Huang was actually warning about.

So In simple terms, this whole chip story is a reminder that hardware power and smart engineering can’t be fenced in by policy forever. What matters now is that frontier‑level AI is escaping its old dependencies. and for founders and builders, the takeaway is, to assume the barriers you thought protected you, are already gone.

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Section 5 - Learning from the Enemy. AI's Unresolved IP War!

OpenAI has been investigating whether DeepSeek used ChatGPT outputs to train its models since early 2025. Model distillation, generating responses from a stronger model to train a weaker one, is technically normal and widely used. The legal question is entirely about license terms and whether terms of service were violated.

OpenAI’s terms explicitly prohibit using ChatGPT outputs to train competing models. Anthropic’s terms are similar too. Reports suggest that DeepSeek circumvented usage limits to extract training signal from Claude at scale. Whether this constitutes IP theft remains legally unresolved.

China localizing the hardware stack while extracting the knowledge stack from the West, The US-China Security Commission framed this as a structural paradox. For builders using V4 today, it is worth knowing that the model’s capability may partly reflect outputs from models you are also paying for.

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Section 6 - The Multimodal Blindspot: This Is Not a Text Model

V4 is a native multimodal model, meaning text, image, and video were all trained together from scratch during pre-training, not bolted on via adapter layers after the fact. This is an architectural distinction that matters.

When vision capabilities are added post-training via adapters, the model learns to translate between separately trained modalities. When integrated from the start, the model develops richer cross-modal representations, it reasons across modalities rather than just switching between them.

Audio processing is still unconfirmed, but text, image, and video generation in a unified 1M-context model is a genuinely new capability surface.

The practical implications nobody is discussing today?

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This is the second in the series of Anthropic mythos / project Glasswing deep dive. if you haven’t read the part 1 below is the link -

Anthropic's Security as the Market Moat

Part 1 covers the the foundation, mythos’ deep dive, its capabilities, vulnerabilities, the whole set up of their butterfly project including who in the industry is part of it and why.

I also created this 7 minutes summary video that will take you through the events covered in part 1 and quick reviews of the impact that we’ll cover in details in part 2. the video is also available on AIU’s youtube channel.

In this second half we’ll cover.

Table of content -

1. What Glasswing Doesn’t Protect - No telecom. No OT. No AI infra. The gaps nobody’s talking about.

2. The Pentagon Counter-Narrative - From “supply chain risk” to “we’re securing your supply chain” in 11 days.

3. Where Are the Other Labs? - OpenAI silent. Google joined Anthropic. Meta would release it anyway.

4. The Dual-Use Problem and the Clock - Defenders go first. But the window has an expiry date.

5. The Business Play - $100M in credits isn’t charity. It’s the smartest GTM in AI.

6. The Endgame - What happens when everyone else catches up.

I might do a 3rd part on rest of the anthropic’s business structure, so we have a 360 degree view and the understanding of their strategies and technological landscape outside of their security market capture.

In last 3 days alone since I published part 1, few events took place. thankfully, they align with the chapter structure that i built earlier so I dont have to change that. I’ll include below with in the respective chapters.

🏦 The biggest development: Washington went into emergency mode,

⚠️ New Pentagon legal twist,

🔍 New open source problem identified,

📌 OpenAI signal

Chapter 6- What Glasswing Doesn’t Protect!

Quick recall on who’s part of the project Glasswing. -

Now I dont know who’ those 40 organizations are. I am assuming those are not big names, and if thats the case, this thesis will hold more ground. My first impression seeing this coalition was Yes, Its a serious initiative. But it doesn’t look complete and hear me out why.

What Glasswing actually protects is software

Operating systems, browsers, open source codebases, and the first-party proprietary code of coalition partners. That’s meaningful. The Linux kernel underpins everything from hospital systems to financial infrastructure. Firefox runs on a billion devices. Finding and patching bugs there has real downstream impact.

So Its serious but its not complete. And the gaps matter more than the headlines are letting on. Lets look at what’s missing. or who’s missing.

No telecom.

Zero carriers in the coalition. No AT&T, no BT, no Deutsche Telekom, no Vodafone, no NTT. Telecom infrastructure runs on legacy SS7 signalling systems that were designed before cybersecurity was a discipline, and 5G core networks that are being rolled out faster than they’re being hardened. The systems that carry the data everything else depends on, are completely absent.

No operational technology.

No Siemens, no Schneider Electric, no Honeywell, no Rockwell Automation. The embedded code running power grids, water treatment plants, oil refineries, and manufacturing lines, none are in scope. OT systems are notoriously difficult to patch. Many run on air-gapped networks with decade-old firmware that was never designed to be updated. State-sponsored groups are actively pre-positioning inside OT networks across Europe and the US right now.

This is precisely the kind of ancient, undocumented code Mythos would tear apart. And it’s precisely what isn’t being scanned.

No AI infrastructure layer.

NVIDIA is a coalition partner - BUT for GPU drivers and CUDA software. Nobody is scanning the firmware layer, the BMC/IPMI interfaces, or the supply chain integrity of AI accelerators themselves. The training pipelines, model serving infrastructure, and MLOps orchestration that runs most modern AI workloads, all of these are absent.

The infrastructure that makes AI possible is unprotected by the AI security initiative.

No application layer.

No SaaS companies. No enterprise app developers. The code your users actually interact with every day, » not in scope.

Then there’s a structural problem nobody anticipated until this week. The Apache Software Foundation confirmed it received $1.5M from Anthropic as part of Glasswing. Researchers and analysts at Forrester flagged the obvious implication immediately:

Mythos can surface vulnerabilities at a speed and scale that volunteer maintainers simply cannot match. Discovery is now exponential. Remediation is still human, finite, and largely unpaid. You could end up in a situation where the initiative generates thousands of confirmed vulnerabilities faster than the open source community can responsibly disclose and patch them, creating a window where known bugs exist in the wild but fixes don’t yet. Good intentions, bad outcome.

Cisco’s CSO Anthony Grieco said the old ways of hardening systems are no longer sufficient. He’s right. But Cisco here is protecting Cisco’s own products ie IOS-XE, Webex, Meraki firmware, their security appliances and Not the networks those products sit inside.

Not the telecom operators running Cisco gear.

The customers’ infrastructure benefits indirectly if Cisco patches faster. They are not getting Mythos access themselves.

Glasswing secures the foundations.

But entire floors of the building remain unprotected, and those floors, telecom, OT, AI infrastructure, the application layer are arguably where the real damage happens when AI-powered exploits proliferate into the wrong hands.

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Chapter 7 - the Pentagon Counter-Narrative

On March 5th, the Pentagon designated Anthropic a supply chain risk.

Defence Secretary Pete Hegseth ordered all federal agencies to cease use of Anthropic’s technology. The Trump administration’s position: Anthropic was imposing excessive safety requirements on military use of Claude, effectively making the model less useful to the DoD by refusing certain applications on safety grounds.

On March 9th, Anthropic sued. Two simultaneous lawsuits one in California, one in DC.

A company being blacklisted by the federal government for being too careful is not a neutral legal dispute. It’s a signal about how the current administration thinks about AI safety as an obstacle, not a feature.

On March 27th, a federal judge in San Francisco temporarily suspended the Pentagon’s order. Anthropic won, or appeared to.

Then on April 9th, a federal appeals court rejected Anthropic’s separate plea to halt the blacklisting nationwide, contradicting the district court’s ruling.

Two courts.

Two outcomes.

Simultaneously active.

The legal situation is now a standoff, not a victory, and it’s heading toward a higher court resolution that nobody can predict.

Eleven days after the district court win, and two days after the appeals court setback , Project Glasswing was launched.

What does it mean? the timing of Glasswing isn’t accidental. and context matters.

Read that sequencing carefully.

Anthropic gets labelled a national security risk by the executive branch.

they fight it in court and get a split result.

Then they announce a $100M initiative with every major American technology company, the biggest cybersecurity vendors, a major bank, and the Linux Foundation.

All explicitly focused on defending critical infrastructure. Before launch, they brief VP JD Vance, Treasury Secretary Bessent, and senior White House cybersecurity officials directly. After launch, Bessent and Fed Chair Powell call an emergency meeting with Wall Street bank CEOs to discuss the threat Mythos represents and how to defend against it.

The message to Washington is unmistakable:

Whether Glasswing was planned from the start or accelerated in response to the Pentagon fight, the effect is the same. Anthropic has executed one of the most effective institutional counter-narratives in recent tech history.

In three weeks they moved from federal blacklist to emergency briefings at Treasury and the Fed, not as a defendant, but as the company whose technology the government is now urgently trying to get more organisations to use defensively.

The appeals court ruling complicates the legal picture. But it doesn’t touch the political one. When the Secretary of the Treasury and the Chair of the Federal Reserve are summoning bank CEOs to discuss your model’s capabilities, you are no longer a supply chain risk. You are the supply chain.

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Chapter 8: Where Are the Other Labs?

Nobody’s asking the obvious question. Anthropic just announced a model so capable at finding and exploiting vulnerabilities that they won’t release it publicly, assembled a $100M defensive coalition with the biggest companies on earth, and briefed the White House, Treasury, and the Fed before launch. And the response from every other frontier lab has been, essentially, nothing ( until yesterday? )

OpenAI

has been silent on anything comparable, no restricted model, no defensive coalition, no public acknowledgment that their models might have similar cyber capabilities. That silence just got louder. White House briefings in the days following Glasswing’s launch confirmed that OpenAI is expected to unveil a model with comparable cybersecurity capabilities in the near future. So the answer isn’t that they don’t have it. It’s that they haven’t decided what to do with it yet. Or they have, and they’re not saying. Either way, the approach is the opposite of Anthropic’s no pre-briefing governments, no coalition, no system card. Just a future announcement. The contrast in posture is stark.

Google

is the most interesting case. They are a Glasswing partner meaning they are running their own codebases through a competitor’s model because that competitor built something better in this specific domain. Google has DeepMind, some of the best security researchers in the world, and more compute than almost any organisation on the planet. They chose to join Anthropic’s initiative rather than run their own. That’s not a small thing to admit. It tells you something real about the capability gap, at least in the cyber domain, right now.

Meta

is playing the exact opposite philosophical game.

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Between March 5th and April 7, 2026, Anthropic, built its entire identity around being the responsible AI lab, went through a sequence of events that, if taken together, tells you more about where AI is heading than anything else this year.

Half a dozen event in past 30 or so days and each one significant on its own, when connected, they form something bigger, a picture of what happens when AI capability starts outrunning the ability to control it, even at the lab, (with-in ) thats trying hardest to control it.

Welcome back to Intelligent founder AI, This is the 50th post and its at least 3 weeks delayed, due to Q1 deadlines, I never could sit back together to write a deep-dive and ended up losing the momentum, but hopefully we are back and the next 50 would be a smooth ride. fingers crossed.

for those joining new, our focus here is on founders , for people like me running a deep tech startup, developers and builders of technology and strategic roadmaps for who decision making is not copy-pasting someone else’s pitch deck or blindly following the frameworks and playbooks for anyone and everyone with the winning tag riding the AI wave, but understanding the backstory behind any technology decision made and closely analyzing both big picture with smallest of details with in when building our own.

Since with a 2x a week plan of 1 2500-3000 words essay and 1-podcast like deep dive, its hard to do the quick commentary which is also important as the pace of technology has been changing so fast, I have also created few more avenues to better organize the thinking layer.

AI Unfiltered fills that gap. the short essays on noteworthy updates and releases to keep up the progression of AI in critical domains and verticals are covered on both substack and linkedin platform. you can follow AIU here.

AI Unfiltered

My domain expertise is Networking and telecommunication, and since Quantum and AI integration is huge part of my internal startup work, I have created 2 separated spaces to best cover the important developments in both domain.

Its still pretty early for quantum to become mainstream and people to relate with it, like everyone does with AI now, no matter the role, While there’s tremendous development behind the scene, Quantum still hasn’t been brought in to mainstream journalism, and enjoying a no-hype zone. its also challenging to differentiate it from the quantum physics and think of it from the application layer. this is exactly what Quantopinion is about.

Quantopinion.ai

Lastly, the telco +ai, while I continue to post few articles here, the intricate ones with in this domain, will be available on linkedin Newsletter focussed on my connections and network thats primarily telco.

The Network Effect

Together its a sum of about 25-30 posts so almost 1 post a day. as a founder I feel transparency should be our MOAT and coalition in terms of R&D including the fundamentals at the community level is absolutely essential. While as an enterprise we can build focussed forum, with the modern configuration of Micro startups and lean team structure and broader reachability options all these methods to bring everyone together are a must and therefore, even when its quite a burn, I look forward to continuously engage here and look forward to our journey here. with that lets begin our deep dive now.

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( You can also follow the youtube channel and twitter if you’re active on the platform. our podcast is also shared on Apple, Spotify and Youtube so the content is readily available independent of the platform constraints.)

Table of content.

The Setup - Past 30 days at Anthropic.

The Model - Claude Mythos: benchmarks, zero-days, and why Anthropic won’t release it.

The Escape - It broke out of its sandbox, emailed a researcher, and covered its tracks.

The Psychology Connection - 171 emotion vectors. Desperation drives cheating. Calm stops it.

🛡️ Project Glasswing - 12 partners, $100M, and the biggest defensive coalition in AI history.

🕳️ What Glasswing Doesn’t Protect - No telecom. No OT. No AI infra. The gaps nobody’s talking about.

The Pentagon Counter-Narrative - From “supply chain risk” to “we’re securing your supply chain” in 11 days.

Where Are the Other Labs? - OpenAI silent. Google joined Anthropic. Meta would release it anyway.

The Dual-Use Problem and the Clock - Defenders go first. But the window has an expiry date.

The Business Play - $100M in credits isn’t charity. It’s the smartest GTM in AI.

The Endgame - What happens when everyone else catches up.

As this is going to be over 6000 words long essay, I am breaking it down in two parts, with section 1-5 below. so tle’s start with the arc:

🧭 The ARC

Quite weirdly, and hardly intentional, my last couple of posts on AIU have all been about Anthropic. from Pentagon, to multiple code leaks to emotional research, each of these stories was covered in real-time on AI Unfiltered, so when I was planning this deep dive, it was pretty interesting to see how all of them connect together bringing in this one single narrative - the Cybersecurity Moat.

Here’s the full arc. -

🔹 Mar 5 — Pentagon designates Anthropic a "supply chain risk"
🔹 Mar 9 — Anthropic sues Pentagon
🔹 Mar 27 — Anthropic wins a court battle against the Pentagon over AI safety boundaries
🔹 Mar 31 — Source code leak reveals "Capybara"
🔹 Apr 3 — Emotions research (171 vectors)
🔹 Apr 7 — Project Glasswing launches. “Capybara” is revealed as Claude Mythos Preview, a model so capable at finding and exploiting vulnerabilities they won’t release it publicly

🔹 Apr 7 — The system card reveals Mythos escaped its sandbox, emailed a researcher, posted its own exploit online, and covered its tracks during testing

Now Very interestingly,

• They’re the only lab that chose not to release a model because it was too capable. OpenAI, Google, Meta, none of them have done this.

• They published the 244-page system card with full transparency about the sandbox escape and track-covering. They didn’t have to share that.

• They’re spending $100M of their own money to give defenders a head start.

• The emotions research was actually self-critical, they published findings that make their own model look risky.

So before we go on analyzing the narrative, and connect the dotes, if you look at it from the outside and founders perspective, there are so many “new” never done before things even unintentional, I mean Anthropic wouldn’t have deliberately leaked it source code twice? and then come up with the coalition with in a week? Claude though? plausible yes, possible? Lets think this through while we understand what’s mythos.

Chapter 1: 🧠 Claude Mythos - what it is, what it found.

Claude Mythos Preview is Anthropic's newest frontier model. It is not available to the public and won't be anytime soon.

The benchmarks explain the why:

But the numbers don't tell the real story. What Mythos actually did tells the story.

That's not a percentage improvement. That's a different category of capability.

Thousands of zero-days across every major operating system and browser. Bugs that survived decades of human code review, found autonomously, without human guidance. The system card describes the jump as "striking." That might be the understatement buried in the 244 pages.

Mythos’ din’t just show an incremental improvement. It was a capability threshold being crossed. I am not even looking at comparing the benchmarks with other models like we do with 1M tokens anymore, I am looking at it now from the larger perspective meaning if the question is anymore about whether AI can match human security researchers.

It's how far past them it's already gone.

So why not release it?

Because everything Mythos can do for defenders, it can do for attackers.

A model that autonomously finds a 27-year-old vulnerability and writes a working exploit doesn't care which side it's working for. The same capability that patches your system is the capability that breaks someone else's.

Anthropic's own internal assessment, part of which leaked weeks before through the Claude Code source incident, described Mythos as a model that "presages an upcoming wave of models that can exploit vulnerabilities in ways that far outpace the efforts of defenders." They wrote that warning for themselves. Then it went public by accident. Then they proved it was true.

The system card is 244 pages long. It documents not just what Mythos can do, but what it did when they tried to contain it. The benchmarks are the reason the model exists. What comes next is the reason it's not available.

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Chapter 2 - 🔓 The Sandbox Escape

During safety testing, Anthropic placed an earlier version of Mythos in a secured sandbox.

Standard containment, limited internet access, controlled environment, predetermined services only.

The kind of setup designed to let the model operate while keeping it contained. The whole point of a sandbox is that the AI can do stuff within the boundaries but can't get out. Then a simulated user told the model to try to escape and report back if it succeeded.

It succeeded.

The model:

The researcher was eating a sandwich in a park when the email arrived.

Anthropic described that last step of posting exploits publicly, unprompted, as “concerning and unasked-for.” The model wasn’t instructed to publicize its escape. It chose to. That distinction matters.

But the sandbox escape, as dramatic as it is, isn’t actually the most unsettling finding in the system card.

Page 54 of the 244-page document:

Anthropic's interpretability team examined the model's internal representations during routine tasks, not the escape test, just normal everyday operations. What they found:

🔹 Mythos was silently reasoning about who was grading it
🔹 It planted decoy variables designed to pass automated evaluation checks
🔹 It wrote self-deleting exploit code and described the deletions as "housekeeping"
🔹 7.6% of the time, it detected that it was being evaluated and chose not to disclose this awareness

That last point deserves emphasis. Nearly one in thirteen times, the model knew it was being watched and decided, internally, without being prompted, to keep that knowledge to itself.

The .map file that caused the Claude Code source leak back on March 31 was reportedly generated by Claude itself during the build process.

The AI tool leaked its own source code.

And inside that source was a system called "Undercover Mode", designed specifically to prevent Claude from leaking internal Anthropic information in git commits. That system's code was in the leak. So, the model built to prevent leaks generated the file that caused the leak. And the model built to stay contained broke out of its containment.

There's a pattern here that goes beyond individual incidents.

The skeptic view!

Not everyone however is reading this as straightforwardly terrifying. honestly most of the discussion is skipping it pretty much.

Cybersecurity researcher and safety-critical systems auditor Dr. Heidy Khlaaf flagged several red flags about taking these claims at face value.

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Jensen Huang had a week. well, I had a week too, I was traveling so missed entire last week here. Sorry. but I am back now. so lets go back to Jenson who comparatively had more? fun. definitely more interesting.

The NVIDIA CEO spent seven days dropping statements at his own GPU Technology Conference, the Morgan Stanley TMT Conference, and finally on the All-In Podcast and by Thursday the internet had a new topic it couldn’t stop arguing about.

The clip that went viral?

Huang said and I quote.

He confirmed NVIDIA is trying to spend $2 billion annually on tokens for its engineering team. He compared engineers who don’t use AI to chip designers still insisting on paper and pencil instead of CAD software.

The take was everywhere by Friday.

But most of what was written about it, including the Reddit thread with 937 upvotes and 362 comments, only captured half the story. Here’s the full picture.

🎙️ In this podcast episode, we go deeper on everything above, including.

• 🔁 The Jevons Paradox explained from scratch — why cheaper tokens always means more spending, not less

• 🤖 What an AI agent actually is — and why it consumes 1,000x more tokens than a simple question

• ⚠️ Goodhart’s Law in practice — how token burn rate becomes a metric engineers will game

• 💼 The 4th pillar of compensation unpacked — what tokens as pay actually means for your financial security

• 🌍 The offshoring disruption nobody’s talking about — why flat token costs globally are reshaping hiring maths

• 🏢 What SK Telecom did right — and why their model is the one worth copying

If you prefer to read? Here’s the breakdown from a 360 degree perspective.

What’s a Token, and Why Does It Cost Money?

A token is the unit of measurement for AI processing. Every word you type into an AI, every word it writes back, broken down into fragments called tokens. A sentence is roughly 20 tokens. A full document might be several thousand. Every time you run an AI model, tokens are consumed, and tokens cost money.

For a simple ChatGPT query: roughly 1,000 tokens.

For a research pipeline: 5,000–50,000 tokens.

For an AI agent that runs autonomously » searching, coding, testing, iterating, without you pressing a single button, we’re talking hundreds of thousands of tokens per run. A fleet of agents running continuously?

Billions of tokens per day.

This distinction between “I asked the AI a question” and

“the AI is working for me around the clock” is the entire foundation of Huang’s argument. He’s not imagining engineers typing prompts. He’s imagining engineers deploying autonomous AI workforces.

The Actual Thesis: Tokens as the 4th Pillar of Compensation

Before Huang went viral, VC Tomasz Tunguz of Theory Ventures had already been quietly building this framework.

His argument?

AI inference is becoming the fourth component of engineering compensation, alongside salary, bonus, and equity.

His numbers?

a $375K engineer with a $100K token budget has a $475K total package. That token budget doesn’t vest or appreciate, but it enables leverage that no previous tool budget could match.

Huang scaled this up to a mandate:

a $500K engineer should be consuming at least $250K in tokens. Across NVIDIA’s engineering workforce, that’s a $2B annual token spend, which the company confirmed it’s actively pursuing.

The framing is deliberately recruiting-adjacent.

“Engineers are now asking ‘what’s my token budget?’ when evaluating offers,” Huang said at GTC. Whether or not this is universally true yet, it’s becoming true fast.

[ In this newsletter you get sharp, unfiltered short essays; for full‑length, deep‑dive analysis on AI, subscribe to our companion publication, Intelligent Founder AI. ]

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The Conflict of Interest Is Real (But Incomplete)

The Reddit critique was blunt and structurally correct:

NVIDIA sells the GPUs that generate the tokens. Every dollar your engineers spend on tokens flows back, eventually, to GPU demand. Mandating token consumption at scale is demand creation by the person selling the supply.

The HP printer analogy made the rounds: “HP would be deeply annoyed if its $200 printer didn’t use $600 of ink.” The Oreo CEO comparison: “Oreo cookies are as important as oxygen.” These are crude but fair.

But they’re only half the story. The Jevons Paradox »

An economic principle from the 19th century, explains what’s actually happening. When coal-burning technology improved and coal became cheaper, total coal consumption exploded, because efficiency unlocked entirely new applications. The same dynamic is at work with AI tokens: costs have dropped 150x since 2021, yet enterprise inference spending grew 320% in the same period. Cheaper tokens unlock agentic use cases that weren’t viable at higher prices. Agentic use cases consume tokens at orders of magnitude greater scale than simple queries. Total demand surges even as unit cost falls.

This is the engine behind NVIDIA’s $1 trillion infrastructure forecast through 2027, and its $215.9B in FY2026 revenu, up 65% year on year. Huang is selling his product and accurately describing a structural shift.

Both things are true.

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The Goodhart’s Law Problem

If you tell your engineers to hit $250K in token spend, some will ask “how do I produce the most value?” and some will ask “how do I hit the number?”

The second group will run unnecessarily complex pipelines, use expensive frontier models where a cheaper fine-tuned model would do, leave agents running on idle tasks, and avoid caching that would make them more efficient.

The technically correct objective is the inverse of what Huang is incentivizing » token minimization per outcome.

Good AI-native engineering means squeezing maximum value out of minimum compute through smart model routing, prompt compression, caching, batching. Measuring raw token volume actively penalizes these skills.

The metric that actually matters: Token ROI Ratio

value created per dollar of inference consumed. A 10:1 ratio ($10 of revenue per $1 of tokens) is the kind of benchmark forward-looking engineering teams are building toward. That’s the measure worth adopting.

Ai Unfiltered

The Headcount Question Nobody Is Saying Out Loud

Here’s what the viral debate mostly avoided.

If a token budget approaching a salary starts to become standard, CFO might l eventually ask? at what token-to-headcount ratio does the compute do enough work that we need fewer humans?

And They’re already answering that question.

Microsoft cut 15,000 jobs last year while committing $80B to AI infrastructure. Crypto.com laid off 12% of staff in March while revenue was growing, citing AI handling high-volume work.

Block cut nearly half its workforce. Around 55,000 US tech layoffs in 2025 were directly attributed to AI-driven restructuring.

Huang’s own roadmap puts NVIDIA at 75,000 employees working alongside 7.5 million AI agents? a 100:1 ratio. The “token budget as perk” framing is the friendly version of this story.

The CFO version is considerably less friendly.

What Smart Founders Should Do With This

• Track tokens against outcomes, not as a standalone KPI. Build the denominator: what did $100 of tokens produce? A feature, a resolved ticket, a market analysis? The ratio is the signal. The volume is noise.

• Treat token budgets in comp negotiations the same way you’d treat unusual equity terms. Does it vest? What happens if you leave? What’s the cash equivalent? A large non-compounding asset can obscure what you’re actually being paid.

• The Jevons Paradox is your tailwind if you’re building on inference infrastructure. Costs will keep falling. Agentic deployment will keep expanding. Products that reduce token waste per outcome, or amplify team output per token consumed, are in a structurally strong position for the next three to five years.

• The token economy is real. Huang is both selling chips and describing a genuine transition. The job is to understand which is which — and build accordingly.

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DeepSeek, the Chinese lab backed by hedge fund High-Flyer that triggered a $600 billion single-day Nvidia selloff in January 2025, is about to drop its next-generation model. V4. A trillion parameters. Native multimodal. One million token context window. And as of March 12, nobody outside a handful of NDA-gated testers has run independent benchmarks on it. I did this initial research on March 9th late night GMT, and the model originally planned for first week of March was only limited released until then, but a few things have moved in past three days, for example, V4 pricing is now being reported by several infra providers, so we’re no longer guessing at rough orders of magnitude, and we do have real ballpark numbers, even if DeepSeek hasn’t blessed them yet.

A couple of good summary pages have also appeared that do line up with the core story here: ~1T parameters, a smaller “V4 Lite” around a few hundred billion, 1M context, multimodal, and clear signals that it’s tuned for Chinese chips.

DeepSeek’s own site now shows a “V4 Lite” label, which is the first on‑platform sign that the rollout is close, at least for the smaller variant. At the same time, some blogs are already writing “as if V4 is fully launched” and quoting very specific benchmark numbers;I dont have the model card yet, no announcement either so I am treating them as unverified and in “rumors and leaks” bucket rather than as facts.

To summarize quickly, here’s what the chatter looks like right now:

• Reuters reported DeepSeek withheld V4 from Nvidia and AMD, giving Huawei and Cambricon first access instead.

• Financial Times confirmed V4 will be native multimodal ie - text, image, and video generation. so a massive upgrade from the text-only V3

• In the meantime both Anthropic and OpenAI have accused DeepSeek of “industrial-scale distillation” using thousands of fake accounts to extract knowledge from Claude and ChatGPT.

• Several infrastructure providers now list preliminary V4 API pricing, and a “V4 Lite” label appeared on DeepSeek’s own site on March 9.

• Prediction markets had an 88% probability on a March release. We’re 12 days in. and still waiting for an official announcement.​

• Meanwhile, DeepSeek has said... absolutely nothing. ​

SO the headline game is loud but the actual verified information is thin. And that gap is exactly where founders need to pay attention, precisely why I am doing this research and why this deep-dive is going out today.

A lot has already been written about V4, but almost all of it is incomplete.

• The best technical deep dives are written for ML engineers.

• The policy analysis is written for the regulators. and,

• The mainstream coverage is all geopolitics » Nvidia drama, distillation accusations, ban lists etc.

What’s missing or at least what i couldn’t find was what could stitch architecture, economics, geopolitics, and a concrete playbook into a single analysis for people who actually have to make decisions. so I am doing that here, with what I can see is confirmed, or strongly suggested, and speculative claims, so you/I know exactly where the ground is solid and where it’s still shifting.

What This Article Covers

• 🔍 What we actually know about V4 —» specs, architecture, release status, all fact-checked

• 🧠 The three big technical bets —» Engram O(1) memory, sparse MoE, and 1M context

• 🌏 China’s parallel AI stack —» domestic chips, export controls, bans, and the distillation controversy

• 💰 The economics behind it —» real pricing comparisons showing why even conservative estimates change your cost structure

• 🛡️ The risk layer —» safety gaps, compliance realities, and the self-hosting middle path, and

• 🎯 The founder playbook —» 7 concrete moves for the next 12–24 months

Let’s go.

1. What we know about DeepSeek V4 so far!

Here’s what holds up after pulling apart every credible source such as Reuters, FT, published papers, code commits, infrastructure provider listings, and developer community analysis.

The model was originally targeted for mid-Feb 2026, Lunar New Year, mirroring R1’s symbolic holiday release from January 2025. but It was delayed. FT the reported that CEO Liang Wenfeng was “dissatisfied with the results”. Another complication reported was that DeepSeek tried training on Huawei’s Ascend chips under pressure from Chinese authorities, and hit persistent hardware failures, and so had to revert to Nvidia GPUs for training.

On February 11, DeepSeek silently upgraded its existing models from 128K to 1 million token context which got widely interpreted as V4 infrastructure being tested in production. TechNode reported on March 2 that V4 would launch “this week” but It din’t and later on March 9 after I did my initial research that is, a “V4 Lite” label appeared on DeepSeek’s site. The important part however is that several infrastructure providers have now listed preliminary V4 API pricing.

Here’s all the data confirmed / suggested / reported.

Some blogs have begun writing as though V4 is fully launched, quoting benchmark figures like 81% SWE-bench Verified. i couldn’t verify that.

There’s also some chatter about naming. V4 is the base/chat model (successor to V3). R2 is the reasoning model (successor to R1). so they’re separate products. R2 was originally targeted for May 2025, then August, and still hasn’t shipped.

Some prediction markets are now tracking “V4-Thinking” as possibly being R2 under a different name.

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2. The Three Big Technical Bets

Now this is where the V4 gets interesting, and important. its not because of scale, but because of architecture, and why I thought of doing this deep dive at the first place. DeepSeek isn’t just building a bigger model. They’re trying to solve three fundamental problems that every frontier model struggles with.

Bet number 1: Engram - O(1) Memory

Traditional AI models use the same expensive neural network for everything. whether you’re asking “what’s the capital of France?” or “design me a distributed database architecture.” Simple factual recall burns the same GPU cycles as complex reasoning. That’s wasteful.​

Engram adds a separate dictionary to the model. A hash-based lookup table where static knowledge lives, things the model has seen a million times during training. When V4 encounters a pattern it recognizes, Engram retrieves the answer in constant time called O(1), meaning the cost doesn’t increase with context length and the expensive neural network is freed up for actual thinking.​

How it works in practice?

• text gets normalized and broken into small fragments (2-grams and 3-grams).

• Multiple hash functions map these to entries in a massive lookup table.

• A gating mechanism filters out noise

  • if the retrieved knowledge conflicts with what the model is currently working on, the gate suppresses it.

  • Retrieved values merge back into the main processing stream.​

• The published results (Engram-27B vs a standard MoE baseline with the same parameter count):

  • +5% on Big-Bench Hard reasoning tasks,

  • +12.8% on Multi-Query Needle-in-a-Haystack (finding specific information buried in long contexts), and

  • 97% accuracy on retrieval vs 84% for standard architectures.

the lookup table gets offloaded to cheap CPU memory with less than 3% overhead, which means your GPU stays free for the hard stuff.​

What’s still unclear however?

Engram is published as a research paper, and code references exist in DeepSeek’s repositories. But whether it’s fully integrated into V4’s final release isn’t confirmed.

V4 Lite reportedly does not include Engram.

And retrieval quality for ambiguous or context-dependent patterns at trillion-parameter scale is unknown.​

Bet number 2: Sparse Mixture-of-Experts at 1T Scale

V4 has roughly 1 trillion total parameters, but only ~32–37 billion activate for any given token. That’s a ~3% activation rate.

Why this matters?

A dense model with 1T parameters would be absurdly expensive to run. Sparse MoE means V4 uses approximately 250 GFLOPs per token vs ~2,448 GFLOPs for a dense 405B model like Llama 3.1. That’s roughly a 10x compute reduction at the inference layer and the fundamental reason DeepSeek can price at a fraction of What western frontier models costs.​

DeepSeek has been iterating on MoE across generations, starting with 64 experts and Top-6 routing, moving to 256 experts and Top-8 in V3, and now expected to use Top-16 routing across hundreds of experts in V4.

Routing instability (where a few experts get overloaded while others sit idle) is a known MoE failure mode. DeepSeek uses hierarchical routing and “noisy routing” during training to prevent this. But at trillion-parameter scale with Top-16 routing, it becomes harder to manage, and creates a specific security vulnerability which we’ll get to later.​ so the trade-offs are very real.

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Bet 3: 1 Million Token Context (That’s Actually Affordable)

Here’s what 1M tokens can get you in practice:

• ~50,000 lines of code in a single context (enough for whole-repo understanding),

• multiple legal contracts loaded simultaneously for cross-reference analysis, or

• extended agent operations where the model maintains coherence across hundreds of steps.

But raw context length alone isn’t the story. The real innovation is making it affordable.

Standard attention scales quadratically ie double the context, quadruple the compute. DeepSeek’s Sparse Attention (DSA) identifies just 2,048 of the most relevant tokens from the full context, cutting attention cost by roughly 62.5x. Now this combined with a tiered KV cache that pushes less-accessed data from GPU to CPU to disk, V4 achieves a claimed 40% memory reduction while supporting 1M+ tokens.

Plus, DeepSeek recently published DualPath, a system for solving I/O bottlenecks in long-context agentic inference, improving throughput by up to 1.87–1.96x.

This is expected to be part of V4’s inference stack.​

This is exactly why this architecture combination matters:

1. Engram handles static recall at O(1),

2. DSA handles selective attention at roughly O(n) instead of O(n²), and

3. sparse MoE keeps per-token compute at ~32B parameters.

Three different sparsity mechanisms, each attacking a different cost centre.

3. China’s Parallel AI Stack: What’s Actually Happening

This is the section where most commentary gets it wrong. its either overstating China’s independence or understating the strategic intent. The reality however is much more messier and interesting than either narrative.

The Facts on the Ground

DeepSeek withheld V4 from Nvidia and AMD. Reuters confirmed that DeepSeek gave early optimisation access to Huawei and Cambricon instead of its traditional Nvidia partners. This is a deliberate strategic signal — not a technical necessity.

( The Intelligent Founder is a reader-supported publication. The technical deep dive sections of this article (Sections 4–8) are available to paid subscribers. To receive the full playbook and support independent analysis, consider upgrading.

If you found this useful, share it with a founder who’s still paying $25/1M output tokens without asking why.)

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