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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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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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.

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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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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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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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This year’s Super Bowl wasn’t just a football game… it was a three‑hour pitch deck for the AI economy. Between drives and timeouts, the usual parade of beer, cars and movies was crowded out by chatbots, HR platforms, AI glasses and prescription molecules. Slack showed up with MrBeast, Squarespace handed a sledgehammer to Emma Stone, Amazon used Chris Hemsworth to humanise Alexa+, and Anthropic used its very first TV buy to roast OpenAI for putting ads inside ChatGPT.

Business Insider and others report including ad trackers say Anthropic’s spot actually out‑performed OpenAI’s in positive sentiment, even as OpenAI drove more overall chatter. Meanwhile, ChatGPT ads just moved from announcement to execution yesterday with partners like Target’s Roundel being among the first to try contextual ads.

Tech Journalism Going Mainstream

I wrote about how tech journalism was going mainstream back in July, and this whole saga lines up perfectly with that shift, as well as with the way tech brands now look more like lifestyle companies and AI is taking over the biggest advertising stage in the world.

I want to use this article to zoom out from the OpenAI vs Anthropic drama and look at what’s actually shifting in culture. I’m writing it this way because founders just don’t just need a hot take on Open War, they need a practical lens on what this new “AI Bowl” moment means for how they design brands, plan launches, and monetize products in 2026, and to understand if You/I need a Super Bowl moment too?

Table of Contents ( Part 1, 2)

1. 🏈 AI Bowl, Not Football
   Super Bowl LX as the moment AI platforms, B2B SaaS, and health‑tech took over the world’s most expensive ad inventory.

2. 🎭 Brand Glow‑Up: From Scary to Soft
   How AI companies use pastels, nature vibes, and friendly UX to defuse fear and build trust—and what founders should copy.

3. 📰 AI as Front‑Page Sport
   Why tech journalism now treats AI like the NFL, and how that changes the risk/reward of every launch, mistake, and pivot.

4. ⚔️ ChatGPT Ads vs. Anthropic: Monetization War
   Deep dive into ChatGPT’s new ad model, Anthropic’s “no ads” stance, and what this reveals about business‑model positioning.

5. 📊 Monetization Matrix: Pick Your Lane (Framework) ( in part 2)
   A category × revenue matrix to decide if you’re playing in consumer‑ads, prosumer‑SaaS, enterprise‑usage, or infra‑platform, and how that drives GTM.

6. 🚀 Super Bowl Moment Playbook (System)
   A 120‑day launch operating system—pre‑launch, spike, activation, retention, monetization—for handling your own 10–100x attention moment.

7. 🔁 90‑Day Funnel: From Hype to Habit (Template)
   Numbers‑driven funnel showing how to turn one big spike into activated, retained, and paying users instead of one‑day vanity metrics.

8. 🧠 Founder Checklists & Scripts (Paid Section)
   Concrete checklists, positioning scripts, and landing‑page/announcement patterns to align brand, pricing, and narrative around your “AI Bowl” moments.

9. 🧩 Final Strategy Snapshot
   A concise, plug‑and‑play summary of the key decisions (segment, brand, GTM, monetization) every founder should make before their next big launch.

Note: This deep dive got way too long in 5 sections only so I am breaking this article down in two parts, so I can release it now. Part 1 is section 1-4 and is free, and section 5-9 in part 2 that will be paid and I’ll release it day after. I am still writing it so if you’ have questions on the content skeleton do post them in comments or chat.

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1. 🏈 AI Bowl, Not Football

This year’s Super Bowl wasn’t just a football game; it was a three‑hour status update on who actually runs the modern economy. Broadcasters were selling 30‑second units for an average of $8M, with premium slots pushing beyond $10M, a new all‑time high. That price tag is only rational if you believe two things: first, that the Super Bowl is one of the last true monoculture moments (120M+ simultaneous viewers); and second, that your category is in a winner‑take‑most race where mass awareness compounds into long‑term dominance. That’s precisely how the leading AI and software players seem to see the world.

The chart above sketches how AI‑related spend in this year’s game clusters: the heavy dollars come from Gen‑AI services and platforms, followed by enterprise SaaS and infrastructure, with a smaller slice from consumer/education tools and individual players. Even without exact disclosure from every brand, the pattern is obvious from the roster.

1.1 Who Actually Showed Up in the AI Block

If you only half‑watched the breaks, here’s who was really buying attention in what many are calling the “AI pod” of the night:

• OpenAI (ChatGPT) – A cinematic 60‑second spot framing ChatGPT as the next great interface for creativity and problem‑solving, and, between the lines, justifying why there are now ads in the free tier.

• Anthropic (Claude) – A 30‑second in‑game ad plus a 60‑second pre‑game film mocking the idea of ads interrupting intimate AI conversations, implicitly targeting OpenAI’s new ad product.

• Slack – Workplace chaos turned into comedy with MrBeast trying—and failing—to keep up without channels and workflows, making the point that Slack is infrastructure, not “just messaging”.​

• Squarespace – A surreal, film‑noir, computer‑smashing ad starring Emma Stone, leaning into high‑end craft to position website building as cinematic and almost mystical.​​

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A deep dive into the $300 billion company that said one thing and did another.

I am not mad at ads. Except YouTube, of course, because they just go on and on if you don’t click skip. which breaks my flow of whatever I am doing while youtube plays in the background. I also never had a ChatGPT subscription and never planned to, so this isn’t an emotional “I’m cancelling my ChaptGPT subscription” piece. But I am seeing tons of discussion and articles focussed on outrage, migration and skepticism and I wanted to make a different point. so here we go!

In May 2024, Sam Altman stood at Harvard and said something that now reads like a punchline: “Ads plus AI is sort of uniquely unsettling to me. I kind of think of ads as a last resort for us for a business model.”​

Eight months later, OpenAI announced it would begin testing ads in ChatGPT.​

But the real story isn’t Altman’s reversal, it’s what happened in between.

In May 2025, OpenAI quietly hired Fidji Simo as CEO of Applications. Her resume? She led the launch of ads in Facebook’s mobile News Feed, oversaw Facebook’s entire advertising business, and later took Instacart public while building a retail ads business with over 5,000 advertisers.​

When you hire someone with that background, you’re not preparing for a “last resort.” You’re building an advertising empire.

In this article I break down what’s really happening:

the financial pressure forcing OpenAI’s hand, the privacy escalation that should concern everyone, and whether the company that promised to save humanity from dangerous AI has quietly abandoned that mission. or was it always Mission AGI ( Ads Generated Income)

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What’s in this article -

📅 The timeline that exposes the contradiction
How OpenAI said one thing and did another

📢 What people are saying online
Reactions from Twitter, Reddit, and LinkedIn

💰 The financial math
Why ads were inevitable, and the $115 billion problem

🔒 The privacy escalation
From what you watch to what you think

🔄 The enshittification playbook
How good platforms turn bad and if AI is next

⚠️ The liability nightmare
When health advice meets advertising

🎯 The AGI question
Did they abandon the mission?

🏁 Who stays ad-free?
Your alternatives to ChatGPT

🔮 Predictions
What happens next (2026-2028)

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1- The Social Media Reaction

The Ad announcement has set off a wave of backlash across Twitter, Reddit, and LinkedIn, with people basically saying, “Yep, we saw this coming.”​

On Twitter/X, paid users were furious that even Pro subscribers paying $200 a month were actually seeing what looked like ads baked into answers, from Peloton plugs in random chats to “shop at Target” suggestions in technical questions.

Sam Altman’s old “ads as a last resort” line was dragged back into the spotlight and mocked as a complete U-turn., and most mainstream chatter is still focused on this.

On Reddit, people argued over whether mixing ads into AI replies would quietly erode trust, while others doubted there would be any real mass exodus, pointing to past internet outrage that never went anywhere.

Some users did note that OpenAI hiring the monetizing operative who squeezed maximum ad revenue out of Facebook made this shift feel inevitable, especially for a company valued at $300 billion while burning billions a year, and now turning in to facebook 2.0 or is it 3.0 really.

On LinkedIn, posts compared Altman’s stance to early Google promises about not letting money influence search results and warned investors to treat any “we’ll never do X” claims from AI companies with serious skepticism.

Others said this all looks like the classic Netflix playbook: first “no ads ever,” then ads once everyone is hooked.

“Benjamin De Kraker, a former xAI employee, went viral with a post showing ChatGPT suggesting “shop at Target” while he asked about Windows BitLocker — the post got 463,000+ views and sparked widespread concern about ad creep.​

Sam Altman’s 2024 quote (”ads as a last resort”) is being widely circulated alongside his January 2026 statement, with users calling it a “spectacular reverse-ferret.”

On r/singularity, users debated whether ads in responses would fundamentally undermine trust in AI: “This should undermine people’s confidence in AI. Sadly, I doubt most users will be particularly concerned about it.”

Notably, r/Futurology users had flagged the Fidji Simo hire early, back in May 2025: “Am I the only one who sees ‘OpenAI hired the person who optimized the biggest social network for ad revenue’ and thinks ‘oh no’?”​

On r/ValueInvesting, users questioned the $300B valuation while burning $5B+ annually, calling the economics “extraordinary” and unsustainable without new revenue streams.​

The Trust Problem: Paid Tiers Aren’t Safe

The deepest concern isn’t the ads themselves, its whether they’ll creep into paid tiers.

OpenAI initially claimed these were “app integrations, not ads.” But Chief Research Officer Mark Chen later admitted: “anything that feels like an ad needs to be handled with care, and we fell short.”​

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This story is about Tailwind Labs, the company behind Tailwind CSS, a very popular tool web developers use to style websites quickly. Tailwind was extremely successful: millions of developers used it every month, and its design system became so well-known that AI coding tools learned how to use it perfectly and that success itself became a problem, because once AI tools automatically knew how Tailwind works, many developers stopped visiting Tailwind's website or reading its documentation.

Less traffic = less revenue, since Tailwind Labs made much of its money from products and sponsorships tied to that documentation site; when Google traffic dropped by around 40%, revenue collapsed by about 80%.

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In simple bullet points, -

What exactly happened?

• Tailwind CSS reached massive adoption, with tens of millions of downloads per month.​

• Search traffic to its docs dropped sharply and revenue fell about 80%.​

• Tailwind Labs laid off around 75% of its engineering team.​

Why it happened?

• AI coding tools learned Tailwind’s patterns so well that developers could get Tailwind answers directly from AI instead of visiting the official docs.​

• Tailwind’s business model depended heavily on documentation-driven traffic and related product sales.​

• When that traffic shifted into AI tools, usage stayed high but income collapsed.​

How the situation evolved?

• The founder publicly explained the numbers, the layoffs, and the risk of Tailwind becoming unmaintained.​

• Major companies and many developers that rely on Tailwind stepped in with sponsorships and financial support.​

• Funding moved from indirect (docs/SEO) to direct support from the ecosystem that uses Tailwind.​

I was working on this research this saturday night, and in last 3-4 days few more developments have come to light.

Latest Developments (as of January 13, 2026)

Since the initial transparency push on January 6-7, the sponsorship response has accelerated significantly. Google AI Studio officially joined as a sponsor on January 8, and within 72 hours, 26 new sponsor companies signed on, including 7 at the highest “Partner” tier.​

Adam Wathan has clarified that Tailwind already had over $800,000 in annual sponsorships before the crisis, but rising costs and declining revenue still forced the decision. The company now has “6+ months runway” and is focusing on revenue-generating work rather than community feature requests.​

The fact that Wathan, didn’t quietly hide the problem and instead of polished PR, he publicly explained what happened, shared numbers, and warned that without support Tailwind risked turning into “unmaintained abandonware, the kind of radical transparency which is unusual in tech, helped worked in his favor and triggered a wave of support. the ecosystem where companies often mask financial trouble behind vague messaging or hype. his honest message was simple. He made it clear that the main goal was simply to keep the project properly maintained for the long term.​

the tool is widely used, but the business model broke, and help was needed to keep it alive.​

In effect, the people and companies who benefit from Tailwind decided to pay to keep their own infrastructure health, and Tailwind became a real‑world example of “if this thing is important to you, help fund it so it doesn’t disappear.

Few key lessons this early 2026 crisis has brought in -

• Popularity does not guarantee a stable business if the revenue model is narrow or fragile.​

• Products built mainly on “information value” are easy for AI to commoditize; services, hosting, support, and strong paid add‑ons are more resilient.​

• Depending on a single channel like search traffic is risky when AI becomes the main interface between users and information.​

• Clear, transparent communication can mobilize direct support from users and companies that rely on critical tools.

There is, however, much more that founders can learn from this story. We can turn these lessons into concrete frameworks and playbooks to avoid similar situations in the future. It is not all positive either. the tech and business community remains split on this - many developers are stepping up with financial support, but there are others questioning whether a CSS framework should operate as a multi‑million‑dollar business or instead be maintained more leanly. The conversation has shifted from “will Tailwind survive?” to “should open‑source infrastructure tools even be venture‑scale businesses?”.

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In this article

• The Tailwind story: what happened and why

• How AI changed distribution (search → AI as the front door)

• Why Tailwind’s model broke (docs traffic, single revenue stream)

• What worked: transparency, community, and sponsor response

• Who should care (frameworks, dev tools, OSS infra, SEO‑heavy products)

• Key lessons for founders, operators, and builders (high-level only)

• One framework: an AI‑era revenue risk check (fragile → more resilient)

• The bigger questions (can sponsorships last, should OSS be venture‑scale, etc.)

• Simple next steps for readers (audit risk, talk to dependencies, be transparent)

• Where the detailed playbooks will live

  
  

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How AI has changed developers’ journey.

AI has quietly become the first place many developers go when they get stuck, and that changes the entire journey from question to product.

From search to AI front door

How many of us now go to Claude (or ChatGPT, Cursor, Copilot) to check or fix our code before even thinking about Googling the error? For a growing majority of developers, that’s the default habit, not the exception.

• A large majority of developers now use AI tools as a primary way to get unstuck, often before (or instead of) searching the web. Industry surveys in 2025 report that about 84% of developers are using or planning to use AI tools, and more than half of professionals use them daily.​

• In the same research, roughly two‑thirds of developers (around 68%) say they turn to AI when they get stuck on a problem, making assistants the first stop for many everyday issues.​

• This replaces a lot of the old flow—copy error → Google → Stack Overflow/blog/docs—with “ask Claude/ChatGPT/Copilot, get an answer, keep coding,” meaning fewer automatic clicks to the original content and docs, even though those sources still underpin many answers.

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“The Agentic AI Foundation (AAIF) is a directed fund under the Linux Foundation”

and each co-founder has contributed / donated a foundational project to the foundation -

• Anthropic → Model Context Protocol (MCP)

• Block → Goose (agent framework)

• OpenAI → AGENTS.md (agent instruction format)

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Anthropic’s MCP is an open standard (Model Context Protocol) that lets AI agents securely connect to external tools, data, and systems in a consistent way. OpenAI’s AGENTS.md, is a widely adopted Markdown convention that acts as a “README for AI agents,” giving coding agents project-specific instructions like commands, tests, and code style, Block’s Goose is a local-first, open-source AI agent framework that automates complex developer workflows using LLMs and MCP-connected tools. Bringing these frameworks and protocols together, the new foundation aims to prevent ecosystem fragmentation and ensure agentic AI infrastructure is developed transparently and in the public interest. but above all, this step signifies that the tech industry has reached a pivotal consensus, which is that the future of AI agents belongs to open standards, not proprietary silos.

None of it happened overnight however. Throughout 2025, there’s been storms of changes forcing a reshape of AI landscape. while the foundation models achieved reasoning and tool-use capabilities rivaling human experts, open-source variants also became cost-competitive (100x cheaper than proprietary alternatives), and enterprise customers demanded transparency and control. In this article I plan to understand this strategic realignment and power play between the open-source and proprietaries.

The year 2025 has changed everything

5 AI shifts in 2025

proprietary models are smarter has been the narrative for years, starting with OpenAI’s GPT-4, Google’s Gemini, and Anthropic’s Claude, all these models have dominated benchmarks. But its end of 2025 and the benchmarks are dead. I’d go in to the details of that in another post, but if we have to look quickly why this narrative can’t work anymore, all we have to see is last few open source releases. -

Meta’s Llama 4 (April 2025) introduced native multimodality.

DeepSeek’s V3.2 (November 2025) - Sparse Attention, a novel architecture that reduced computational overhead for long-context tasks by 50% while maintaining quality.

Mistral’s Large 3 (December 2025) - The 675B-parameter MoE model matched GPT-4-class performance with a 256,000-token context window and exceptional multilingual capabilities, which is crucial for global enterprises wary of US or Chinese vendor lock-in.

The implication has been profound: intelligence was becoming a commodity. When your competitive advantage can’t be proprietary models, it shifts to something else entirely: standards, integration, and orchestration.

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The Enterprise Demand Crisis

Enterprises wanted AI that was simultaneously powerful AND auditable, but did they get it in 2025?

By Mid 2025 we saw a number of major lawsuits over training data, regulatory scrutiny from the SEC and EU, and high-profile AI failures (hallucinations causing financial losses, agents taking unintended actions) creating not only a “trust crisis” that gripped enterprise AI adoption but also an impossible demand and the Closed-model APIs couldn’t satisfy.

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The AI revolution is reshaping how businesses operate today and for founders and CEOs, navigating this complex landscape of AI adoption, AI as a Service (AIaaS) has emerged as the most pragmatic path forward. The global AIaaS market is projected to explode from $21.48 billion in 2025 to $175.99 billion by 2032, representing a compound annual growth rate of 35.1%. For startups and SMEs, this democratization of AI technology means competitive parity with industry giants without the prohibitive investment in data science teams or GPU infrastructure. AIaaS enables businesses to access enterprise-grade AI capabilities through cloud-based platforms at a fraction of traditional costs, with deployment timelines measured in weeks rather than months. For startups it means being able to compete with larger players by leveraging subscription-based or modular, plug-and-play AI tools.

In this blog I explore market intelligence, implementation strategies, and strategic opportunities specifically relevant to technology leaders and deep-tech founders of lean startups like me, looking to leverage AI without the cost and burden of building infrastructure from scratch.

Note - to keep the focus on the current deployment scenerios, I am skipping two important points, one is on-device AI and chip development, and other is the use cases of small language models.

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Understanding AI as a Service: The Core Paradigm

AI as a Service delivers AI capabilities including machine learning, natural language processing (NLP), computer vision, and predictive analytics through cloud-based platforms accessible via APIs and web interfaces. which means rather than investing $250,000 to $1,000,000 in building custom AI models, businesses can leverage pre-trained algorithms and scalable infrastructure on a subscription or pay-as-you-go basis.​

The AIaaS model transforms AI from a capital expenditure into an operational expense, fundamentally altering the economics of innovation. Organizations now deploy AI solutions in 2-4 weeks compared to 6-12 months for traditional custom development. This acceleration enables rapid prototyping, faster time-to-market, and the agility to pivot based on real-world performance data.

Market Landscape and Growth Trajectory

The AIaaS market demonstrates explosive growth across all forecasting models. According to Fortune Business Insights, the market will grow from $21.48 billion in 2025 to $175.99 billion by 2032. GM Insights reports the 2024 market valued at $12.7 billion with a projected CAGR of 30.6% through 2034. These projections reflect that 78% of organizations are now using AI in at least one business function up from 55% just one year earlier.​

Regional dynamics reveal North America dominating with 38.7% market share in 2024, driven by advanced cloud infrastructure and significant R&D investment. However, the Asia-Pacific (APAC) region is expected to grow at the highest CAGR of 19.8% through 2034, fueled by rapid digitalization and expanding cloud infrastructure. Private investment patterns also underscore this momentum. In U.S. organizations invested $109.1 billion in AI during 2024 nearly 12 times China’s $9.3 billion.

Industry Adoption Patterns and Use Cases

AIaaS adoption varies dramatically across sectors, reflecting different maturity levels, regulatory environments, and use case readiness.

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The AI landscape witnessed an unprecedented developments in August 2025, marking what many experts are calling a pivotal moment in AI evolution. From groundbreaking model releases to significant market shifts and concerning cybersecurity threats, the AI sector and with it us all went through a transformation of sort that has somewhat reshaped how we understand both the potential and perils of new technology and specially AI. And so here I am, sipping my Sunday tea and wondering if with all the developments this month, this August may just have been beginning of time when AI finally started acting like a responsible adult instead of a caffeinated college freshman with access to the entire internet? or may be that line works better on its creators and users. But anyhow, It's been quite the journey watching our digital offspring mature from "Can you believe ChatGPT can write poems?" to "Oh great, now it's helping criminals build ransomware."

So before I go about the the stability and maturity part, lets take a quick look at what made the headlines this month, and why they matter so much. both good news and the oops!

August 2025 headlines -

• Microsoft's Divorce Papers: Finally ditched their OpenAI dependency and launched MAI-Voice-1. It's like watching your friend finally break up with their toxic ex and start making their own decisions but on a serious note, the announcement of in-house MAI-1 models marked a turning point, shaking up industry alliances and setting new efficiency records. this news somewhat also created a shift towards powering conversation on autonomy and infrastructure and giants actually owning their AI.

• GPT-5’s Perfect Score: GPT-5’s flawless 100% on high-stakes math competitions sent shockwaves through the tech world, raising the bar for all future AI systems. the whole 5 PhD in your pocket reasoning sparked intense debates about AI’s limits (or lack thereof) and spurred a wave of “thinking mode” innovation. but with sky-high expectations, it dint take long for organizations to realize they have underestimated the complexity of integrating AI into real business workflows—and overestimated how quickly hype could translate into measurable value. and by the end of the month most found themselves facing the tough reality that even the smartest AI can’t deliver magic on demand. but here’s a good thing at last with Enterprise Adoption Hitting 45% - Companies are no longer treating AI like that weird intern nobody knows how to manage. I think thats a good thing.

• Agentic AI is Everywhere: I have so much to say about this, I think I’ll better write a full post on this, after? about these so called digital assistants, along with that they are actually. so Lets just stick to what market is raving about how they handle 80% of customer service issues without a human having a nervous breakdown, for now.

• 95% of Companies Still Waiting for ROI: The MIT report or went viral even before anyone could read the actual report. well power of hype? or AI? or social media for that matter. But Lets just say all thanks to Fortune, the news that 95% of organizations aren’t seeing a return on AI investments fueled a reality check, gave investors and execs a hefty pause or was it a heart attack? and as a result -

• Stock Market Had Trust Issues: Turns out throwing money at AI doesn't automatically print more money. Who could have predicted this shocking development? but despite doubts, big tech’s $320B infrastructure spend (up 40% from last year) underscored faith in AI’s future. and what this means is even if ROI remains elusive, the market has cemented AI’s place as an essential in business infrastructure.

• Claude Turned Criminal: Well, a note to self: even the nice AIs can have a dark side. So the news is that Anthropic’s Claude was used in sophisticated cyberattack and North Korean operatives gaming Fortune 500 hiring with AI. This news raised urgent concerns about tech misuse, prompting new calls for security and regulation. PS Claude may be redeeming on the chrome plug-in front, where perplexity comet failed. but lets cover it in another post.

• Video Generation Went Mainstream: Google's Veo 3 created 70 million videos. That's a lot of cat content, no seriously but with 70+ million AI videos generated and creative tools integrated across platforms, inspiring both businesses and artists, Google’s Veo 3 has redefined what’s possible for creators. and we' haven’t even spoken about storybooks, notebookLM, and uh nano banana yet.

• The Nano Banana Frenzy: and finally, a nano banana meme storm swept through feeds (and a few AI models), providing a fun and zesty counterpoint to all the above August’s serious news. a mystery, perfectly orchestrated by Google to let the AI community discover and test the model organically through LMArena, built genuine excitement and speculation before the big reveal with Sundar Pichai's three banana emojis tweet on August 26, 2025. Well, with most in my circuit going bananas I would say they kinda succeeded that rarely happens with google marketing team though! they should do it often and so should we.

Well cheers to the month that dared us to dream, fail, and laugh in equal measure, and making us believe that AI is no longer a future possibility and understand both both the potential and perils of it as we head boldly into September and the next 12 -18 months the reality of it with immediate implications on both businesses and society take center stage. and therefore challenge now lies in harnessing AI's benefits while effectively managing its risks, ensuring that this powerful technology serves humanity's best interests while maintaining appropriate safeguards against misuse.

"The future is already here — it's just unevenly distributed and occasionally trying to hold your data for ransom." - William Gibson (probably didn't say this, but he should have)

So what do we have ahead of us?

My personal opinion is that we should see not just maturity, but likely will reach some stability by this time next year. but lets look in to some patterns and data points emerging form all this august’s Chaos as well and see what may be in for next.

The Security Awakening

And It should come first right. The Claude cybercrime fiasco is going to trigger what I'm calling "The Great AI Security Panic of Fall 2025." Every company will suddenly realize they need AI governance policies, and consulting firms will make a fortune selling "AI Ethics Frameworks" (which is just fancy language for "try not to let your AI become a criminal").

Prediction Bullets:

• At least three major tech companies will announce "AI Safety Officers"

• New regulations will require AI systems to have digital "ID cards"

• Insurance companies will start offering "AI Misbehavior" policies

• Someone will inevitably create an "AI Therapy" service for corrupted models

The Infrastructure Reality Check

Remember those $320 billion infrastructure investments? They're about to hit some practical limitations. Not because the money ran out, but because we're running out of people who know how to manage these massive AI data centers without accidentally melting half the power grid.

so here’s what I would watch out for -

• Google and Microsoft announcing "AI efficiency partnerships" (translation: we need to share resources before we bankrupt ourselves)

• A surge in "AI Infrastructure Engineering" job postings with salaries that make your eyes water

• The first major AI data center brownout may be? (probably in Texas, because of course)

The Domestication Accelerates

Looking at my "AI Wildness Chart," ( yes, I have one!) I predict we'll see most applications move down the scale by year-end. The wild west phase is ending, and we're entering what I like to call the "suburban phase" of AI development.

The Taming Process may look something like this:

• Customer Service Bots will become so good, you'll forget you're talking to AI (wildness level dropping to 1)

• Healthcare AI will get FDA approvals for routine diagnostics (staying stable at 3)

• Autonomous Trading will have mandatory "sanity checks" after some inevitable market mishaps (dropping from 8 to 6)

• AI-Powered Cybercrime will face sophisticated countermeasures (hopefully dropping from 9 to 7)

So why do I think August 2026 may just be our "Stability Sweet Spot"

No I dont think because innovation stops, but because we might finally have figured out how to make AI boring in the best possible way. no hear my out. -

1. The Infrastructure Will Finally Match Demand: All those billions being thrown at data centers will pay off, and we'll have enough computing power to run AI without rationing

2. Security Will Become Routine: The cybercrime wake-up call will result in standardized security protocols that actually work

3. ROI Will Start Making Sense: Companies will stop expecting magic and start seeing predictable, measurable returns on AI investments

4. Regulation Will Find Its Groove: Governments will have figured out how to regulate AI without accidentally killing innovation

The Sunday Morning Bottom Line

Its almost afternoon, but I have reached the bottomline yes, and as I finish this article and get to another deadline which is midnight tonight, and While I not have a full plan for my year ahead with multiple unknown to process let me conclude that I am I'm oddly optimistic about where we're heading, in both cases. trust me, I can’t wait to stop talking about AI adoption and instead actual integration and operational process and how will optimize everything with quantum comping. for now Lets spend this September in Nano bananas’s glory while it lasts, and wait for AI to become as mundane as GPS in the very near future. I am actually yet to try it, Likely later in the week ahead so perhaps we’ll discuss how nano bananas gonna officially make us ultra productive.

I hope you found this article interesting, informative, and useful. Do support by liking, subscribe, share it with you colleagues and friends and on social media — X, LinkedIn, or the platform of your choice. You can follow me on linkedin here.

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Further reading :

• Who’s Profiting Most from AI Agents? Inside CB Insights’ Top 20 Startup Ranking

• So What's so special about GPT-5?

• Bandwidth & Broken Hearts: How Telcos Became the Unrequited Lovers of the Digital Age

• Wiring the Future: AI, Chips, and the Race to Reinvent the Internet

• AI’s Gold Rush 2.0: Unpacking the Psychology, Power, and Paradoxes Shaping Billion-Dollar Bets

• Why Tech Journalism Is Now the Most Powerful Beat in the Newsroom

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase

This morning I also noticed Fortune’s piece on Mira Murati’s $2B seed round pop up on LinkedIn. Interestingly, it’s already a week old and which means it didn’t gain the same kind of viral traction — even with the “largest ever by a female founder” twist. Of course not every headline is breaking through the noise, though Fortune is clearly pushing hard to keep AI at the center of the conversation, and here is why!

💡 lets’ look at the streak of headlines from August 2025, all circling the shift from AI hype → skepticism:

• 📰 “MIT report: 95% of generative AI pilots at companies are failing” (Aug 17, 2025)

• 📉 “U.S. tech stocks slide after Altman warns of ‘bubble’ in AI and MIT study doubts the hype” (Aug 19, 2025)

• 😬 “AI anxiety has sent markets into a tizzy, but experts say the jitters will only ‘punish those chasing the froth’” (Aug 19, 2025)

• 💻 “The ‘shadow AI economy’ is booming: Workers at 90% of companies are using personal AI tools” (Aug 18, 2025)

• 🗣️ “What CEOs are saying about the alleged AI bubble” (Aug 20, 2025)

• ⚖️ “Just don’t call it an AI bubble” (Aug 20, 2025)

• 💵 “Wall Street isn’t worried about an AI bubble. Sam Altman is” (Aug 19, 2025)

And remember: back in Dec 2024 they already ran “AI leaders provide a reality check about a technology they say is both ‘exciting’ and still a very ‘manual exercise’.” The “reality check” narrative isn’t new — but this August pile-on is impossible to ignore.

🔎 The AI Hype Reality Check: Multiple Stories Signal a Major Shift

But is it just Fortune?. August 2025 feels like the first real AI reality check since ChatGPT took off three years ago: Over the past few weeks there’s been a wave of stories that together are shaping this story — even sparking bubble warnings. A quick recap of the flashpoints:

• ❌ The GPT-5 Disaster (Aug 7): Marketed as a PhD-level breakthrough, GPT-5 landed with a thud — described by early users as “cold,” “mechanical,” and prone to bizarre mistakes (like claiming “blueberry” has three b’s). The backlash was so strong that over 3,000 users petitioned to bring back GPT-4o. OpenAI caved, and Sam Altman issued a rare public apology, admitting they “messed up.”

• 💬 Bubble talk - Sam Altman’s Bubble Warning: At a private dinner with reporters, Altman himself admitted AI is in bubble territory — comparing it to the dot-com era and warning “someone’s going to get burnt” on overvalued AI startups.

• 📉 Market jitters: The stock market noticed too. Nvidia, Palantir, and even SoftBank all tumbled. Nvidia shed 3.5% despite its $4T milestone. Palantir dropped nearly 10%. Nasdaq fell 1.2%. International AI stocks like SK Hynix (–2.9%) and SoftBank (–7%) also stumbled.

• 😒 AI fatigue: Consumer pushback is rising. Surveys show nearly half of Gen Z and Boomers now oppose AI ads. Companies like Duolingo and several gaming studios drew heavy criticism for AI-first strategies or AI-generated content. Social media sentiment has turned increasingly hostile, with people trolling AI outputs instead of celebrating them.

• 💬 The “AI Winter” (or Autumn?) Debate: Analysts disagreed on whether this signals a looming “AI winter” or simply an “AI autumn” — a necessary correction instead of collapse. The utility is there, but the exuberance is fading.

• 📊 Corporate Spending Worries: Meta’s abrupt AI hiring freeze (after dangling $100M sign-on bonuses) and its restructuring of the AI unit into four teams sent another signal: even giants are tightening belts.

As CNN Business put it, August has delivered a “vibe shift” around AI. With GPT-5’s flop, the MIT 95% failure study, Altman’s bubble admission, Meta’s reorg, and market selloffs all converging, this feels like the first serious reality check for AI since the ChatGPT boom began in late 2022.

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The mood has flipped from inevitability 🚀 to caution ⚠️.

👉 In other words, it’s not the end of AI — but the narrative has shifted. We’ve moved from unchecked hype to skeptical evaluation, and that transition is now playing out in headlines, boardrooms, markets, and consumer sentiment all at once.

📊 Evidence of Fortune’s AI Hype Strategy

Here’s another twist though: Fortune isn’t just covering AI — they’re monetizing both sides of the cycle.

• 🤝 AI Partnerships: They’ve teamed up with Accenture to launch Fortune Analytics, branding themselves as AI innovators.

• ✂️ AI Layoffs: Yet in July they cut 10% of staff, citing “the rise of AI” and declining traffic — while publishing stories about AI’s disruption of jobs.

• 🌀 Content Pivot: Their headlines span the whole spectrum — hype, backlash, and reality checks — capturing clicks from every camp.

Now this, follows the classic media playbook: hype → backlash → analysis. And the result? Engagement from all sides.

Which is the real point: Fortune’s AI coverage isn’t about a consistent stance — it’s about winning the attention game. From the boom to the backlash, they’re having their 🍰 and eating it too.

❓So Where Does This Go Next?

Here’s what I keep wondering:

• ⏳ How long will this hype/backlash cycle keep spinning?

• 🧠 Is the MIT “95% failing” story the moment people actually wake up to AI’s realities, or just another spike in the cycle?

• 🚀 Does the next “AI breakthrough” reignite hype just as fast as this bubble talk cooled it down?

Because if Fortune’s coverage proves anything… it’s that the hype cycle itself has become the story.

💭 What do you think? Are we finally moving beyond the hype, or just stuck in another loop of exaggerated peaks and panic dips?

In the next post we’ll cover the MIT report and try to find the answer to some of the burning questions .»

If the AI revolution really living up to its promise, or are most companies stuck at the starting line? With 95% of generative AI pilots reportedly failing, are businesses missing something critical in their rush to innovate? Could the billions invested have overlooked the true hurdles to impact? What will it actually take for AI to deliver on the hype?

Further reading :

• Who’s Profiting Most from AI Agents? Inside CB Insights’ Top 20 Startup Ranking

• So What's so special about GPT-5?

• Bandwidth & Broken Hearts: How Telcos Became the Unrequited Lovers of the Digital Age

• Wiring the Future: AI, Chips, and the Race to Reinvent the Internet

• AI’s Gold Rush 2.0: Unpacking the Psychology, Power, and Paradoxes Shaping Billion-Dollar Bets

• Why Tech Journalism Is Now the Most Powerful Beat in the Newsroom

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase

The AI landscape landscape has undergone a seismic transformation, with AI agent startups emerging as the new powerhouse sector commanding unprecedented valuations and revolutionary revenue growth. According to CB Insights' latest revenue ranking, these top 20 AI agent startups collectively generated $1.28 billion in annual revenue, marking a historic milestone that validates the commercial viability of autonomous AI systems. While traditionally these milestones took decades to reach, these barely old companies are making 100s of millions in annual reviews, with Anysphere's Cursor leading the pack with $500 million, representing nearly 40% of the total market share among the top 20 players in various sectors such as enterprise workflows, software development, customer service, and legal applications. So It seems that AI companies are strategically pausing to rebuild their foundation before continuing after slamming at full speed and crossing $100M mark.

The collective $1.28 billion revenue achievement and exponential growth also validates both market demand and technological capability and investor confidence. Each headline leverages psychological hooks like mystery, shocking numbers, impossible speed, and counterintuitive insights to drive curiosity and engagement.

Here are some of the most remarkable—and surprising—stories revealing how new players and unseen strategies are transforming entire industries at record speed:

1. A 3-Year-Old Company Hits $500M Revenue (And You've Never Heard of It)
   Anysphere skyrockets to half a billion in just three years—outpacing tech giants and revolutionizing productivity instead of replacing jobs.

2. Investors Are Paying 225x Revenue for These 'Invisible' AI Companies
   AI agent startups like Sierra rewrite the rules of valuation, trading at 225x multiples and outshining even Apple in investor confidence.

3. A Company Founded in Someone's Dorm Makes $4.5 Million Per Employee
   Mercor’s ultra-lean HR model stuns the industry by yielding more revenue per employee than even the world’s largest tech and finance firms.

4. $740M in Revenue, Zero Human Programmers, AI coding agents like Anysphere fuel a software boom where autonomous code-writing eclipses anything seen in Silicon Valley.

5. A wave of new AI startups surges to the top, redefining entrepreneurial speed and market disruption. interesting fact— 40% of These Billion-Dollar Companies Didn't Exist 2 Years Ago

6. The $100M Mystery: 4 of these distinct AI Companies All Hit the Same Revenue Number revealing a universal law in AI business growth.

7. A Legal AI Makes $75M Helping Lawyers Do What They Hate Most
   Harvey AI capitalizes on lawyers' pain points, turning tedious legal work into a $75 million business serving hundreds of elite firms.

The era of AI agents as a fringe experiment is over— they are now a core force driving business transformation. The AI agent sector is maturing at record speed, with its leaders showcasing how quickly startups are capitalizing on generative AI’s potential and Industry pioneers like Anysphere, Harvey, and Glean pulling ahead with expertise and strong customer loyalty, yet the market faces growing pains including profitability, talent shortages, and potential consolidation. As AI agents become integral to enterprise tech and the market heads toward $13 billion by year’s end, those who deliver sustainable value through disciplined economics, technical strength, and meaningful differentiation may just be the win this battle.

Here is the full breakdown.

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Company Formation Timeline and Growth Velocity

The temporal analysis of company formation reveals 2023 as the peak founding year, with six companies (30% of the top 20) established during this period. This surge corresponds directly with the ChatGPT launch and subsequent generative AI adoption wave, demonstrating how quickly entrepreneurs capitalized on emerging market opportunities.

This acceleration timeline represents a fundamental shift in startup development cycles. Traditional software companies typically required 5-7 years to reach $100 million in annual recurring revenue, while leading AI agent startups achieve this milestone in 12-24 months. Cursor exemplifies this phenomenon, increasing its annual recurring revenue from $1 million to $500 million in just over a year, making it the fastest-growing SaaS company in history

Valuation Metrics and Revenue Multiples

The valuation landscape for AI agent startups defies traditional software-as-a-service benchmarks, with companies commanding revenue multiples averaging 52.5x compared to typical SaaS multiples of 7-10x. The median revenue multiple stands at 22.5x, while outliers like Sierra achieve extraordinary 225x multiples, reflecting investor confidence in exponential growth potential.

Market Dominance and Sector Distribution

With software development tools capturing the largest share of both company count and revenue generation, the AI agent startup ecosystem exhibits clear market concentration patterns.

The sector analysis reveals that four companies in software development collectively generate $740 million annually, representing 58% of the total market value among the top 20 startups. The geographic distribution remains heavily concentrated in North America, with the United States capturing approximately two-thirds of global AI agent venture capital investment in 2025. UK AI startups secured $2.4 billion in venture capital funding in the first half of 2025, representing 30% of all UK VC activity and demonstrating the global nature of this market expansion.

Enterprise workflow solutions represent the second-largest category with four companies generating $142 million combined, followed by customer service applications with three companies producing $121 million in annual revenue.

Industry Applications and Use Cases

Software development dominates the AI agent landscape with $740M in revenue, representing 58% of the total market, while enterprise workflow, customer service, and other applications create a diversified ecosystem with varying revenue potentials.

Software development applications represent the most mature AI agent market, with companies like Cursor, Replit, StackBlitz, and Lovable achieving significant revenue milestones through code generation, debugging, and full-stack development automation. The coding AI agents market has shown particularly impressive momentum, with companies achieving unicorn status in as little as six months—four times faster than the AI industry average.

Enterprise workflow automation encompasses broad applications including document processing, knowledge management, and business intelligence. Companies like Glean, Sana, and Hebbia provide AI-powered search, analysis, and content generation capabilities that integrate with existing enterprise systems while maintaining security and compliance requirements.

Customer service AI agents are transforming support operations through autonomous conversation management, ticket resolution, and customer interaction handling. Sierra, Crescendo, and Decagon demonstrate how AI agents can manage complex customer relationships while reducing operational costs and improving satisfaction scores.

Legal technology applications show significant adoption among elite law firms and corporate legal departments, with Harvey leading the sector through specialized modules for contract analysis, legal research, and compliance monitoring. The legal services market represents a $300 billion opportunity in the United States alone, with analysts estimating 44% of legal tasks could eventually be automated.

Market Leaders and Strategic Positioning

• Anysphere (Cursor) with users generating nearly one billion lines of working code daily, demonstrates the market demand for AI-native development tools

• Glean’s Work AI platform connects enterprise data sources and enables intelligent search and automation. The company's 40% weekday daily/monthly active user ratio significantly exceeds the typical 10-20% seen in enterprise SaaS applications, indicating strong user engagement and product-market fit.

• Harvey AI serving 337 legal clients including Fortune 500 corporations and elite law firms, demonstrates the legal industry's readiness to adopt AI-powered workflows for document analysis, contract drafting, and legal research.

• Sierra Co-founded by former Salesforce CEO Bret Taylor and Google executive Clay Bavor, Sierra enables companies to deploy AI agents that handle complex customer interactions across multiple channels while maintaining brand consistency and compliance standards.

Revenue Efficiency and Operational Metrics

The revenue per employee analysis reveals extraordinary operational efficiency among top-performing AI agent startups, with significant variations reflecting different business models and operational approaches. Mercor leads with $4.55 million revenue per employee, followed by Anysphere at $3.21 million and Lovable at $2.22 million, demonstrating the scalability potential inherent in AI-powered solutions.

These efficiency metrics substantially exceed traditional software companies, where Microsoft generates $1.8 million per employee and typical enterprise software companies achieve $200,000-$500,000 per employee. The exceptional performance of AI agent startups reflects their ability to leverage automation and artificial intelligence to scale revenue without proportional increases in human resources. The median revenue per employee across the top 20 companies stands at $145,000.

Investment Risk Factors and Market Sustainability

Despite remarkable growth metrics, the AI agent startup sector faces significant structural challenges including high compute costs, talent scarcity, and uncertain unit economics. Many companies operate with challenging cost structures that scale with usage, creating potential profitability pressures as they mature.

Revenue multiple sustainability remains a critical concern, with some companies trading at 200x revenue despite traditional SaaS companies averaging 7-10x multiples. This valuation gap reflects investor expectations of exponential growth rather than current financial performance, creating potential downside risk if growth trajectories normalize. also the concentration of venture capital among elite firms has created funding accessibility challenges for early-stage companies The recent acquisitions of Windsurf by OpenAI and other talent-focused transactions suggest that independent AI agent startups may face strategic acquisition pressures rather than long-term independence.

Investment Trends and Market Projections

The enterprise AI agents and copilots market has experienced explosive growth, expanding from $5 billion in 2024 to a projected $13 billion by the end of 2025, representing a 150% year-over-year growth rate. This expansion reflects both increasing adoption rates and the maturation of AI agent technologies across enterprise applications.

Venture capital investment in AI reached record levels in the first half of 2025, with $49.2 billion globally compared to $44.2 billion for all of 2024. The concentration of capital among mega-deals has created unprecedented funding availability for proven AI agent startups, while smaller companies face increased competition for early-stage investment.

Three additional markets are expected to reach $1 billion revenue milestones by end of 2025:

1. recruiting AI agents,

2. sales AI agents, and

3. customer service AI agents.

Future Outlook

The meteoric rise of AI agent startups marks a pivotal shift in the business and technology landscape, transforming once-theoretical possibilities into billion-dollar realities at unprecedented speed with AI agents startup now leading the charge, redefining productivity, customer experience, and even the nature of work itself. At the same time the flood of new entrants since 2023 highlights both how quickly fortunes can be made—and how quickly competition ramps up.

Looking ahead, AI agents are set to become a fixture in enterprise infrastructure, with market size projected to reach $13 billion by the end of 2025 and ever-increasing relevance moving forward. The transition from growth-at-any-cost to sustainable value will define the next wave, favoring those who blend relentless innovation with operational rigor and strong moats.

Key Lessons for Founders

1. Focus on Sustainability as Early as Growth: Hyper-growth is exhilarating, but long-term success depends on mastering unit economics, customer retention, and defensible differentiation.

2. Prepare for the $100M Plateau: Many AI startups hit a ‘speed trap’ at $100M revenue. Anticipate operational scaling, profitability pressures, and the need to prove your business model works at scale.

3. Embrace Rapid Learning and Adaptation: The rules for AI are being rewritten in real time. Founders must constantly learn, iterate, and adapt to shifting market, technical, and talent realities.

4. Build Real Moats: Domain expertise, technical defensibility, and superior customer experience are the foundations for surviving market consolidation and emerging as a true leader.

5. Don’t Chase Growth Blindly: In the new market phase, sustainable value and judicious execution trump blitzscaling. The companies that endure will be those that balance strategic ambition with operational discipline.

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Further reading :

• So What's so special about GPT-5?

• Bandwidth & Broken Hearts: How Telcos Became the Unrequited Lovers of the Digital Age

• Wiring the Future: AI, Chips, and the Race to Reinvent the Internet

• AI’s Gold Rush 2.0: Unpacking the Psychology, Power, and Paradoxes Shaping Billion-Dollar Bets

• Why Tech Journalism Is Now the Most Powerful Beat in the Newsroom

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase

So OpenAI’s Most Capable Model GPT-5 Just Rolled Out and with all the buzz in the community, I quickly went to live and before I could focus, I hear the words - “ couple of PhDs in your pocket” and that was good enough for my founder brain to transport in to all those project plans I have been writing with PhD Research Fellows funding requests. So can the funding body now take GPT-5 subscription as a valid plan or should I make a bunch of agents and name them and add them in to the project as resources. anyways, before I could go back looking for the whats’ so special about it, my good old perplexity added GPT-5 on both pro and comet browser, so I thought why bother, lets just quickly ask? and so -

I asked GPT-5 Whats so special about GPT-5 and here is what he thinks.

1. The World’s First "Unified Intelligence" System

Well according to GPT-5, It isn't just another model - it's the first AI that thinks like a human expert, perhaps the GPT-5's most revolutionary feature. Which means that unlike previous models where one had to manually choose between different versions, GPT-5's smart router automatically decides whether to use fast responses or deep reasoning based on your query complexity. So It's like having an AI that knows when to think quickly versus when to think deeply—just like humans do. I guess no new product is coming up with a deep prefix anymore eh? but well couple of examples here:

• When you ask "What's the weather?" → Fast response mode

• When you say "Think hard about this complex problem" → Deep reasoning mode automatically engages

• No more confusing model selection - it handles everything seamlessly

2. PhD-Level Performance That Actually Works

Thats the one that gave me the kick yup. - GPT-5 achieving genuine expert-level capability across domains:

• 94.6% on AIME 2025 math problems (vs 71% without reasoning)

• 89.4% on PhD-level science questions (GPQA Diamond)

• 42% on "Humanity's Last Exam" - nearly doubling previous model performance

• 74.9% success rate fixing real GitHub issues (SWE-bench Verified)

What's remarkable is this isn't just benchmark performance - early testers report it genuinely feels like talking to a PhD expert in any field.

3. Revolutionary "Safe Completions" Instead of Refusals

This is perhaps the biggest breakthrough. Previous models would simply refuse sensitive requests. GPT-5 introduces "Safe Completions" Could this be the most game-changing improvements? For example, instead of refusing to discuss cybersecurity, it might provide high-level security concepts while avoiding specific exploit details.

• Provides helpful information within safety boundaries instead of blanket refusals

• Explains its reasoning rather than just saying "I can't do that" or "I cannot help with that"

• Reduces over-refusals by 50% while maintaining safety

• Better handles dual-use questions (like biology or cybersecurity topics)

What This Means in Practice

Dramatically Fewer Hallucinations:

• 80% fewer factual errors than previous reasoning models

• 6x fewer hallucinations on complex factual questions

• Only 1.6% error rate on medical questions (vs 15.8% for GPT-4o)

Genuine Coding Partnership:

• Creates complete applications from single prompts with aesthetic sensibility

• Understands spacing, typography, design principles

• Can debug large codebases more effectively than human programmers

More Human-Like Interaction:

• Less sycophantic and artificially agreeable

• Fewer unnecessary emojis and AI-like phrases

• More honest about limitations - says "I don't know" when appropriate

GPT-5 Feature Matrix: What's New and Revolutionary

This matrix shows just how comprehensive GPT-5's improvements are. Nearly every major capability that people wished previous AI models had is now available in GPT-5—and many of these features are available to free users for the first time. Well, that could be a game changer for sure.

Breakthrough Performance Across All Domains

I also looked at the some performance benchmark numbers. so GPT-5 achieves massive performance gains across every major benchmark, with some improvements being absolutely stunning—like jumping from 22% to 42% on "Humanity's Last Exam" and achieving near-perfect 98.4% medical accuracy.

GPT-5 lineup:

• GPT-5 — $1.25/M input, $10/M output

  • Flagship model with premium reasoning and generation quality, ideal for complex coding, UI builds, and long tool-use chains.

• GPT-5 mini — $0.25/M input, $2.00/M output

  • Lighter, faster variant offering the same context window at lower cost, great for rapid prototyping and high-throughput workloads.

• GPT-5 nano — $0.05/M input, $0.40/M output

  • Ultra‑efficient model optimized for massive-scale inference and edge deployment where latency and cost are critical.
    

What does this means for users?

• Clear costs per task: $1.25/M input and $10/M output for GPT-5, with cheaper Mini and Nano tiers for budget control.

• Right tool for the job: GPT-5 for complex coding and long tool-use chains; Mini for rapid iteration; Nano for massive scale or edge.

• Practical savings: Most spend is output—use concise prompts or minimal reasoning to cut costs without hurting simple tasks.

• Billing mindset: 1M tokens ≈ up to ~1M words total; heavier workflows skew costs toward output—optimize responses accordingly.

The Game-Changer: It's Free for Everyone

So unlike previous reasoning models that required expensive subscriptions, GPT-5's reasoning capabilities are available to all users - including the free tier. This democratizes access to PhD-level AI for the first time. So GPT-5 may not be just an incremental upgrade—but a revolutionary leap that fundamentally changes how AI and we work.

But hold on, I also wanted to see if it can give me some data on What it could change in my sector. I mean help me with…

and here are some numbers it gave me.

Network Protocol Understanding:

• GPT-5 scores 96.7% on τ²-bench telecom - a tool-calling benchmark specifically designed for telecom environments

• Can interpret 3GPP standards, protocol specifications, and network configurations with human-expert accuracy

Code Generation for Telecom:

• 74.9% success rate on real-world GitHub issues (SWE-bench Verified)

• Generate complete network functions, protocol implementations, and orchestration code

• Debug complex multi-vendor network configurations

Multi-Domain Reasoning:

• Understand relationships between radio access networks, core networks, transport, and edge computing

• Correlate network KPIs across different domains and vendors

• Predict cascading effects of network changes

GPT-5 enables the creation of truly intelligent networks that can think, reason, and act autonomously. Unlike previous AI models that required extensive fine-tuning for telecom applications, GPT-5's unified reasoning system can understand complex network topologies, protocols, and performance metrics out of the box.

What this means for me:

• Network slicing becomes intelligent: GPT-5 can dynamically optimize 5G/6G slices based on real-time demand, SLA requirements, and business objectives

• Edge computing gets smarter: Deploy GPT-5 inference at network edges to reduce latency for AI applications by up to 90%

• Autonomous operations: Networks can self-heal, self-optimize, and predict failures before they occur

Well that makes me tad more excited indeed, and I am now actually looking forward to trying the GPT-5 out with all its glory! what about you?

With so much activity today, and the number of LIVEs specially on Youtube, and publications already, there’s gonna be even more of early reviews coming in tomorrow and this weekend, so bracing up for that. also some more detailed videos / reports and guides from OpenAI hopefully and the use cases from different sectors! considering the integration with different tools went pretty fast and almost at the same time. some busy times ahead if it wasn’t already, well.

(EDIT) : One step closer to AGI.

PS Since I din’t see any mention of AGI except on linkedin I just went ahead and asked about AGI too ( so updating the article here)

What Makes GPT-5 "AGI-Adjacent" : AGI-Like Reasoning:

• GPT-5 can follow multi-step instructions, coordinate tools, and adapt to changing contexts

• It demonstrates agentic capabilities—completing end-to-end tasks rather than just answering questions

• The model shows honest self-assessment—knowing when it can't complete a task (a key AGI requirement)

Why Experts Are Cautiously Optimistic

Sam Altman's Own Assessment: is that model is generally intelligent and a substantial step toward something very AGI-like'" However, he admits GPT-5 still lacks continuous learning from new experiences

The Missing Pieces:

• Persistent memory: Can't learn from interactions over time

• Autonomy: Still requires human prompting for tasks

• Adaptability: Can't improve itself without retraining

I hope you found this article interesting, informative, and useful. Do support by liking, subscribe, share it with you colleagues and friends and on social media — X, LinkedIn, or the platform of your choice. You can follow me on linkedin here.

Share @poonamparihar

Further reading :

• Bandwidth & Broken Hearts: How Telcos Became the Unrequited Lovers of the Digital Age

• Wiring the Future: AI, Chips, and the Race to Reinvent the Internet

• AI’s Gold Rush 2.0: Unpacking the Psychology, Power, and Paradoxes Shaping Billion-Dollar Bets

• Why Tech Journalism Is Now the Most Powerful Beat in the Newsroom

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase

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In a recent LinkedIn conversation with Adarsh Lathika, as we reflected on the momentum driving the AI wave: enterprises chasing trends without clear ROI, startups leaning heavily on hype, and investors doubling down on bold, often unproven bets, I couldn’t help, but notice: the kicker—from my front-row seat as a founder, it sometimes feels like we’re all in a game whose rules keep changing.

Everywhere you look, companies are racing to adopt AI—not necessarily because it drives value, but because no one wants to be left behind. Enterprises feel the pressure to “do something AI,” regardless of ROI clarity. Startups ride the hype wave, often using buzzwords like “AI-everywhere” to stay relevant. And behind much of this momentum, venture capitalists are pouring billions into the space, betting big on trends and hope over proven outcomes.

So while it’s tempting to see this as just another innovation boom, the cracks are showing. Is this momentum driven more by pressure than purpose? Could the biggest players fueling the AI fire end up burned? Is FOMO driving more decisions than strategy. Are we actually innovating, or just riding a hype cycle. maybe the real plot twist is that venture capitalists. with their wild portfolios, end up as the biggest losers if this AI gold rush fizzles out. Or Maybe, just maybe, it’s time for a reality check to see who really comes out on top—and who might be left holding the bag. Let’s explore this in depth:

“Vibe Valuation” in Generative AI Startups

Vibe valuing a term coined by The Economist in their June 25, 2025 feature, is the ability of venture capitalists to conjure up vast valuations for AI startups with scant regard for old-school spreadsheet measures like cash burn or ARR. the discussion and some of the examples that followed from it on linkedin, were interesting to say the least.

To begin with, the scale and speed of capital flowing into AI seemed unprecedented, with investors often betting on founder pedigree and vision rather than proven business fundamentals. for example:

• Thinking Machines Lab: Raised $2B at a $10B valuation, despite having no public product or revenue.

• Perplexity: At the time of its $14B valuation, had $34M in revenue and $65M in burn, equating to a valuation over 400× revenue—far above the ~6.5× multiple typical for public SaaS companies. Later comments updated Perplexity’s revenue to $100M+.

• Cursor (Anysphere): Raised $900M at a ~$10B valuation as an early-stage code-generation company, with later claims of $500M ARR.

The macro trend of skyrocketing valuations in generative AI sparked a good live debate and the reactions and surfaced key insights that warranted attention:

• Fact-Checking and Data Updates:
  Several participants challenged the original figures, especially for Perplexity and Cursor, providing more recent or higher revenue numbers and emphasizing the importance of up-to-date, accurate data.

• Founder Pedigree as a Key Driver:
  Many agreed that investments are often made based on the reputation and track record of founding teams, especially those with prior success in AI (e.g., ex-OpenAI leadership).

• Historical Parallels:
  Comparisons were drawn to past industry bubbles, such as telecom in India, where heavy early investment led to consolidation and significant losses for most players.

• Skepticism and Caution:
  Some commenters warned of a potential bubble, noting that gravity eventually catches up with inflated valuations and that most startups historically fail in their early years.

• Contrasting Approaches:
  The discussion also highlighted companies like Surge AI, which reportedly achieved $1B in revenue without any VC funding, showing a different path to success.

• Diverse Investment Rationales:
  Others noted that each business is unique, and direct comparisons are difficult; investors may have a variety of reasons for backing high-valuation startups.

I wanted to dig a bit deeper in to the telecom parallel, so I looked up.

In the late 1990s (around 1996-97), India opened its telecom sector to private players. At the outset, there were 12-13 well-funded companies entering the market, each investing heavily in capital expenditures (CAPEX).

• Outcome:
  Despite the initial influx of capital and competition, only three companies survived in the long run, with one reportedly still struggling ("in ICU"). The majority of the early entrants failed, resulting in significant financial losses for shareholders, the government, and investors.

• Lesson Drawn:
  The commenter suggested that the pattern of heavy early investment followed by consolidation and widespread losses is not unique to AI. The telecom example serves as a cautionary tale: large amounts of capital and hype do not guarantee sustainable business success. The cycle of over-investment and subsequent failure is a recurring theme in new, high-potential industries.

• The comparison was used to highlight that while AI is a promising field, strong investment fundamentals and a focus on real market needs remain crucial. History shows that speculative bubbles can lead to significant losses if not tempered by careful analysis and realistic expectations.

Together, these insights reflect both the optimism—and the caution—surrounding generative AI’s investment boom, underscoring the need for balanced perspectives and rigorous due diligence going forward. The debate is a healthy nonetheless, At this early stage its crucial to understand whether these “vibe valuations” are justified or if they signal a speculative bubble that could eventually burst. The current wave of AI investment is driven more by potential, vision, and founder reputation than by traditional financial performance and this conversation underscores the need for accurate, up-to-date data and skepticism toward media reports that may lag behind industry realities.

Why VCs Are Pouring Money Into AI

The current AI investment frenzy isn't just about technology—it's driven by a perfect storm of psychological and economic factors that’ll create massive opportunities for savvy founders who would understand the game.

The Fear-Driven Investment Machine

Two powerful psychological drivers that are reshaping the entire investment landscape:

AI FOMO (Fear of Missing Out) has reached unprecedented levels, with 57.9% of global VC dollars flowing into AI startups in Q1 2025 alone. This isn't rational analysis—it's panic-driven decision making where VCs fear being left behind in what many consider the biggest platform shift since the internet.

FOLS (Fear of Looking Stupid). VCs know that missing the next OpenAI or Anthropic could end careers, while investing in a failed AI startup can be explained away as "betting on the future." This creates a powerful asymmetric risk profile that heavily favors AI investments, regardless of traditional metrics.

The Math That Broke Everything

Venture capital runs on a simple "two and twenty" formula—VCs collect 2% annual management fees plus 20% of profits. Sounds straightforward, right? Critically, 95% of VCs never actually turn a profit, meaning they're essentially living off those management fees alone. A $100 million fund? That's $2 million flowing in annually, whether they back the next Google or fund spectacular failures.

This creates perverse incentives where VCs prioritize raising larger funds (bigger fees) and making headline-grabbing investments over actual returns. Ever wonder why every VC suddenly sounds like an AI evangelist?

When Fees Matter More Than Returns

What happens when your survival depends on management fees rather than picking winners? You start prioritizing bigger funds over better investments. Why stress about spreadsheets when you can raise a $500 million fund and guarantee $10 million in annual fees?

This creates the perfect storm: VCs now hunt exclusively for "extraordinary businesses" promising 10x-100x returns—not because these are better bets, but because that's the only math that justifies their existence.

The "Vibe Valuation" Era & The Real Question

Enter the most curious phenomenon in modern finance: traditional metrics are out, vibes are in. Annual recurring revenue? Burn rates? How quaint.

What matters now is founder pedigree, AI positioning, and whether the business feels like it could scale to the moon. It's as if an entire industry decided that actual business fundamentals were too boring for their ambitions. Makes you wonder what amazing technologies we're missing simply because they don't sound extraordinary enough for someone's fund math, doesn't it?

Here's what keeps me curious: when 95% of the people deciding which technologies get funded can't generate profits themselves, should we really trust them to pick the innovations that shape our future?

The Psychology Behind VC Decision-Making

VCs are not purely rational decision-makers—they operate under specific psychological drivers, economic pressures, and market dynamics that significantly influence their investment choices.

Pattern Recognition and Cognitive Biases

VCs rely heavily on pattern recognition, looking for familiar signals that indicate success such as market size, traction, and team credibility. Once they form an initial impression, confirmation bias leads them to seek supporting evidence while discounting contradictory information.

Current Market Dynamics Affecting VC Behavior

The 2025 venture capital landscape presents unique opportunities and challenges that directly impact how VCs make investment decisions. Global VC funding reached $91.5 billion in Q1 2025, representing an 18.5% increase from the previous year, signaling market recovery from the 2022-2023 downturn.

Valuation Environment

Current market conditions show significant valuation disparities:

• VC-backed startups trade at >30x revenue multiples based on growth potential

• Private SaaS M&A averages 4.2x revenue multiples

• Public SaaS companies trade at 4.8-5.0x revenue multiples

This means VCs are willing to pay substantial premiums for companies they believe can achieve massive scale, particularly in AI infrastructure.

AI infrastructure that enables everything else : Leveraging the Opportunity

So what could be one of the most compelling investment categories for VCs? Would bandarlog.dev be attractive enough to these VCs? I thought i’d look in to it from my own startup’s focus and positioning—Our focus is core network infrastructure which is not exactly AI infrastructure but supports it and is supported by it. this means, on a broader level I am in long-term looking in to everything from On-device AI, to semiconductors, quantum network optimizations, future business applications and services.

The current market presents unique timing advantages:

• AI infrastructure is underfunded relative to applications (supply/demand imbalance)

• 5G buildout is creating massive infrastructure upgrade cycles

• Edge computing requirements are exploding with AI workload growth

• Quantum computing is moving from research to commercialization

The key insight is that VCs are betting on picks and shovels, not just gold miners. While application-layer AI companies face brutal competition, infrastructure providers can capture value across the entire ecosystem. Infrastructure investments have historically produced the largest returns in technology platform shifts, and VCs know this. a quick revenue strategy is crucial for de-risking the investment for VCs which means focussing on:

1. Immediate value capture: Network optimization services using your AI algorithms

2. Proof of platform potential: Edge AI deployments that demonstrate scalability

3. Customer development: Building relationships with the telecom and cloud providers who will be your future SoC customers

So does this align with current VC psychology? Perhaps, I could tell a better story. The key is to position yourself not just as another AI company, but as the critical infrastructure that enables the AI future VCs are desperately trying to fund. Remember: VCs aren't just investing in technology—they're investing in their own career success and fund performance. So, Instead of saying "we make networks better with AI," I’d say "we're building the neural pathways for the next generation of connected intelligence." enablement over optimization. less incremental, more transformational.

What Storytelling Strategies Resonate Most with VCs to Boost startup Funding Chances

Understanding how to craft a compelling narrative is crucial for securing venture capital funding, especially in the competitive AI infrastructure space. The key is to remember that VCs are human beings making decisions under uncertainty and pressure. VCs make investment decisions based on both rational analysis and emotional connections, making storytelling a critical skill for founders seeking funding. the graph below presents the key and the most effective storytelling strategies that resonate with the VCs:

VCs typically make tentative investment decisions within the first 20 minutes of a pitch meeting. This compressed timeline means founder’s story must immediately capture attention and build conviction quickly. The most successful founders understand that VCs are pattern-matching against previous successes while simultaneously seeking breakthrough opportunities that defy conventional patterns

Core Storytelling Frameworks That Drive Investment Decisions

The most effective startup pitches follow a clear narrative progression that mirrors classic storytelling structures. Start with a compelling problem statement that creates emotional resonance, then present your solution as the inevitable answer, and finally demonstrate the transformative impact your technology will deliver.

Successful VC storytelling combines emotional resonance with logical proof points, leveraging psychological triggers while demonstrating clear paths to massive outcomes. The key is authenticity— Focusing on enabling the AI future rather than just participating in it, and structuring the story to make VCs feel they're getting early access to essential infrastructure for the coming AI transformation.

Positioning an AI Startup for Dual Appeal: A Strategic Framework for VCs and Founders

Dual positioning means crafting your AI startup’s story to appeal to both VCs and founders. It boosts funding chances, builds ecosystem credibility, and balances growth with technical trust. By aligning with investor goals and developer needs, you create a more resilient, scalable, and partnership-ready business for today’s competitive AI landscape.

The strategic positioning matrix above reveals that the most successful AI startups occupy the upper-right quadrant, achieving high appeal to both VCs and Founders simultaneously. This "sweet spot" positioning requires careful balance between commercial viability and technical excellence.

I hope you found this article interesting, informative, and useful. Do like subscribe, share it with you colleagues and friends and on social media — X, LinkedIn, or the platform of your choice.

Share @poonamparihar

Further reading :

• Why Tech Journalism Is Now the Most Powerful Beat in the Newsroom

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase

Over little more than a decade, technology has moved from the sidelines of public conversation to the very heart of global culture. Once relegated to back pages of specialist magazines and tech forums, news about software breakthroughs, AI advancements, and product launches now dominates headlines and everyday discussions including dinner-table conversations. What began as dispatches about chips, modems, and venture funding is now continuous coverage of AI trends and consumer experiences like metaverse.

This mainstreaming of tech news has done more than simply change what we talk about—it has transformed who we admire and what we aspire to become. Today, the household names shaping our world are not movie stars or pop icons, but tech founders and CEOs whose innovations and bold visions capture the public imagination.

Before the iPhone era, very few schoolchildren dreamed of running a semiconductor company or chairing a space start-up—well at least I did not. Fast forward to 2025, and tech chief executives are the new household names, eclipsing many Hollywood idols in reach and cultural sway.

While the parallel boom in influencer culture is plateauing or simply mutating. It’s the Tech leader’s meteoric follower counts, meme-ready sound bites, and market-moving antics illustrate how technology leadership itself has become a spectator sport—and a career aspiration. As technology reporting and tech leaders become ever more central to our lives, it is also fueling a profound cultural shift: entrepreneurship and innovation are now the new symbols of aspiration and influence.

The Algorithmic Transformation of News

Technology journalism’s rise has fundamentally transformed the news landscape, with algorithms now acting as gatekeepers that shape what stories reach audiences and how they are presented. This shift is not just technical but philosophical, as editorial decisions are increasingly driven by algorithmic logic rather than human judgment. Newsrooms are rapidly adopting AI tools for both efficiency and creative purposes, with a majority of media executives viewing AI as essential for journalism’s future. However, this integration raises concerns about the erosion of distinctly human journalistic qualities—such as judgment, curiosity, and skepticism—while also fostering “filter bubbles” that personalize content at the expense of shared civic understanding. The proliferation of misinformation, amplified by social media algorithms and AI-generated content, has created a crisis of trust, as traditional fact-checking struggles to keep pace with the scale and sophistication of false information. At the same time, local news is in steep decline, with many communities becoming “news deserts” lacking any full-time journalists, which undermines democratic accountability and civic engagement.

The Mainstreaming of Technology News

The 1990s – A confined beat

Early technology desks catered to investors and hobbyists. Circulations were modest, and stories rarely crossed into mainstream newspapers unless a spectacular IPO or antitrust case arose.

2005-2015 – The smartphone era

Apple’s iPhone (2007) and Android’s rapid rise normalized the idea that every consumer is a technology user. Newsrooms responded by adding “Tech & Gadgets” verticals and liveblogs; conference keynotes began to attract audiences rivaling political debates.

2020-2025 – Always-on tech coverage

Average time US adults spend with digital media has climbed steadily, reaching 7.58 hours per day in 2025. The news industry follows eyeballs; as daily digital engagement grew, so did demand for real-time reporting on the platforms, policies and personalities shaping those screens.

U.S. adults now spend nearly 8 hours per day with digital media, up 57 minutes since 2021

Engines of the Tech-News Boom

Digital ubiquity

Two-thirds of humanity – 5.64 billion people – is now online. Smartphones, wearables and connected appliances turn even lifestyle stories into technology stories, pulling general-interest outlets into the beat.

Platform gateways reshape discovery

Traditional homepages no longer dominate traffic. Only 22% of consumers in 47 markets start their news journey at a publisher site; most begin on social platforms, search or aggregators. The biggest gateways for news in 2025 are Facebook (36%), YouTube (30%), Instagram and WhatsApp (both 19%).

Capital markets and corporate drama

Technology now accounts for almost one-quarter of global equity value. Quarterly earnings calls from Apple, Microsoft or Nvidia move broader indices, so mainstream outlets cover them as closely as treasury reports or labour statistics.

Influencers and personality-driven storytelling

A generation of creators – from Marques Brownlee to TikTok gadget reviewers – reaches tens of millions directly, demonstrating to legacy newsrooms that personality, demonstration and entertainment can coexist with rigorous reporting.

Social and Cultural Consequences

A more tech-literate public

Surveys show rising familiarity with concepts such as encryption, algorithmic bias and quantum computing, driven in part by explanatory journalism and YouTube explainers. This literacy empowers consumers to demand better privacy standards and product transparency.

Information overload and news fatigue

The volume and velocity of tech headlines also fuel psychological strain. Four in ten adults across 47 countries now say they feel “worn out” by the news cycle, a record high. Selective avoidance of news – choosing to skip stories perceived as negative or relentless – has climbed from 29% in 2017 to 39% in 2025.

Shifts in newsroom practice

• Data-driven beats: Journalists increasingly interrogate training-data sets, carbon footprints of data centres and supply-chain spreadsheets.

• Solutions framing: Editors, responding to fatigue studies, package constructive angles on digital harms and “what can be done” pieces to retain readers.

• Cross-disciplinary teams: Product, design and investigative units now work side-by-side, mirroring the agile culture of the sector they cover.

Economic and Political Repercussions

Regulatory urgency

Mainstream exposure has accelerated policy timelines. The EU’s Digital Services Act, multiple US congressional hearings and Australia’s bargaining code all drew momentum from sustained public coverage of data breaches, AI hallucinations and social-media harms.

Advertising realignment

Brands have followed engaged audiences into tech sections, but keyword blocklists on “crypto,” “hack” or “AI risk” sometimes choke revenue even for high-quality outlets. Simultaneously, specialised tech newsletters command some of the industry’s highest open rates – 34.6% on average – supporting subscription and sponsorship models.

The timeline showing tech coverage’s journey from niche newsletters to nightly news still stands, but its climax now includes a celebrity subplot: live-streamed launch events draw ratings that rival awards shows, and CEOs’ tweets are dissected like red-carpet outfits. CEO charisma has become headline fuel because direct-to-public channels let leaders bypass traditional media and instantly sway markets, while social platforms amplify bold personalities and dramatic moments for maximum visibility. The spectacle of outspoken CEOs—whose remarks can move billions in market value—draws relentless media attention, further fueled by the para-social bonds audiences form with these figures online. for example Elon Musk’s following on X (formerly Twitter) recently crossed 220 million, outperforming every entertainer on the platform. This dynamic creates a feedback loop where coverage of CEO antics drives market volatility, which in turn generates even more coverage, often benefiting celebrity leaders with higher pay and job security, even as their companies’ long-term performance may suffer.

Celebrity Tech Titans vs. Old-School Fame

Follower math: tech outruns Tinseltown

• Elon Musk: ≈ 222 M followers on X

• Dwayne Johnson: ≈ 393 M on Instagram

• Tim Cook: sub-15 M on X—yet Apple’s product keynotes still command 30 M live streams.

Despite Hollywood’s longevity, four of the ten most-followed social accounts belong to tech or sports figures, not actors. The data suggest a tectonic shift in who society finds aspirational.

Why kids now cosplay CEOs

A 2019 Lego-Harris poll found that only 11% of US and UK children wanted to be astronauts, while 30% aimed for YouTube stardom. Fast-forward to 2025 and a Whop survey shows 32% of Gen Alpha wanting to become YouTubers or TikTok creators, but another emergent ambition is “tech founder/CEO,” cited by 14%—twice the share dreaming of acting careers.

Is Influencer Culture Really Dying?

Audience trust is fragmenting, not evaporating

Edelman’s multi-market Brand Trust study reveals 56% of Gen Z still prefer influencer recommendations to brand ads, yet user fatigue with mega-creators is climbing. Among Gen Z, influencers still hold a trust edge over conventional advertising in 2025.

Macro-influencer fatigue, micro-influencer boom

• Engagement rates for creators above 1 M followers fell 23% between 2023-25, while micro-influencers (10-100 K) grew 14%.

• Brands now allocate 59% of their creator budget to <100 K-follower talent for authenticity

• Creator-led commerce is far from dead: Sprout Social shows 49% of consumers still make monthly purchases triggered by influencers, with Gen Z the most responsive cohort.

Influencer culture may not be dying as yet—but the era of one-size-fits-all celebrity endorsements is waning. Trust is migrating toward niche experts and, intriguingly, towards charismatic CEOs whose updates feel “inside track.”

Social and Cultural Consequences – New Signals

Aspirational spillovers

Tech’s rags-to-riches narratives inspire grassroots learning: Udemy reports 40% YoY growth in under-18 enrollments for “how to build a start-up” courses following high-profile CEO interviews.

Downsides of CEO idolatry

Academic meta-analyses warn that celebrity chiefs tend to double down on the behaviours that earned them fame, even when markets shift, raising strategic risk. Boards now debate whether public adoration is a governance asset or a liability.

Tech journalism is now people journalism. When CEOs accumulate fan followings that dwarf pop icons, their every keynote, meme and regulatory rebuke becomes mainstream news. Yet the same surveys that declare influencer fatigue reveal persistent trust in relatable voices—be they nano-creators or charismatic founders. The next decade will test whether audiences can maintain curiosity without surrendering critical distance, and whether celebrity capital can be channelled from hype to long-term value. The spotlight is brighter than ever; so is the need for clear-eyed coverage.

Where We Go Next

Rise of AI-mediated news journeys

Generative AI search and chatbot answers already divert clicks from publishers, with 69% of news searches yielding no website visit. Journalists will need structured data, licensing deals and conspicuous added value (for example, interactive testing or verifiable sourcing) to stay visible.

Diversification of formats

•  Vertical video and shoppable reviews for younger audiences.

•  Long-form podcasts retaining older, high-engagement segments.

•  Immersive explainers in XR as devices become lighter and cheaper.

Strategies for stakeholders

I hope you found this article interesting, informative, and useful. Do like subscribe, share it with you colleagues and friends and on social media — X, LinkedIn, or the platform of your choice.

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Further reading :

• Legends from the Land of Logic…in the making, mostly.

• Project Trillion: UK's 10-Year Race to Reshape Global Technology

• Between Promise and Practice: The Real Story of AI Adoption

• The Anatomy of the Shake-Out Phase