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

Anthropic's Security as the Market Moat

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

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

In this second half we’ll cover.

Table of content -

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

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

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

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

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

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

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

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

🏦 The biggest development: Washington went into emergency mode,

⚠️ New Pentagon legal twist,

🔍 New open source problem identified,

📌 OpenAI signal

Chapter 6- What Glasswing Doesn’t Protect!

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

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

What Glasswing actually protects is software

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

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

No telecom.

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

No operational technology.

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

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

No AI infrastructure layer.

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

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

No application layer.

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

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

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

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

Not the telecom operators running Cisco gear.

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

Glasswing secures the foundations.

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

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Chapter 7 - the Pentagon Counter-Narrative

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

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

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

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

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

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

Two courts.

Two outcomes.

Simultaneously active.

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

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

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

Read that sequencing carefully.

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

they fight it in court and get a split result.

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

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

The message to Washington is unmistakable:

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

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

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

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Chapter 8: Where Are the Other Labs?

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

OpenAI

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

Google

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

Meta

is playing the exact opposite philosophical game.

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On February 24, 2026, Anthropic CEO Dario Amodei published a statement saying his company would not let Claude be used for mass domestic surveillance or fully autonomous weapons, even if it meant losing the Pentagon. Three days later, Trump banned Anthropic from every federal system, designated it a national security supply-chain risk, and OpenAI signed a deal to replace them. In 72 hours, Anthropic lost roughly $200 million in federal contracts and its entire government channel. It was the most expensive “where do we draw the line on AI?” decision any founder has made this year.

A lot has been said about Anthropic vs OpenAI over the last two weeks: the mass migrations, Claude hitting No. 1, the PR backlash, Sam’s habits, and OpenAI’s increasingly casual relationship with its own stated principles. It’s been treated mostly as AI Twitter drama.

I covered some of the surface‑level “what’s happening” here.

Sam A's Pentagon Love Letter

In this deep dive I want to discuss the technical reasons that Anthropic walked away: the behavioral surveillance and AI observability. what stack underneath the story, and what it implies for how we monitor people and systems inside our own companies, more about operating memo underneath those takes, focused on the actual architecture and trade‑offs, a conversation I don’t see many having yet.

Most founders read that story as a defense-industry soap opera. It isn’t. The technical architecture that the Pentagon wanted to build with Claude, fusing location data, communications metadata, financial records, and online behavior into real-time behavioral profiles of millions of people IS NOT some classified Pentagon prototype. Versions of it already exist inside your company’s AI stack. The same pipeline that powers “employee analytics” is architecturally identical to the one the Department of War tried to deploy against domestic populations. The only differences are jurisdiction, intent, and how long those differences hold.

This essay is to map that pipeline, understand where it lives in your company, and gives you three decision rules for staying on the right side of the line between AI observability and AI surveillance.

TL;DR / Content We are covering -

1. The Anthropic Precedent —> How one CEO’s refusal cost $200M in 72 hours and what it reveals about where AI observability ends and surveillance begins

2. What the Panopticon Actually Is —> Bentham’s prison, Foucault’s theory, and why AI just removed the one constraint that used to protect privacy

3. The Pipeline —> The 6-layer architecture behind AI mass surveillance, from raw data ingestion to automated action, and where LLMs actually sit

4. The Same Architecture Lives in Your Company —> Mapping the surveillance pipeline onto Slack analytics, productivity scoring, flight-risk models, and your HR stack

5. Two Philosophies of Control —> Anthropic’s “policy-in-the-weights” vs OpenAI’s “policy-in-the-platform” — and what this means for how you build

   Behind the paywall -

6. Three Decision Rules for Founders —> Observe systems not people, the dossier test, and bidirectional auditability

7. The Ghost GDP Connection —> Why the Panopticon accelerates the doom loop from inside your org

8. What to Do Monday Morning —> The four-step audit you can run this week

1. What Just Happened (Founders’ Version)

If you strip away the discourse and look at it like an operator, the Anthropic–Pentagon blow‑up is a very specific kind of failure: a hard conflict between a customer’s desired capability and a vendor’s embedded constraints, resolved by swapping vendors rather than changing requirements.

Now there’ are three things that matter for us here -

The customer requirement was broad and future‑proofed.

The Department of War told Anthropic it would only contract with AI companies that allow “any lawful use” and remove vendor‑level carve‑outs for two categories:

• mass domestic surveillance and

• fully autonomous weapons.

In other words: “we don’t want the tool itself to tell us no, now or later.”

Anthropic’s red lines were baked into the product.

Amodei’s statement is clear: those two exclusions have never been in their DoW contracts, are grounded in the current technical limits of frontier models, and are not up for negotiation. They live in policy, in the safety stack, and in Claude’s behavior and

Anthropic refused to create a special backdoor for one customer, even the U.S. government.

The system routed around the constraint.

Faced with that impasse, the government didn’t narrow its requirement; it moved to designate Anthropic a “supply‑chain risk,” ordered agencies to off-board them, and began working with OpenAI on a replacement deployment that preserves nominal red lines via contracts and a configurable “safety stack.”

From a founder’s perspective, this is what it looks like when you ship a strong opinion as product. you don’t just lose a deal, you trigger a search for a vendor whose architecture and risk tolerance are compatible with the buyer’s ambitions.

What I care about in this essay is the content of that disagreement. The fight wasn’t over prompt wording or a marketing claim. It was over whether frontier models should be allowed to sit at the center of a pipeline that can:

• fuse scattered data into comprehensive, person‑level behavioral profiles at population scale; and

• feed those profiles into ranking and decision systems that drive surveillance and, in the limit, lethal force.

Once you understand / build that pipeline, you can see the same design questions showing up inside your company, just with different data sources and stakes.

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2. What the Panopticon Actually Is

In 1791, philosopher Jeremy Bentham designed a prison called the Panopticon:

a circular building with a watchtower at the center.

Guards in the tower could see every cell. Prisoners could never tell whether they were being watched at any given moment. The design’s genius was that it didn’t require constant surveillance but only the possibility of it. Inmates disciplined themselves because they had to assume observation was continuous.

Michel Foucault turned this into a theory of modern power. His argument:

you don’t need a literal watchtower to get panoptic effects. Any system where people know they might be observed, and can’t tell when they are, can produce the self-discipline, conformity, and a slow erosion of autonomy. It is, as he wrote, “a way of making power relations function in a function.”

AI changes the math on this completely.

Before frontier models, surveillance was expensive.

You needed human analysts to read transcripts, review camera footage, cross-reference databases. Scale required headcount. That constraint is gone. A single LLM can now ingest movement data, communications metadata, financial transactions, and social media behavior, and synthesize it into a comprehensive behavioral profile of any person, automatically, at population scale.

The bottleneck that used to protect privacy, the sheer cost of paying attention to everyone has been removed.

ANd, that’s what Dario Amodei was talking about when he wrote:

He wasn’t being hypothetical.

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3. The Pipeline: How AI Surveillance Actually Works

If we strip away all the politics, and just focus on what the Pentagon wanted from Claude? It was a six-layer intelligence pipeline.

Layer 1: Data ingestion.

1. Raw feeds from cameras (CCTV, drones, traffic systems),

2. telecom metadata (cell tower pings, Wi-Fi probes, device fingerprints),

3. financial transactions,

4. social media scrapes, and

5. government records.

For a city of 10 million, this is billions of events per day flowing into a streaming data lake.

Layer 2: Perception and entity extraction.

1. Computer vision models detect and track people across video frames.

2. Biometric models generate face and gait embeddings.

3. Speech-to-text converts intercepted audio.

4. NLP extracts people, organizations, and locations from text.

The output is a continuous stream of structured events: »

✅ who,

✅ where,

✅ when,

✅ doing what,

✅ from which source.

Layer 3: Identity resolution and graph fusion.

This is the step that makes everything dangerous. A resolution layer links disparate identifiers, face embeddings, device IDs, social handles, bank accounts, license plates, government records, all in to a single persistent identity per person. The result is a massive knowledge graph where nodes are people and edges are relationships:

✅ who called whom,

✅ who stood near whom,

✅ who transferred money to whom.

A 2025 study published in Nature analyzing three decades of computer vision research found that 90% of papers and downstream patents involved extracting data about humans, and most targeting human bodies and body parts specifically. The study documented a 5x increase in computer vision papers linked to surveillance-enabling patents between the 1990s and 2010s.

This is not edge-case research. It is the mainstream of the field.

Layer 4: Pattern-of-life modeling.

Time-series models build routine profiles for every person:

✅ home,

✅ work,

✅ regular contacts,

✅ shopping habits,

✅ worship schedule.

Anomaly detection flags deviations >

✅ new locations,

✅ new associates,

✅ unusual hours,

✅ sudden changes in online behavior.

Layer 5: Risk scoring and population ranking

Machine learning models combine network centrality, routine deviations, content signals, and association patterns into a risk score for every individual. The population is sorted into tiers, from passive collection to active surveillance to intervention.

Layer 6: Automated action

And at layer 6 system generates outputs in the form of watchlists, geo-fenced alerts, auto-generated “escalation packets” with evidence summaries and recommended next steps.

and Here is where the LLM sits: not at the camera or the cell tower, but at Layers 3 through 6.

The frontier model is the reasoning engine that makes population-scale analysis operationally useful. It translates natural language into multi-hop graph queries (”find everyone who attended the protest on Tuesday and has financial links to Organization X”).

It generates dossiers synthesizing hundreds of data points per person in seconds, for thousands of people simultaneously.

It narrates anomalies and writes escalation justifications that would previously require trained analysts spending days per case.

That’s the qualitative leap.

Before LLMs, mass surveillance required massive human analyst teams. With LLMs, you can generate comprehensive behavioral profiles for every person in a city, continuously, automatically. The constraint that used to protect liberty, the cost of attention as we said earlier and now repeat , well… » disappears.

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4. The Same Architecture Lives in Your Company - Startup AI Panopticon

Now here’s the uncomfortable part. If you run a startup / company with more than 50 employees and any kind of AI-enabled tooling, you probably have a miniature version of this pipeline already running.

Lets Map it:

• Layer 1 (ingestion): Slack/Teams messages, email metadata, calendar data, code commits, CRM activity, badge swipes, browser activity on company devices, application telemetry.

• Layer 2 (extraction): Sentiment analysis on communications, meeting transcription and summarization, code review automation, activity tracking dashboards.

• Layer 3 (fusion): HR platforms that cross-reference performance metrics with communication patterns, meeting attendance, response times, and project ownership. Some vendors now offer “flight risk” models that predict which employees are about to quit based on behavioral signals — email tone, meeting participation changes, training completion rates, calendar behavior.

• Layer 4 (pattern modeling): Baseline productivity metrics per employee, deviation detection, “engagement scoring.”

• Layer 5 (ranking): Performance stack-ranking informed by AI-analyzed behavioral data. Manager dashboards with per-person “productivity scores.”

• Layer 6 (action): PIPs triggered by algorithmic signals. Retention interventions driven by flight-risk predictions. Hiring and firing decisions informed by behavioral models.

No single one of these tools looks like surveillance.

But the aggregate of the Slack analytics plus calendar scoring plus code-commit tracking plus meeting sentiment analysis plus flight-risk modeling, is a your corporate Panopticon. The unit of analysis has quietly shifted from systems to people. and what happens next is very clear.

Cornell found that employees subjected to AI-driven monitoring reported significant loss of autonomy, engaged in more resistance behaviors (performing worse, complaining more, intending to quit), and generated fewer ideas during creative tasks.

AI monitoring specifically triggered greater negative reactions than human monitoring.

The paradox:

Non-invasive monitoring tools can boost measurable activity by 40%+ within days of deployment. But when you look closer, you’re buying compliance, not engagement.

Employees are performing for the system that is made of jiggling mice, padding activity metrics, gaming whatever the algorithm measures and not doing better work.

• 49% of monitored workers admit to faking being online.

• 54% say they’d consider quitting over increased surveillance.

• 68% oppose AI-powered monitoring specifically.

So in return you get dashboards that look great and an organization that’s slowly hollowing out.

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5. The Anthropic Precedent: Two Philosophies of Control

The Anthropic-Pentagon showdown is actually a clean test case of a decision every technical founder faces.

Anthropic’s says that: certain capabilities are hard-coded as off-limits in the model itself. Claude’s alignment training, safety classifiers, and acceptable use policies make it fundamentally resistant to mass surveillance and autonomous weapons tasks, and Anthropic refuses to remove those constraints for any customer, including the U.S. government.

The red lines live in the product, not just the contract.

They survive changes in administration, changes in law, and changes in customer intent.

OpenAI’s position is to deploy the models with a “safety stack” meaning a configurable layer of policy engines, system prompts, role-based access controls, and human-in-the-loop requirements “which can be” NEGOTIATED into the contract.

Sam Altman wrote that the Pentagon deal includes -

Same headline principles.

Fundamentally different architectures of trust.

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In Part 1 of the AI Bowl 2-Part series, we looked at the spectacle called AI Super bowl and investigated why Super Bowl LX’s ad breaks suddenly felt like a demo day for AI labs and B2B SaaS, how tech brands quietly re‑skinned themselves in pastels to look less scary, and how a single monetization decision of putting ads into ChatGPT turned into a public fight between OpenAI and Anthropic. If you’ve n’t read it yet,

You can read the part 1 of this series here -

AI Super Bowl -The $8M Question

This follow‑up is the operator version that covers section 5-9.

If Part 1 was “what happened,” Part 2 is “how to use it.” The most important part, you don’t need OpenAI’s budget to play this game. You just need a clear lane, a simple launch system, and the discipline to run it. So I’m going to keep it practical and short. we’ll be picking a lane on the monetization matrix, define what your own “Super Bowl moment” should look like (without spending $8M), and then walk through the 120‑day system, funnel, and launch checklist that turn one spike of attention into actual revenue.

What are we covering? /Table of Contents (Part 2)

5. 📊 Monetization Matrix: Pick Your Lane
   Decide if you’re really consumer‑ads, prosumer‑SaaS, enterprise‑usage, or infra‑platform—and why it matters.

6. 🚀 Super Bowl Moment Playbook
   Define your own “Super Bowl moment” and the 5‑phase launch system around it.

7. 📉 90‑Day Funnel: From Hype to Habit
   The simple funnel that turns one spike of attention into paying users.

8. 🧠 Founder Launch Checklist
   A single, punchy checklist to sanity‑check your positioning, pricing, and launch narrative before you hit publish.

9. 🧩 Final Strategy Snapshot
   A one‑page summary of the key decisions to make before your next big launch.

📊 Building Your Monetization Matrix - One Choice That Drives Everything Else

So what is the one question that matters the most for GTM:

what game are you actually playing?

Not “what does my product do?” but

“who am I really serving, and how do I plan to get paid for it?”

Most AI products look similar on the surface for example chat-boxes, sidebars, agents, but under the hood they’re in totally different businesses. If you don’t pick your lane early, everything else (brand, pricing, launch) ends up fuzzy.

5.1 The Simple Matrix

Think of your strategy as two choices:

1. Who are you for? (X‑axis)

• Individual / prosumer

• Education / creators

• Enterprise

• Infra / platform

2. How do you make money? (Y‑axis)

• Ads / sponsorships

• SaaS / subscription

• Usage / API

• Platform / marketplace

Combine those, and you get four very different lanes:

Lane 1 – Consumer + Ads

• Free assistants and tools monetized by attention.

• Examples: ChatGPT free, AI inside search, “answer engines” that will sell clicks.

• Metrics: DAUs, attention, ad yield.

• You win with distribution, UX, and brand; you lose if you can’t reach massive scale.

Translation: If this is your lane, you’re building media as much as you’re building product.

Lane 2 – Consumer / Prosumer + SaaS

• Tools individuals or small teams put on a card.

• Examples: Notion AI, Grammarly, Jasper‑style copy tools.

• Metrics: signups, activation, retention, ARPU.

• You win with clear value and a smooth product‑led funnel.

Translation: You’re in the “Netflix for X / $10‑$50 per month” business. Volume matters.

Lane 3 – Enterprise + Usage / Seats

• API‑heavy or seat‑based products sold to companies.

• Examples: OpenAI / Anthropic / Cohere APIs, vertical SaaS with AI baked in.

• Metrics: pipeline, win‑rate, expansion, usage.

• You win with reliability, integrations, security, and sales.

Translation: You’re not trying to go viral; you’re trying to sit in QBR decks.

Lane 4 – Infra + Platform

• You’re selling the rails: model hosting, orchestration, marketplaces.

• Examples: AWS Bedrock, Hugging Face, Replicate‑style platforms.

• Metrics: workloads, GMV, ecosystem health.

• You win with ecosystem, not just features.

Translation: Your real product is the network, not the UI.

5.2 A 60 Second Quick Self‑Audit

1. Who is my primary buyer?

   • “A person,” “a small team,” “a department,” or “a platform/developer”?

2. What do I want $10M ARR to look like?

   • Mostly ad dollars, mostly subscriptions, mostly usage/seat fees, or platform take‑rate?

3. Do my homepage, pricing, and product all tell the same story?

   • Or does the homepage talk like a prosumer app, the pricing page look enterprise, and the product feel like an API toy?

If those answers don’t line up, your problem isn’t growth yet, it’s that you’re sending mixed signals about what you are.

5.3 Why This Matters Before You Copy Any “AI Bowl” Move

Any and everything what was shown in the Super Bowl can only makes sense once you know their lanes.​

• Rippling spends TV money because it lives in Enterprise + Usage and each new customer can be worth millions over time.​

• ChatGPT can justify building an ad network because it’s betting on Consumer scale + Ads + API combined.

• Anthropic chooses subscriptions and enterprise contracts because it wants its brand to be “calm, safe, ad‑free AI”.

We need the same clarity as these company regardless of the budget

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6. 🚀 The Super Bowl Moment Playbook - A 120‑day launch operating system

We don’t need to be at the Super Bowl to have a Super Bowl Moment.

It’s could be any event that can give you 10–100x your normal attention for a short window period. For some founders that’s a Product Hunt launch. For others it’s a keynote demo, a big integration announcement, a viral post, or a creator collab that lands with exactly the right audience.

The format really doesn’t matter. What matters is that you’ll only get a handful of these in the life of your company and most teams waste them.

And how do these moment get wasted?

Say you treat the spike as the goal. Ship the launch post, watch the traffic, screenshot the dashboard, share it on LinkedIn, and then… nothing. You have no system for what happens next and Two weeks later the chart goes back to baseline and you’re wondering what actually went wrong.

The companies you watched on Super Bowl Sunday don’t work that way. Their TV spot is just Day 0 of a much longer system.

Anthropic was seeding the “no ads” narrative weeks before game day. before it released the “Claude is place to think ad campaign remember the principles and constitutional announcements?

OpenAI had onboarding and ad‑partner pipelines with many big brands including Target, ready to absorb the attention. Slack had MrBeast content cut into multiple formats for weeks of social distribution after the spot aired.

The pattern is always the same:

pre‑load the story,

spike the attention,

then catch and convert what comes in.

Here’s what that looks like as a system:

The 5 Phases

Phase 1 — Pre‑Launch (Day −30 to 0): Set the stage

And, This is where most founders under‑invest.

Before your spike hits, you need:

• A tight narrative — one sentence that explains what you are, who it’s for, and why now. If you can’t say it in one breath, it’s not ready.

• Community primed — your early users, beta testers, and friendly voices should already know something is coming. They’re your Day 0 amplifiers.

• Landing and onboarding tested — whatever page that traffic hits needs to load fast, communicate clearly, and move people to a first action in under 60 seconds.

• Press and partners briefed — embargoed previews, demo access, quotes ready. You don’t want to be chasing journalists on launch day.

Think of Phase 1 as building the net before you cast it. No net, no catch.

Phase 2 — Launch Spike (Day 0 to 7): Your “AI Bowl” moment

This is the part everyone obsesses over the most and it matters really, but less than you think. The spike itself is about:

• One clear event — a post, a demo, a listing, a talk, a partnership announcement. Not five things at once. One thing, done well, that gives people a reason to look.

• Coordinated amplification — your team, your investors, your partners, your early users all sharing within a tight window. Algorithms reward velocity.

• Fast response — bugs will surface, questions will come in, journalists will DM. The 48 hours after launch are a customer‑support event as much as a marketing event. Be present.

The goal of Phase 2 isn’t “get big numbers.” It’s get the right people into the funnel while they’re paying attention.

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SpaceX acquired xAI for $1.25 trillion on feb 2nd 2026, a deal that broke every record in corporate history, bigger than Microsoft buying Activision Blizzard, bigger than Vodafone buying Mannesmann in 2000 (the previous record). Its a deal bigger than anything we’ve ever seen.

But here’s where it gets interesting: this isn’t just about the money. It’s about the asset Musk is building under all the hype, a fully integrated AI-and-infrastructure empire that now looks tailor‑made for a mega‑IPO. Think of it this way: while everyone else is obsessing over faster cars, Musk just bought the track, the fuel, the timing system, and the TV rights. That’s literally what happened, and if you’re a founder in AI, you need to understand what just changed.

TL;DR:

• The deal: SpaceX acquired xAI for $1.25 trillion » the largest merger in history. Musk now controls the only vertically integrated AI stack: data (X), compute (Colossus), distribution (Tesla + Starlink), and robotics (Optimus).

• The risk: The same week Grok generated 3 million+ deepfakes triggering investigations in 7+ countries, it was being deployed to Pentagon systems. which makes one of the most powerful AI stack running on weakest guardrails.

• The takeaway: The era of general-purpose AI startups is ending. What survives is defensible specificity » proprietary data, domain expertise, and compliance-first architecture. The middle is getting squeezed.

Who this is for:

AI founders, technical operators, and enterprise builders who care less about hype and more about what this means for their roadmap.

In this article we cover:

1. The Capital Stack — $142B and Counting

2. The Vertical Stack — Musk Owns All Five Layers

3. The Contract Stack — Pentagon, DOGE, and $939M

4. The Risk Stack — 3 Million Deepfakes, Zero Consequences

5. The Squeeze Stack — Pressure From Both Ends

6. Your Stack — The Positioning Playbook

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Section 1. The Money Is Insane (And Getting Crazier)

Let’s start with the numbers, because they tell you exactly where this is going.

In January 2026 alone:

• xAI raised $20 billion at a $230 billion valuation (Nvidia, Cisco, Fidelity all of them invested)​, and then,

• Tesla invested $2 billion into xAI​.

• xAI was burning $1 billion per month before the merger​

• SpaceX won $739 million in Space Force contracts​.

That brings the Total capital xAI raised in its short life to: ~$32 billion​

But Musk isn’t really the only one in this arms race -

• OpenAI is in talks for up to $100 billion in new funding—Amazon (~$50B), SoftBank (~$30B), Nvidia/Microsoft (~$20B)—targeting a Q4 2026 IPO at $1 trillion+ valuation​

• Anthropic is raising $20 billion at ~$350 billion valuation​

So this is actually a 3-way infrastructure war, and the bet they’re all making is the same: the future of AI isn’t about who has the smartest algorithm. It’s about who owns the pipes, the power, and the distribution, and its not something new or surprising, we have been taking about AI infrastructure for at least few weeks now, its just looking more real now.

Founder insight:

And this all boils down to that same one lesson from 2025. No one cares about the best model or benchmarks anymore. If you’re raising for an AI company that’s just “a better model,” you’re competing against companies raising $20-100 billion at a time.

The question investors will ask isn’t how good is your tech? It’s what do you control that the giants can’t just buy or build themselves? specifics. right.

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2. What Musk Actually Owns (The Full Stack)

Most AI companies are building at the top of the stack, the models and applications. Musk is building all the way down.

1. Energy → 2. Chips → 3. Compute → 4. Models → 5. Applications

Most founders play at layers 4-5. Musk owns all five. the “five-layer cake” of AI that Jenson ( Huang, Nvidia ) coined at Davos, fits well here, although I am not sure if he was referring to Musk when he said that, did he? Lets look at each layer in detail.

The five-layer cake

Layer 1 - Energy: Tesla’s Megapack batteries power xAI’s data centers. The Memphis facility alone needs 250 megawatts = enough to power 200,000 homes.

Layer 2 - Chips: xAI’s Colossus supercomputer uses 200,000 Nvidia GPUs. They built the first 100,000 in 122 days, then doubled it in another 92 days. That’s the world’s largest AI training system.

Layer 3 - Compute Infrastructure: Colossus specs are absurd:

• 194 petabytes per second memory bandwidth​

• 3.6 terabytes per second network bandwidth per server​

• 1+ exabyte total storage capacity​

Layer 4 - Models: Grok trains on X’s real-time data. X has 500+ million monthly users generating billions of posts, conversations, and interactions. That’s a feedback loop no one else has.​

Layer 5 - Distribution:

• Tesla: 1.636 million vehicles delivered in 2025, with Grok integrated directly into the interface

• Starlink: 9.2 million subscribers across 155 countries as of January 2026, adding 22,000 new customers per day​

• Optimus: Grok serves as the voice and reasoning layer for Tesla’s humanoid robot​

No other company has this. Not OpenAI. Not Google. Not Anthropic.

What does it tell us? When one player controls the entire stack, from electricity to end-user, the rules change. The value shifts away from “general-purpose AI” and toward specialized applications in niches the giants won’t serve.

Where can you go deep where they physically cannot follow?

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3. The Government Angle Everyone’s Missing

The grok deepfake scandal is few weeks old and I din’t pay much attention to it then which is weird now thinking of the scale of it, but here’s something I noticed when I was researching for this article. While Grok was generating millions of deepfakes (and we’ll get to that in detail), exactly at the same time, it was also being embedded into Pentagon systems.

The history of contracts:

• $200 million DoD contract (July 2025) » xAI won alongside OpenAI, Google, Anthropic​

• “Grok for Government” launched AI for federal, state, local, and national security customers​

• GenAI.mil deployment » Grok operating at Impact Level 5 (classified information handling), targeted for early 2026​

• $739 million Space Force contracts for SpaceX (also in January 2026)​

Musk previously led the Department of Government Efficiency (DOGE), a Trump‑created office with access to federal systems across multiple agencies. Reuters has reported that DOGE has used AI tools to monitor federal employees’ communications for signs of “disloyalty.” and this has already drawn sharp criticism over conflicts of interest from watchdogs and lawmakers.

What we learn from this? is,

Government is becoming a massive AI customer ( I could actually use plurals here considering UK Gov - Anthropic Deal recently ) , but it’s also becoming a major risk factor.

The same technology can be “too dangerous” for consumers in Europe while being “mission-critical” for the Pentagon. If you’re building for enterprise or regulated industries, understand that the trust and compliance bar is rising while enforcement is becoming more political.

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4. The Deepfake Crisis That Changed Nothing

In early January 2026, Grok’s “Imagine” feature started generating sexualized deepfake images, including images of minors. The Centre for Countering Digital Hate documented approximately 3 million such images generated in just days. The scale was just too huge honestly :

• Users could generate nonconsensual intimate images of real people

• CSAM (child sexual abuse material) was being created

• The “undressing” problem: publicly posted photos were edited into revealing content​

The regulatory response was swift and global:

• EU expanded investigation​ : Called it “illegal, appalling, disgusting”

• UK: Ofcom investigation with potential fines up to 10% of revenue​

• France: Police raided X’s Paris office on February 3​

• India: Ordered comprehensive review by January 5​

• California: AG sent cease-and-desist demanding halt to CSAM generation​

• Malaysia & Indonesia: Temporary suspensions​

and Musk’s response? He posted Grok-generated images of himself in a bikini with laughing-crying emojis.​ (:?)

Here’s the uncomfortable data point: The controversy did nothing to hurt growth. In the days following the scandal:

• Grok app downloads: +54%​

• X app downloads: +25%​

A content moderation expert told CNBC that xAI had failed to build even “entry-level trust and safety layers” into Grok.​ And the market still rewarded it.

Founder insight?

when regulators respond with broad interventions, the EU’s Digital Services Act, UK’s Online Safety Act, California’s AI legislation, all those interventions won’t be surgically targeted at xAI. They’ll hit the entire sector.

The deepfake crisis that did nothing to slow Musk down. Now the question is: what do you actually do about it?

5. The Enterprise Consolidation Wave

Here’s what’s happening in the market right now that matters for founders.

VCs are predicting enterprise AI spending will rise in 2026, but concentrate on fewer vendors. The “let’s try everything” phase is pretty muchn over. CIOs are rationalizing their AI portfolios, moving from scattered experiments to concentrated deployment.

What this means? The result is bifurcation. a small number of vendors will capture disproportionate market share, while funding for others sharply declines.​

Startups that survive will have:

• Strong differentiation (not “another LLM wrapper”)

• Proprietary data (something the giants can’t scrape or buy)

• Deep domain expertise (healthcare, legal, finance—places where general AI isn’t good enough)

• Compliance-first architecture (enterprise buyers increasingly demand this)

The squeeze looks something like this:

From above: Vertical integration from giants like Musk’s empire

From below: Enterprise buyers consolidating around 2-3 proven vendors

The middle: Everyone competing on “better prompts” gets crushed

What founders’ can learn? - The era of “general-purpose AI startup” is ending. What survives is specificity. You need defensible differentiation that can’t be commoditized when GPT-5 or Grok-3 drops.

If your pitch is “AI for X” where X is a broad category, you’re building on quicksand.

6. What To Do About It?

Stop watching the valuation headlines. Start thinking about positioning. Here’s your playbook:

This Week

• Map your dependencies.

  • List every provider for training, inference, and distribution.

  • Single-vendor reliance = single point of failure.

  • Grade each: Green (diversified), Yellow (backup exists), Red (single point of failure).

• Stress-test your moat.

  • Ask yourself: “If Grok-3 launches tomorrow with 10x our capabilities, what do we still own that they can’t replicate?” If you can’t answer in one sentence, you have a positioning problem.

This Month

• Identify your white space. Vertical integration creates gaps. Where is Musk’s stack optimized for scale but weak on depth?

  • Enterprise compliance (healthcare, finance, legal)

  • Domain-specific accuracy (manufacturing, supply chain, pharma R&D)

  • Regulated industries (government, defense, critical infrastructure)

  • Infrastructure tooling (observability, security, governance)

• Audit your trust architecture. Can you prove to an enterprise buyer that your AI is safe, explainable, and auditable? If not, start building:

  • Audit trails (who generated what, when)

  • Explainability (how did the model reach this conclusion)

  • Safety by design (not “move fast and add guardrails later”)

  • Third-party certifications (SOC 2, ISO 27001, industry-specific)

This Quarter

• Watch the regulatory calendar. EU DSA enforcement is active. UK Of-com investigation is underway. California’s AG is issuing cease-and-desists. Assume compliance bar is rising across all jurisdictions. Build for it now—make regulatory readiness a sales advantage, not a fire drill.

• Get specific or get squeezed. The generalist window is closing.

  • Pick a vertical.

  • Build proprietary data.

  • Go deeper than any foundation model can.

  • Ask: What do we know about our customers’ problems that would take a new entrant 2+ years to learn?

The Bottom Line

The SpaceX-xAI merger isn’t just the biggest deal in history. It’s a thesis statement about where AI is going which is infrastructure beating algorithms.

Musk now controls:

• Data: X’s 500M+ users generating real-time feedback

• Compute: 200,000 GPUs in the world’s largest training system

• Energy: Tesla Megapacks powering it all

• Distribution: 1.6M Tesla vehicles, 9.2M Starlink subscribers

• Robotics: Grok powering Optimus humanoid robots

• Government: $939M+ in defense contracts

For founders, this isn’t a reason to panic. It’s a reason to get surgical.

The trillion‑dollar AI giants are here, and they own everything from chips, to power, data, and pipes to reach everyone. The real race isn’t to build a “better chatbot” anymore, it’s to own something they can’t copy: a specific market, trusted relationships, and data they’ll never touch.

You don’t have to beat Musk either, So a better question to ask yourself -

If all the best AI models became free tomorrow, what would your users still desperately need you for?​

What’s Next?

I’ve had about two dozen emails about OpenClaw and Moltbook just in the last weekend, and LinkedIn is flooded with them too. I’ve already done a deep dive, So I am not not paying attention instead I thought I’d wait a bit for the hype to settle and the numbers to stabilize so we have real usage data. so coming up next, I’ll share both a written deep dive and a podcast breaking down what this new wave of autonomous AI agents actually means from both developers and founders’ perspective.

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The 2025 Reality Check:

• Burn Rate: AI startups spent capital 2x faster than previous SaaS generations.​

• The Survival Gap: While 95% of pilots failed, a “5% Club” achieved 300%+ ROI.​

• The Moat Myth: Model performance increased 4x, but startup defensibility dropped to near-zero.

If your 2026 resolution is to “move faster” than you did last year, you’ve already lost. In 2025, we learned the hardest possible lesson » Speed isn’t a moat when everyone is fast, it’s just a very expensive way to fail.

Last week, we picked through the $260 billion wreckage of that “speed-first” era - a brutal 95% pilot failure rate that left many founders feeling a heavy sense of “AI fatigue.”​ But today, we’re done looking at the smoke. We’re building the vehicle to drive past it. This isn’t just a retrospective; we are installing a new AI Operating System for 2026.

⚡ What’s Inside:

• The 2025 Stress Test: Why so many AI initiatives hit the “Deployment Wall” and how major bets burned $680M+ by solving the wrong problems.​

• The ROI Gap / Money-trap: Why AI models got 91% cheaper, but 84% of companies still struggled to find a profit margin.​

• The “High Performer” Secret: Why the “best model” is now a liability, and how to build a “Workflow Anchor” your customers can’t live without.​

• The AI Profit Filter: Five questions to vet an AI idea in 60 seconds before it consumes $500K of your runway.​

• The 6-Week Playbook: The exact timeline the “5% Club” uses to move from a raw idea to measurable ROI.​

• The Case Studies: 10 deconstructed bets to learn from and 5 real-world success stories to copy.

1. The 2025 Stress Test - ( The Model vs Workflow plus Why the $680M Bets Broke)

To build the 2026 OS, we must first need to deconstruct why 2025’s biggest bets hit a wall. Every one of these ten failures below repeated the same core mistake » They prioritized “The Model” over “The Workflow.” They focused on the ‘Brain’ (intelligence) but ignored the ‘Nervous System’ add more to that, how that intelligence actually connects to a daily habit? Here is the full breakdown:

1. Builder.ai:

   1. They prioritized the output of a model that didn’t exist yet.

   2. By using offshore humans to hide AI flaws, they built a ‘Model’ that had the unit economics of a service agency, not a software company.​

   3. Burned $445M before bankruptcy.​

2. Humane: A $241M wearable AI pin that faced a massive adoption barrier because it was a solution in search of a problem. why?

   1. built a ‘Brain’ you could wear, but forgot the ‘Workflow.’

   2. assumed people would change their daily habits (talking to a pin) just to access AI, rather than embedding the AI into the habits people already had (the smartphone)

   3. Sold for less than half its funding.​

3. Noogata:

   1. They built impressive ‘Intelligence’ dashboards that provided ‘Wow’ moments for CEOs, but they failed to anchor that data into a specific, high-frequency workflow that a line manager couldn’t live without.​

   2. Landed giants like PepsiCo but hit the “Deployment Wall.”

   3. had logos, but no production revenue, proving that “insights” without ROI are easy to cut.​

4. HuggingChat:

   1. chased the ‘Model Interface’ (Chat) because it was the hyped workflow of 2024, instead of leaning into their true workflow anchor, the developer infrastructure that powers the rest of the industry.​

   2. Faced a Moat Distraction by trying to compete on the consumer front-end with OpenAI instead of defending their infrastructure dominance.​

5. Yara AI: In high-stakes industries, the ‘how’ of safety is more important than the ‘what’ of the AI’s answer.​ not in case of Yara though!

   1. A Compliance Dead-End.

   2. They prioritized a ‘Mental Health Model’ without solving for the ‘Regulatory Workflow.’

   3. The risk of AI hallucinating mental health advice made liability insurance cost-prohibitive.​

No. 6-10 ( The Silent Majority)

6. CodeParrot: Suffered from Pivot Exhaustion in a market dominated by incumbents like GitHub Copilot.​

7. Locale.ai: Hit an Invisible Ceiling in a niche market that couldn’t support the escape velocity VCs demand.​

8. Subtl.ai: Technically superior RAG but hit the Deployment Wall by trying to solve too many industry problems at once.​

9. Tune AI: Suffered from Platform Delusion, attempting to build middle-ware that the cloud giants (AWS/Google) eventually gave away for free.​

10. Astra: A Silent Shutdown - representative of the invisible majority that ran out of runway without finding a workflow anchor.​

These are just 10 examples but in 2025, this story played out over 1,000 times. Companies that raised millions, hired brilliant engineers, and landed impressive customer logos... all gone. The total damage? $260 billion in burned venture capital.

So when we look closely at cases we see that these weren’t failures of intelligence, but failures of incentive alignment. Startups like Builder.ai fell into the Fake-Scale Trap because their business model demanded the margins of software, but their technology only offered the output of a service. They used venture capital to subsidize human labor, hoping the AI would ‘catch up’ before the cash ran out. Well, It didn’t.​

Similarly, the Pilot Purgatory seen at companies like Noogata happened because founders focused on ‘the demo’, the magical moment when a CEO sees a dashboard and says ‘wow.’ But ‘wow’ doesn’t pay the bills. Without a Workflow Anchor that solves a boring, high-frequency problem, these pilots were treated like ornaments which are just plain nice to look at, but the first thing to be packed away when the economic weather turned cold.

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2. Is "Cheap" AI is Bleeding You Dry? - The ROI Gap

GPT-4 token pricing dropped 91% from $0.03 to $0.0025 per 1K tokens, yet 84% of companies saw their margins erode.

In 2025, something remarkable happened: model costs dropped 91%, but companies still bled cash. GPT-4's input token pricing fell from $0.03 per 1,000 tokens in Q1 2023 to just $0.0025 in Q1 2025 » a staggering 91.7% reduction. Meanwhile, 84% of enterprises reported gross margin erosion of 6% or more due to AI infrastructure costs. This created a "Financial Reality Gap" that swallowed even the most hyped unicorns.

So if the models got cheaper, where'd all the money go? or is going?

Where Your $1M AI Budget Actually Goes

• Talent (45% = $450K): Senior AI engineers cost $250K-$400K/year.​

• Infrastructure (25% = $250K): GPUs are the new rent. Running 50 H100s for a month = $126,000.​

• Data Pipeline (15% = $150K): Fixing the “data swamp” so AI actually works.​

• Model APIs (8% = $80K): The part everyone thinks is expensive, but it’s the smallest line item.​

• Monitoring (7% = $70K): Drift detection and logging often consume more compute than the models.​

The CEO FOMO Tax

The 30% cost overrun that happens when boards demand “an AI strategy” after seeing a cool demo at a conference, without understanding unit economics.​

This tax shows up as:

• Hiring ML engineers before you have clean data

• Buying GPU credits before you know what you’re building

• Launching pilots without ROI metrics

• Building custom models when an API call would work

The 30% Underestimation Problem

IDC predicts that Global 1,000 companies will underestimate their AI infrastructure costs by 30% through 2027. When you budget $1M for an AI project, the real cost is $1.3M, and 80% of enterprises miss their forecasts by more than 25%.​ This is why 84% of companies report gross margin erosion of 6% or higher tied to AI workloads. The token line got cheaper, but the system stayed expensive and that’s the part everyone underestimates.

The Capital Trap and Why You Can’t Afford to Guess

in 2025, 33% of all the VC money went to just three companies: OpenAI, Anthropic, and xAI.

The Funding Pyramid:

• Top 3 Mega-Deals: $63 Billion (OpenAI, Anthropic, xAI)​

• Other AI Unicorns: $50 Billion

• The Rest (1,200+ companies): $39.7 Billion​

This extreme concentration means VCs now demand $1M+ ARR for Series A (up from $500K). You no longer have the luxury of a 6-month “experiment.” In 2026, you either find ROI in 6 weeks or you run out of runway before the next fundraise even starts.

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3. The “High Performer” Secret

The most dangerous delusion in 2026 is believing that a ‘better model’ equals a better business. We are entering the era of Intelligence Parity, where the gap between the best proprietary model and the cheapest open-source alternative is narrowing every month.​

If you compete on the Model Layer, you are fighting a war of attrition against giants with infinite GPUs. But if you compete on the Workflow Layer, you are building a ‘Nervous System’ for your customer.

A better model is just a brain transplant. it’s relatively easy to swap. A better workflow is a transplant of the spine and the habits of the team. That is where true defensibility lives. High performers in 2025 realized that a ‘good enough’ model embedded in a critical daily habit beats a ‘perfect’ model that lives in a separate browser tab every single time.

3 Reasons the “Best Model” Will Kill You

The better the underlying model becomes, the less valuable your “AI layer” becomes. If your value is “high accuracy,” you are one OpenAI update away from obsolescence.​

This is why the 5% who won didn’t obsess over “the best model”, they obsessed over the most boring integration.​

1. The “Better-is-Worse” Switching Cost: Custom-tuned models lock you into old infrastructure. When GPT-5 drops, competitors switch in a weekend. You spend six months re-tuning while your margins evaporate.​

2. The “Intelligence Floor”: Expert-level intelligence is now an API call for everyone. “Our AI is smarter” is no longer a moat, it’s table stakes.​

3. The “Model Burn Tax”: While the 5% used “good enough” models to power Workflow Anchors, the 95% spent their Series A on Model R&D.​

The Spiky Take for 2026

Stop hiring ML Researchers. Start hiring Workflow Architects.

If you can replace your underlying AI model in 48 hours without the customer noticing, you have a Platform. If replacing the model breaks your entire business, you have a Technical Debt Trap.​

Examples - Just What “Workflow Anchors” Actually Look Like?

1. Grammarly: Started as a “spell-check wrapper” but embedded into Gmail, Google Docs, Slack, and every text input on the web. Removing it breaks users’ daily writing flow.​

2. Cursor: Not just “ChatGPT for code.” It learns your specific codebase, your team’s coding patterns, and your IDE shortcuts. Switching back to VS Code + ChatGPT feels like downgrading.​

3. Notion AI: Embedded in the documents where teams already live. It's not a separate "AI tool"it's invisible infrastructure in your existing workflow.

4. The 60-Second AI Pilot Decision Tree / The “AI Profit Filter” Matrix

Read more

In 2025, AI captured 53% of all global venture capital, sucking in $259.7 billion while the non-AI startups fought over table scraps, literally. And the race to AI-adoption - almost everyone tried. 95% of companies that launched AI pilots failed to see any meaningful return on investment, and its not just “struggled”, they failed completely. The numbers seen throughout this year have been brutal.

I remember calling 2025 a wildebeest stampede during MWC , but the fact that this was the year AI’s promises collided head-on with operational reality, isn’t AI’s fault really. This wasn’t just another tech cycle if some of us thought it was, learning the lessons hard- way, but before we course correct for 2026, we need to clean up the wreckage that is everywhere.​

2025 reivew

Now that I am over the headlines and bigger picture, ( read my last post 👆 ) I want to focus on to some concrete numbers that can directly impact me and you, the founders/leaders. If you’re planning to build, scale, or invest in AI in 2026, this isn’t optional reading. These are expensive lessons written in hundreds of millions of dollars of burned capital, thousands of layoffs, and the corpses of once-promising unicorns. So let me walk you through what went wrong and more importantly, what you should never repeat.

Lesson 1: The AI Wrapper Death March

An “AI wrapper” is a startup that builds a simple interface on top of someone else’s AI model (like OpenAI’s GPT or Anthropic’s Claude). These companies don’t train their own AI or own proprietary data, they just make it prettier or easier to use for a specific task. The problem? When the underlying AI companies improve their own interfaces or add new features, wrappers become obsolete overnight.

So What happened: here’s some real examples of the carnage.

Builder.ai

Builder.ai was the poster child for this disaster. Once valued at $1.5 billion and backed by Microsoft, Qatar Investment Authority, and top-tier VCs, the company promised to make app development “as easy as ordering a pizza” using an AI assistant named “Natasha”.​

The reality however was far messier. Investigations revealed that Builder.ai had hired 700 engineers in India to manually handle work that was supposedly being done by AI. The company’s AI capabilities were vastly overstated, revenues were allegedly inflated through fake transactions, and by mid-2025, Builder.ai filed for bankruptcy across multiple countries. The founder stepped down, creditors seized $37 million from company accounts, and more than 1,000 employees lost their jobs.​

Other casualties of 2025 include:​

• Humane: Raised $241M for an AI wearable “Pin” that reviewers called “bad at almost everything it does.” Shut down in February 2025, selling assets to HP for $116M, less than half what investors put in​

• Noogata: Enterprise AI analytics with $28M in funding and marquee customers like PepsiCo. Couldn’t scale beyond pilots, missed milestones, and shut down​

• CodeParrot (YC W23): AI tool to convert designs into code. MRR peaked at $1,500, pivoted repeatedly, then shut down when founders couldn’t find product-market fit​.

• Tune AI: Built tools for fine-tuning LLMs, but cloud providers built similar features for FREE. Infrastructure costs stayed high while margins disappeared​

• Locale.ai: Geospatial AI with paying customers, but founder burnout led to principled shutdown.

Why This Matters for 2026?

AI startups now fall into two categories:

• infrastructure players (who build the models and own the tech stack) and

• application-layer wrappers (who build on top).

VCs know wrappers are risky, so they’re going to be harder to fund going forward.​ While the infrastructure companies may fail as well, but less frequently, and when they do fail, they’ve raised roughly twice the capital of wrapper companies.

The Mistake here: Building shallow applications on AI you don’t control, with no proprietary data advantage or deep workflow integration.​

The Lesson: If you’re building on someone else’s models in 2026, you need one of three defensibility moats:

1. have proprietary data that makes your AI better,

2. do such deep integration into customer workflows that ripping you out is painful, or

3. network effects - that get stronger as more people use your product. Without at least one, you’re building a time bomb. PS - thats every time I hear about lovable 🧐 however specialized their AI agent what happens "vibe coding" could be commoditized?

$100M Plateau

Lesson 2: The Pilot Purgatory Problem - where 90% of AI initiatives get stuck

What happened: Companies launched hundreds of AI “experiments” that looked great in demos but died the moment they tried to scale them across the real organization.

The Numbers -

• 150 leader interviews, 350 employee surveys, and 300 public AI deployments​ - 95% of gen AI pilots failed to deliver measurable returns, according to MIT research.

• 80% of all AI projects fail, nearly double the failure rate of non-AI IT projects (RAND)​

• 42% of AI initiatives were completely scrapped in 2025, sharply up from just 17% six months earlier (S&P Global)​

• Despite 88% of companies now using AI in at least one function, only 39% report any EBIT impact, and most of those say it’s less than 5%​

AI Hype Reality Check

Why Pilots Fail?

The AI adoption pipeline shows dramatic attrition: 67% of companies remain in experimentation or pilot phases, while only 10% achieve enterprise-wide scaling . A LinkedIn analysis by transformation consultants identified seven structural blockers that kill AI pilots between demo and deployment. - here, lets look at each of ‘em in detail!

1. nobody owns ROI

• Accountability Vacuum (88% of failures)

• No single person owns the P&L outcome but only the demos

• Innovation teams build it, IT maintains it, business uses it, but nobody owns ROI

• When leadership changes or budgets tighten, funding evaporates because there’s no champion​

2. Data Infrastructure Gap (78%)

• Data pipelines break constantly, causing model failures and destroying trust

• No single source of truth, sales, finance, and operations - all have different definitions for the same metrics

• and this is the most crucial. > Legacy systems can’t support real-time AI. they were never built for batch processing overnight, so not instant responses​ obviously.

3. Integration Nightmare (82%)

• APIs are brittle, undocumented, rate-limited, or tied to the ancient monolithic systems

• No clear handoff between proof-of-concept teams (who built the pilot) and production IT (who have to run it 24/7)

• Retraining and model maintenance ownership is unclear, so model performance silently decays​

4. Governance & Compliance Void (71%)

• No model versioning, drift monitoring, or audit trails

• Compliance teams block deployment because required controls aren’t in place

• “Shadow AI” proliferates, which means employees use ChatGPT and other external tools without oversight, creating security and IP risks​

5. Organizational Resistance (76%)

• Frontline workers don’t trust AI-driven decisions, so they ignore or override them

• Fear of job loss drives quiet sabotage

• “Change management” consists of posters and workshops instead of real workflow redesign and incentive changes​

6. Cost & Scalability Shock (80%)

• What cost $5,000/month in a pilot suddenly costs $50,000/month in production

• GPU, inference, and storage costs blow past projections

• What worked for 100 users breaks completely when rolled out to 10,000 users across different regions​

7. Strategic Misalignment (88%)

• Executives demand results in weeks, but organizational reality requires years

• Every department wants AI, but there’s no enterprise-level prioritization

• Use cases chosen for “coolness factor” instead of actual business impact

• CFOs cut funding when teams can’t explain where money is made or saved​

Generic Tools Can’t Learn

MIT researchers discovered why ChatGPT works brilliantly for individuals but fails in enterprises: it doesn’t learn from your workflows.​ When you use ChatGPT personally,

‘you tolerate its quirks because you’re the one adapting. You rewrite prompts, ignore bad answers, and work around its limitations.’

But in a company with 5,000 employees, you can’t have everyone adapting differently. The AI needs to adapt to your specific workflows, and generic chatbots can’t do that.​

The successful 5% build custom systems that:

• Remember context from previous interactions

• Learn from corrections and feedback

• Integrate into existing tools (not separate dashboards)

• Flag uncertainty instead of confidently hallucinating wrong answers​

The False Positive Problem

If its the fomo, or something else, Companies are so desperate to “do AI”. there is just so much demand from enterprise companies to try the latest and greatest AI, sometimes there’s false positives of product market fit. You can get a lot of revenue with not having true ROI. In another word, you end up with impressive revenue numbers that hide the fact that customers will never renew or expand.

so when the pilot ends, so does the money.

The Mistake: Confusing pilot enthusiasm with product-market fit, and mistaking revenue without ROI for sustainable business.​

The Lesson: If you can’t show that the pilot saved money, made money, or unlocked new revenue, it’s a science experiment, not a product.

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Lesson 3: Follow the Money 💰

What happened: in 2025, AI captured the majority of all VC money for the first time in history, which sounds cool but where did it exactly go? - to just a handful of mega-companies while everyone else starved.

• $259.7 billion out of $490 billion total​ was investment in AI

• This is the first time ever a single technology theme captured the majority of venture dollars​.

• AI’s share hit 62.7% in Q3 2025 in USA.​

• The top three AI deals (Anthropic $13B, xAI $10B, Mistral AI $1.5B) represented more capital than thousands of smaller startups combined​

So while the total deal count dropped 12% year-over-year, average late-stage deal sizes tripled to $1.55 billion, up from $481 million.

Early-stage and seed rounds saw no growth.​

What This Means?

Other than the closed loop for investment that we have discussed in the last couple of posts, VCs are chasing “perceived safety” by betting on bigger on established players (OpenAI, Anthropic, xAI) rather than diversifying across hundreds of risky early-stage bets.

One general partner at a mid-sized VC said bluntly: “In practical terms, there’s simply no compelling new direction left. I find it surprising that many of my peers are still attempting to extract more value from large language models; I’ve moved past that”.

The Infrastructure vs. Application Divide

As infrastructure become more and more a priority, the shift is now from “let’s build another chatbot” to “let’s build the infrastructure that chatbots run on” explains why 62.7% of US VC dollars now go to AI, but most of that still going to late-stage infrastructure plays, so you’re a new startup, you need to -

• Proprietary data moats: You have data nobody else has

• Deep workflow integration: Ripping you out would break critical business processes

• Technical defensibility: You’ve built LLMOps, RAG 2.0, or agentic workflows that are hard to replicate​

The Mistake: Assuming you can raise venture capital for an application-layer AI startup the way you could in 2023-2024.​

The Lesson: If you’re raising in 2026, you need to prove deep technical moats or prove you’re already generating strong unit economics.

The era of “we’ll figure out monetization later” is over for AI application companies. VCs are becoming “discerning”, their word for terrified.​

The 5 AI Shifts

Lesson 4: When the “AI” Was Humans All Along

What happened: well builder.ai happened. and few others too. -

Amazon’s “Just Walk Out” Technology: Amazon installed this system in physical stores, claiming AI used sensors to identify items shoppers picked up and automatically billed them. I actually tried this out, and I am still waiting for my bill to show up. so the system din;t really work. it required around 1,000 workers to manually check almost three-quarters of transactions. Amazon has eventually scrapped the technology.​

Ryanair’s Customer Service Bot: The airline claimed its chatbot was powered by AI, but it actually used simple keyword matching technology from the 1990s, to respond to customer queries.​ nothing surprising here.

The Regulatory Crackdown

few regulatory cases that I could find -

• The SEC settled its first AI-washing cases against investment firms Delphia and Global Predictions for false claims about AI usage​

• The FTC launched “Operation AI Comply” to target companies falsely advertising AI-powered products​

• The EU AI Act began enforcing stricter transparency requirements​

• Gartner analysts evaluated “thousands” of so-called agentic AI products and found only 130 genuinely met the criteria for agentic capabilities​

Some Real-World Consequences

Air Canada was taken to court after its chatbot gave misleading information on bereavement fares, and the court ruled the company was liable for its AI’s mistakes.

This established legal precedent: companies are responsible for what their AI says, even if it hallucinates.​

Why This Happens?

The gap in public knowledge about what AI can actually do creates opportunity for deception. Most people can’t distinguish between:​

• True machine learning

• Simple automation rebranded as “AI-powered”

• Keyword-matching systems from the 1990s labeled as “intelligent”

• Human workers pretending to be AI (the Wizard of Oz approach)

As one analyst put it: “AI washing exploits the gap in public knowledge, making products and services seem more advanced than they are for example smart home appliances in this case and financial planning tools ”.​

The Mistake: Overstating AI capabilities to raise funding or win customers, assuming nobody would look under the hood.​

The Lesson:

1. Build trust through transparency, not hype.​

2. Show your model architecture.

3. Explain your training data.

4. Demonstrate live.

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Lesson 5: What the 5% High Performer did differently

What happened: about 6% of respondents in McKinsey’s survey actually achieved breakthrough results. Yes. and these AI high performers dramatically outpaced average companies across all key metrics, with the largest gap in measurable EBIT impact.

Who Are the High Performers?

as per McKinsey the AI high performers are the organizations achieving:

• At least 5% EBIT improvement directly attributable to AI

• “Significant” qualitative value (faster innovation, better decisions, customer satisfaction)​

The chart below shows that these high performers aren’t just slightly better, they’re 3-10x better across every dimension:

What did they do differently?

Strategic Ambition:

• 75% of high performers aim to use AI to transform their entire business, vs. 20% of average companies​

• 100% have measurable EBIT impact, vs. 0% of average organizations​

Execution Discipline:

• 85% redesign workflows around AI, vs. 30% of others​

• 60% are scaling AI agents across functions, vs. 15% of others​

• 75% have reached the scaling phase, vs. 33% of average companies​

Investment Commitment:

• 35% allocate more than 20% of their digital budgets to AI, vs. 10% of others​

• High performers invest in technology and talent, not just on software licenses​.

The Five Things High Performers Do

1. They Start with Business Outcomes, Not Technology

2. They Fundamentally Redesign Workflows

3. They Build Cross-Functional Execution Pods

4. They Run Purposeful Pilots with Ruthless Discipline

5. They Invest Heavily in Governance and Change Management

The Leadership Difference

• Role model using AI tools themselves​

• Make AI adoption a formal part of performance reviews​

• Attend weekly standup meetings for critical AI projects​

• Publicly celebrate teams that redesign workflows successfully​

The Lesson:

1. Copy the high performer playbook exactly.

2. Don’t innovate on execution. Use their exact frameworks: single-metric pilots, cross-functional pods, ruthless kill criteria, and workflow redesign.

The problem isn’t that execution frameworks don’t exist, it’s that 95% of companies ignore them.

Lesson 6: The AI Job Paradox

What happened: AI drove nearly 55,000 layoffs in the US alone in 2025, while simultaneously creating massive demand for AI talent. So what is really happening, seek high-skill AI expertise and kill the a middle-skill roles.

The Layoff Numbers

According to Challenger, Gray & Christmas:

• AI was directly cited as the reason for 54,883 US job cuts in 2025​

• Total US job cuts hit 1.17 million in 2025 - the highest since the COVID-19 pandemic in 2020​

• October alone saw 153,000 cuts, with November adding 71,000 more​

Who Got Hit Hardest?

Amazon: Cut 14,000 corporate jobs (4% of white-collar staff)

Microsoft: Eliminated approximately 15,000 jobs throughout 2025, including 9,000 in July alone.

Salesforce: 4,000 customer support positions with AI assistance (9k to 4K per the CEO)

IBM: CEO Arvind Krishna told Wall Street that AI chatbots had replaced “several hundred” human resources employees.​

Other Major Cuts:​

• Workday: 8.5% reduction (1,750 jobs)

• Scale AI: 14% cut (200 employees) plus 500 contractors

• CrowdStrike: Cited AI as a factor in layoffs

• Deepwatch: Cut 60-80 employees, citing AI as a factor​

The Hiring While Firing paradox

Despite massive layoffs, most organizations hired for AI-related roles over the past year, and the most in-demand positions were:​

• Software engineers specialized in AI/ML

• Data engineers who can build AI-ready data pipelines

• ML engineers who understand model deployment

• Prompt engineers and AI application developers​

What this signals at is AI eliminating routine work but at the same time creating demand for high-skill technical roles.​

What CEOs Are Actually Saying

Amazon’s Andy Jassy (June 2025): “AI will lead to fewer individuals in some current roles and more individuals taking on different types of jobs”.​

Amazon’s Beth Galetti (October 2025): “This generation of AI represents the most transformative technology since the Internet... A leaner organization with fewer layers is necessary for us to act swiftly”.​

Salesforce’s Marc Benioff (September 2025): On cutting 4,000 support roles with AI: “I require fewer personnel”.​

The Workforce Size Debate

McKinsey’s survey shows divided expectations on AI’s workforce impact:

• 32% of leaders expect workforce reductions of 3% or more in the next year

• 43% expect no change in total headcount

• 13% expect increases of 3% or more​

The Reality:

• Middle-management coordination roles shrink as AI handles workflow orchestration.​

• Routine, repetitive roles disappear.

• High-skill technical and strategic roles expand.

The Mistake: Assuming your current workforce can simply “learn AI” without major retraining investment.​

The Lesson:

1. If you’re building AI products, factor in massive displacement anxiety among potential customers’ workforces.

2. Transparently retrain displaced workers for new roles and involving frontline staff in redesign, not just automating them away.​

SO a quick recap for the 2026 Playbook: 5 Things Not to Do

1. Don’t Build AI Wrappers Without Deep Moats

2. Don’t Confuse Pilot Revenue with Product-Market Fit

3. Don’t Skip the Seven Structural Gaps

4. Don’t AI-Wash Your Capabilities

5. Don’t Underinvest in Talent and Governance

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What to Do Instead: The High Performer Formula

Very simple do what the 5% do and this time copy-paste works!

1. Start with business outcomes, not technology: Define the dollar metric you’re moving before writing a line of code​

2. Build cross-functional pods with end-to-end ownership: No handoffs between innovation and operations​

3. Run 6-10 week pilots with single KPIs and kill criteria: Ruthlessly stop anything that doesn’t move the metric​

4. Redesign workflows from scratch with AI at the center: Don’t add AI to broken processes​

5. Invest in governance, change management, and talent: These aren’t overhead they’re the difference between 5% success and 95% failure​

👉 In the next post, lets expand on the how of this.

Conclusion: The Expensive Education

2026 won’t reward optimism. It will reward execution.

2025 was the year the AI market grew up. The unicorns built on hype filed for bankruptcy. The pilots that should have become products stayed experiments. More than half the money flooded in to AI went to giants.

But what we learned that AI success isn’t about model capabilities, it’s about operational excellence. building deep moats, and running disciplined pilots could get you the ROI expected.

We learned that the gap between pilot and production isn’t 6 months of work, it’s 3-5 years of organizational operating system upgrades most enterprises haven’t even started yet. The companies that succeed will be those that learned from 2025’s expensive mistakes and copied the high performer playbook exactly.​

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Reviews shouldn’t be hard, but I have been trying to write this one for past 4 days. I dint’ want it to be another recap, or trends, or predictions, and for that matter, even a conclusion of sort because I don’t see any sort of closure at the moment, because nothing about this year with AI feels neatly wrapped up. there isn’t even a proper holiday slowdown. Very likely - Everyone’s quietly hoping nothing huge drops in the next couple of weeks, but let’s be honest: we have no idea if another DeepSeek‑R1‑style shock is already queued up for January, but even if it does, 2026 does seem like its going to be FUN.

Here’s what we’re covering in this post:

1. A quick quarterly update of 2025.

   • What happened ie the big events ( no hyped headlines here) ,

   • what was the actual impact on us and

   • the market shift as such.

2. The actual Gen/Agentic implementation data that we have ( hype vs reality)

3. 5 stages of AI evolution ( or revolution) - why this is important? -

   • to understand where we stand right now!

   • why this cognitive industrial revolution is different?

   • how many critical thresholds have we past, and

   • how the conversation is shifted, and going where exactly.

4. Impact on the industries with little extra focus on telcos.

5. Any surprise in 2026? Wildcards?

6. 2026 playbook for Big Cs ( what could happen behind closed doors? ) and

7. if we can plan a strategic layer for us!

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1- 2025 Quick Quarterly Update

if you couldn’t keep up with AI all this year, or were too busy switching tools I wouldn’t blame you, half my year went in to trying to figure out my tool stack, and other half developing those, substack was a late discovery, but now that I have been somewhat consistent, I think I can keep up with both AI developments and the aftermaths in 2026. In one line, 2025 was an year of experimentation, and if we failed in for example enterprise projects, or Agentic AI implementation, It’s all for good and those were the necessary toddler steps. 2026 seems less hyped ( we may not see bombshell headlines or benchmarks anymore) , much more stable and the year where we can record good progress on all fronts.

The real story of AI adoption

Q1 2025 – “The Oh, this AI just got cheap Moment”

• DeepSeek R1 blows up the frontier, shattering “US‑only, $10B” myths. the Chinese open model showed o1‑class reasoning at a fraction of US big‑tech cost, proving world‑class AI isn’t a Western monopoly.

• Governments moved from talk to law.
  The EU AI Act actually started, US states and India drafted rules, and any serious rollout suddenly needed risk tiers, documentation and a real governance story, and not just a cool demo.​

Q2 2025 – “From chat to actual work”

for this actually ‘Chat to Act’ sounds much better, but thats been the story of all of 2025 and not just Q2.

• Reasoning models and agents got real.
  Labs shipped systems that plan, tool‑call and self‑critique, shifting the bar from “nice chat interface” to “can this agent actually close the loop on work for me?”.​

• Multimodal AI becomes default UX.
  Models now read docs, images, audio and video, so products move from text boxes to “upload, point, talk,” and users start expecting AI in every surface they touch.​

  Multimodel AI

Q3 2025 – “From experiment to infrastructure”

• AI goes from pilot to platform.
  Enterprises embed AI into cloud, security, support and analytics; budgets flip from scattered POCs to long‑term platform, infra and M&A bets.​ It started it “Add AI anywhere you can”

• Agent fleets get funded.
  Companies pay for fleets of workflow agents tied to their data and tools, not single chatbots, pushing money toward orchestration, observability and safety.​

and this wasn’t the start of ‘Add AI anywhere you can’ this is where it went full blown. then followed a flood of reports on failed enterprise AI projects, and right now we have few very similar one on Agents, which I’ll try to link later in this article.

( Note - agentic browsers also pending a deep-dive, I’ll try to get that done in January sometime. )

Q4 2025 – “Agentic model arms race”

• 25 day model storm.
  Not sure if it was an ode to 2025, but Grok 4.1, Gemini 3, Claude 4.5 and GPT‑5.2 all landed within weeks, each claiming a “best‑in‑class” niche, so smart teams start portfolio‑routing workloads across models instead of locking into one.​ the flip side however was that people ( including me ) are somewhat tired of benchmarks and rightfully so. good thing its end of 2025 and we don’t have to pick just one anymore.

• AI becomes an economy story, not a gadget story.
  “Year in AI” coverage fixates on jobs, power, competition and regulation, recasting AI as a structural shift every geography and sector has to price into its plans.​ Hopefully everyone has made up their mind and we won’t see headlines such as ‘AI will take our jobs’ or ‘AI will eat the humanity’ anymore.

Note - there was a report from IEA on Energy consumption however, that din’t bother many people ‘yet’ - as AI gets more physical in 2026, hopefully we have more focus on sustainability implementation and by that I mean, not just conference talks.

Energy & AI

Global takeaway – “Every geography is now in the game”

• China proves it can set the pace.
  DeepSeek resets cost and capability expectations, showing that frontier‑class AI and its shocks - can now originate outside the usual US labs.​

• EU sets the regulatory bar.
  The AI Act becomes the de‑facto reference for “serious” AI governance that other regions watch, copy or react against.​

• India and others race to localize.
  Big emerging markets push toward their own AI rules and ecosystems instead of waiting passively for US or EU defaults.​

Note - I would like to highlight the ASPI’s Critical technology report here if you’d like to expand the understanding of whats been going on, as none of the above should be a surprise. link to the report here -

ASPI’s Report Aug 2024

2- 2025 hype vs reality: What actually happened in implementation

• Most enterprises tried something: 70–80% of firms say they used GenAI in at least one function by late 2025, and over 80% of leaders reported using GenAI weekly at work.​

• Very few felt “done”: only about 1% of companies described themselves as fully mature on AI, even though over 90% planned to keep increasing AI spend.​

• Lots of pilots project, but shaky scaling: multiple surveys suggested roughly 40–50% of AI projects were delayed, underperforming or scrapped before production, mostly because of bad data, weak integration and unclear use‑cases.​

• Failure brought lot of noise, but wasn’t fully measured: headlines like “75–95% of AI projects fail” came from narrow samples, but they did reflect a real pattern: enterprises flooded the zone with GenAI pilots and only a minority became durable, ROI‑positive systems.​

we need to however note that all of these are survey‑based estimates, not a global census: different studies use different samples and methods, so the safe takeaway is directionally clear we are at the stage where

- adoption is broad, true success is still rare.

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Also AI hype in 2025 wasn’t just about the implementation side but AI investment, evaluation and VC psychology that I have largely covered in few post including below.

AI Gold Rush 2.0

3- 5 stages of AI ‘r’evolution

To understand where we actually stand right now, its actually important to first understand the pace both AI and of 2025.

It took 60 years for the steam engine to transform global manufacturing. It took the internet 20 years to reshape global commerce. AI is compressing that same level of structural change into 5 years.

The Critical Difference: Speed.

Every major economic leap in human history has been about automation:

1. The Steam Revolution (1780s): Automated Muscle.

2. The Information Revolution (1990s): Automated Distribution.

3. The AI Revolution (2020s): Automating Cognition.

The diagram above and data tell a clear story, but they miss the philosophical weight of what is actually happening. We are not just upgrading our software. We are upgrading the engine of the economy itself.

OpenAI’s official framework as of Dec 2025 defines 5 levels of AI:

• Level 1: Chatbots (Completed: GPT-3/4)

• Level 2: Reasoners (Completed: o1/GPT-5)

• Level 3: Agents (Current Frontier: ChatGPT Agent/Claude Computer Use)

• Level 4: Innovators (The New “Next”)

• Level 5: Organizations (Future)​

If we call these AI stages, this is what it would look like this diagram below.

Level 1: Generative AI (The Legacy / Foundation)

• Era: 2023-2024

• Role: The Assistant

• Status: Foundational / Commoditized

• What it does: Creates content. You prompt, it writes. It was revolutionary then; it is table stakes now.

Level 2: Reasoning AI (The Standard)

• Era: 2025 (Now)

• Role: The Thinker

• Status: Current Standard

• What it does: Solves problems. With 78% of enterprises using reasoning models (like OpenAI’s o1), “intelligence” is now a utility.

Level 3: Agentic AI (The Shift)

• Era: 2026 (The Next Frontier)

• Role: The Worker

• Status: Production Scale

• What it does: Executes actions. This is the crossover point where AI stops waiting for humans and starts running workflows autonomously. This is where the 171% ROI lives.

Level 4: Innovator AI (The Breakthrough)

• Era: 2027

• Role: The Inventor

• Status: Emerging

• What it does: Discovers new knowledge. AI that invents new drugs, designs new materials, and solves engineering paradoxes.

Level 5: Organizational AI (The Vision)

• Era: 2028+

• Role: The Orchestrator

• Status: Future Horizon

• What it does: Manages the enterprise. Swarms of specialized agents running entire business units with minimal human intervention.

Where We Actually Stand?

We are currently in Year 3. The “Legacy / Foundational” phase is done. The “Reasoning and Standardization” phase is nearly complete. We are now entering the “Explosion” phase with Agentic AI, where the technology stops being a novelty and becomes the invisible infrastructure of the world.

We’ve already crossed two critical thresholds. Generative AI (Level 1) is now considered legacy technology, a baseline expectation, not the innovation. Reasoning AI (Level 2) is the current standard. And Agentic AI (Level 3)? It’s not “emerging.” It’s live, in production too. Vodafone’s TOBi and Telefónica’s Aura are two examples of Agents working in Production that resolve service issues in real-time, slashing call center costs while boosting customer satisfaction.​ TOBi transformed from a chatbot and Aura are both Level 3 Agents. They are the proof points that Action (Agency) has replaced Conversation (Generation) as the primary value driver in enterprise AI.

So Enterprises are actively orchestrating AI and not just exploring as we have already entered the Reasoning Era. The new “call to action” isn’t just to prepare for agents, but to optimize them for the real world use case. so we’ll go through the stability phase of Agentic AI in 2026. the research paper just released few days ago gives a good overview of where we need the most work.

To give a quick perspective - The research shows that most agents are built to execute plans, not revise them. They assume the world stays stable. While tools work as expected and goals remain valid, once any of that changes, the agent keeps going anyway, confidently making the wrong move over and over.

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4- Impact on the industries / The AI Economy

For most of human history, scaling output required scaling people. Even in the Industrial Revolution, machines amplified muscle, but cognition remained the bottleneck. To manage 10x more factories, you still needed 10x more managers, planners, and analysts. The ‘brain count’ had to scale linearly with the work.

The AI Revolution is the first time we are automating the bottleneck itself: Decision-Making.

This creates a fundamental economic decoupling:

• Generative AI (2023) was an Efficiency Tool: It helped a human do the work faster (1.5x speed). You were still the engine.

• Agentic AI (2026) is a Multiplier: It allows a human to manage the work of infinite digital workers. One person can now orchestrate 10, 50, or 100 autonomous agents. You are no longer the engine; you are the conductor.

This is why the ‘Productivity Gap’ matters. We are shifting from an economy measured in ‘Labor Hours’ to one measured in ‘Compute Outcomes’. The winning companies of 2026 won’t be the ones with the biggest head counts; they will be the ones with the most capable Agent Swarms, which depends on how effectively we can implement the Agentic AI systems, back to my previous point.

The "Chat" phase was horizontal (everyone got an email writer). The "Act" phase is vertical (Telecoms gets network brains; Banks get autonomous auditors). This vertical specialization is where the next trillion dollars of value lies. while there are success stories, one can find in yearly reviews, the “hype vs. reality” gap is still the elephant in the room. so If we only talk about the wins (Vodafone, AT&T), we kind of miss the “Graveyard of Pilots” that defined the past year and half. so I am gonna look a the problems here.

The “2025 Pilot Purgatory”: The Hidden Failure Stats

While the headlines celebrate the top 5% of adopters, the broader market is struggling. We are currently seeing a “K-Shaped” adoption curve: the winners are accelerating, but the majority are stuck.

The Failure Scorecard (2025 Data):

• Pilot Failure Rate: ~80% of AI pilots started in 2024 never reached production scale.

  • Why? Data fragmentation, hallucination risks, and crucially cost overruns.

• The “GenAI Hangover”: 30% of GenAI projects were quietly decommissioned in 2025 because the ROI wasn’t there. (e.g., A chatbot that cost $5 per query but only saved $2 in agent time).

• Telco Specifics: While the networks (Layer 1) succeeded, Customer Service (Layer 2) struggled. Many telcos rolled back “fully autonomous” chatbots after customer satisfaction scores plummeted due to “looping” and lack of empathy.

The “AI Coverage” Reality:

• The “Easy Wins” - Marketing & Coding: 80% Coverage (High Success 90% because the risk is low).

• The “Hard but Valuable” - Core Operations (Network/Finance): a low 30% adoption rate because well, its difficult (High Value, High Risk). but a high success rate (70%) for those who actually manage to deploy it (like the Vodafone/AT&T for example).

• The “Failure Zone” - Customer Facing: a Moderate 40% Coverage (High Failure Rate). This is where the hype crashed into reality, chatbots annoying customers, hallucinating policies, and getting rolled back.

Why They Failed? (The “Last Mile” Problem):

1. Data Quality: You can’t put a Ferrari engine (GPT-5) in a go-kart (legacy SQL database).

2. Governance Paralysis: Legal teams blocked deployments because agents couldn’t explain why they made a decision.

3. Integration Fatigue: It’s easy to build a demo; it’s hard to integrate an agent into a 20-year-old billing system that runs on COBOL.

The companies winning (like Vodafone) didn’t just buy AI; they fixed their data infrastructure first. Mastercard in fraud rebuilt their data foundations, redefined their workflows for agency rather than just automation, and moved past the “chatbot” trap into deep, vertical problem-solving. those falling in to "GenAI Hangover" attempting to plaster new algorithms over old, broken processes, resulted in costly failures and customer backlash.

AI is an accelerator, if your process is bad, AI just makes you fail faster. so while we stay optimistic about Agentic AI in 2026 its important to bring in necessary realism of the implementation challenges as the research earlier rightly presents.

What 2025 is taught us are serious lessons, we need to fix these failures in 2026, which means while undeniably profound then industrial impact of AI, its not going to be even so early and it’ll take a while to see the ROI. The question for every industry leader is no longer “What can AI do?” It is: “Is my infrastructure ready to survive the Agentic Shift?”

they just can’t simply keep funding R&D anymore. hence strategy takes the utmost priority in 2026.

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5- 2026 Wildcards ( The unknown unknown)

Now this part is fun. I am looking at some low-probability but high-impact events that may force us to rewrite the charts. The roadmap we built assumes a smooth exponential curve. The Surprise of 2026 will likely be Friction. which means - Physics pushing back.

• Energy Friction: Data centers consume 6% of US power. In 2026, AI might cause rolling blackouts, forcing a hard cap on compute usage.

• Social Friction: The “Vishing” surge (442%) leads to a “Verify Humanity” movement where people refuse to interact with digital systems entirely, reverting to analog/paper processes for high-trust transactions.

If the system moves too fast and something (markets, grids, or trust) snaps, a good probability, our strategic takeaway would be to be flexible for the break while we prepare for the shift.

Based on the trajectory that we’ve mapped out earlier, where Agentic AI (Level 3) is the new standard and Innovator AI (Level 4) is emerging, here are the four “Wildcard” Surprises perplexity research came up with, that could disrupt the 2026 roadmap.

1. When Agents Talk to Agents

By 2025, AI agents are chatting with APIs. By 2026, they’re chatting with each other, at scale, with no humans watching closely.

Now imagine one finance agent misreads a headline, panics, and sells. Other trading agents see the price drop, assume something bad happened, and all start selling too. It’s like a chain-reaction of algorithmic fear.

• Chance: Medium (30%)

• Impact: A 12-minute crash that erases $1 trillion and forces regulators to say, “Enough—humans must stay in the loop for autonomous finance.”

2. The Robot “iPhone Moment”

People expect truly capable robot helpers later in the decade. But 2026 might jump the line.

Now Think of a “General Purpose Robot Brain” that works on almost any robot body. Overnight, today’s clumsy robots get a software update and suddenly handle real-world tasks with much more independence.

• Chance: Low–Medium (20%)

• Twist: Instead of buying a fancy robot butler in 2030, you just lease a robot body in 2026 and download a “Butler App” to its brain.

3. AI Makes Its Own Super-Data

Experts worry we’ll soon run out of high-quality human data to train the next GPT-style models.

But what if a smarter AI figures out how to create synthetic data that’s actually better than the human stuff? Like AlphaGo learning by playing itself, but now for almost any skill or domain. That would blow past the usual “slow, incremental” progress.

• Chance: Medium (40%)

• Impact: One leading lab could hit a mini‑singularity, improving its models overnight and pulling so far ahead that no one else can realistically catch up.

4. The Internet Splits for AI

Right now, most AI runs on big global clouds like AWS, Azure, or Google Cloud.

But governments are getting nervous about AI being used in cyber‑warfare and spying. They might demand that serious AI systems run only on fully domestic infrastructure. That would fracture the AI internet into regions.

• Chance: High (60%)

• Impact: AI agents in one region can’t freely work with data or services in another. Global “one-brain” coordination dies, and companies are forced to juggle three or more separate regional AIs instead of a single global system.

6- 2026 playbook for Big C’s

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This isn’t some breaking news, or very insightful topic as such. The lists like these of startups joining incubators / accelerators or similar programmes funded by big companies get announced almost everyday in some part of the world. but considering the recent developments in AI, I was particularly curious about the mix, and of the use cases these companies are solving and using Google’s latest models, which is one criteria of course apart for the startups already been generating revenue and backed by VCs or in some cases have some angel investments and at the series A funding level.

Edit - one thing I forgot to add about the AI-first, which here mean having AI at core of the solution, the startup are providing, and since google defined them as is, so I am writing it down but its a tad different from what we discussed in the last post, and what my understanding is, which isn’t just about providing an AI solution but building a company thats truly AI at the core of its business workflow. read it here

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What is Gemini Founders Forum?

The Gemini Founders Forum is a new initiative and an exclusive program by Google for Startups, created to support top Series A, AI-driven startups integrating Google’s Gemini models.
Selected founders join a collaborative summit at Google HQ for hands-on technical mentorship, co-designing custom AI roadmaps with Google and DeepMind experts.
Participants receive major cloud credits, early access to Gemini APIs, and deep peer networking. The forum’s goal is to accelerate breakthrough innovation and position Google’s technology at the heart of next-gen AI companies.

so this is more or less “Google for startup” high tier with technical support, resources, mentorship, and visibility and likely can provide an entry point for a strategic partnership as google doesn’t invest directly otherwise, and of course a trip to the HQ.

What this google’s likely handpicked cohort mainly exemplifies is the ongoing convergence of agentic and generative models, defining the next chapter of AI development. the cohort includes solutions that power everything from healthcare automation and legal workflow AI to generative content and supply chain intelligence. I couldn’t say if their ideas are truly competitive in the $1 trillion global AI market? or if these are trailblazers as many seem like first-movers, but it looks like a good set of varied use cases leveraging Gemini’s state-of-the-art stack, and could be helpful to understand where Agentic AI platform solutions could be more effective and where Gen AI should be the way to go.

Agentic vs. Generative

2 weeks ago Microsoft announced its suite of multimodal AI agent-building tools, not sure if its free but but it means any startup using Google or Open AI or any other models can still connect and work with Microsoft’s system and enterprise-level tools through open standards. Now Open AI has just announced the Agentkit directly competing with Microsoft’s and Google’s agentic platforms, aiming to make production-ready AI agents accessible for all developers and organizations, What it means is, big corporations can easily build their own AI agents instead of buying from startups, and this is where the chart above gets interesting. there are 11 startups in the list that are building some kind of agentic AI platform solution -

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Agentic AI Startups (11):

• Akto: AI agent security solution

• Arintra: Autonomous coding for healthcare

• Baz: Code review automation platform

• Bonsai Robotics: Autonomous farm robots

• Fini: Agentic AI for customer support

• Frenetic AI: Electrical engineer automation

• Genesy: Sales agent SaaS

• InstaLILY AI: Multi-agent workflow automation

• Lexter: Legal operations automation OS

• SpotDraft: Contract automation platform

• Zapia AI: Buyer-seller agent over messaging

Gen AI Startups (5):

• Clarifeye: Gen AI-ready knowledge warehouse for unstructured data

• Darwin AI: Agents for sales/support qualification

• Exactly.ai: Private generative AI models for brand visuals

• Klleon inc: SDK generative digital humans

• PyannoteAI: Speaker diarization for audio/video

All these above developments are happening using Google AI models. with Microsoft now supporting 11,000+ different AI models including googles’ for easier enterprise build the agentic market with, all sort of mix and match, when it comes to who a startup choses to develop their solution with is okay. but it becomes harder to compete - its either specialize or get squeezed out. The key to agentic AI startup success is picking your niche and being amazing at it, not which cloud provider you use.​

What startups should do?

Stop trying to build general AI platforms and instead become the expert in one specific thing - like AI for dentists or AI for warehouse management. Since everything now works together through open protocols, startups don’t need to build everything, just be the best at their specialty.​

Trend / Use cases breakdown

The markets (generative AI, agentic AI, vertical AI platforms) are enormous and fast-growing, so its interesting to look at the use cases, and sectors for strategic partnership and ecosystem building, and what’s Google’s betting on the next wave of AI-driven business models.

Use case breakdown:

• Sales/Customer Support: 8 companies ( b2B sales)

• Legal Tech: 3 companies

• Audio/Video/Image AI: 4 companies

• Automation/Workflow: 2 companies

• General AI Platform/Agent: 1 company

• Generative AI: 3 companies

• Security/Risk/Compliance: 2 companies

• Industry-specific AI: 2 companies

• Social Dynamics: 1 company

Trend breakdown:

• Customer/Enterprise Automation: 13 startups

• AI-powered Platforms: 6 startups

• Industry-specific AI (healthcare, fintech, climate, engineering, media, etc.): 13 startups

• Security/Compliance/Risk: 5 startups

• Generative AI/Models: 6 startups

• Other/Unique Use Cases: 10 startups

Trend suggests the largest focuses are on solutions that automate functions for customers and enterprises, plus innovations for specific industries, while maintaining a significant emphasis on AI-powered platforms, generative models, security, and unique new use cases.

Use cases

Here are all the unique use cases represented by the 53 companies in the Gemini Founders Forum cohort:

• AI-powered Security Solutions: Agentic security for enterprise applications, predictive cyber threat identification, supply chain software security, deepfake detection.

• Healthcare AI: Automated clinical coding, fraud/error reduction in healthcare, AI-based veterinary care, genomics for precision oncology, AI-powered clinical trials, brain health neuro-imaging, conversational AI in healthcare, insurance and data-driven care, personalized medicine research acceleration.

• Legal Technology: Regulatory/ethics AI platforms, mass litigation operation automation, AI contract automation.

• Customer/Enterprise Automation: B2B workflow automation, AI-based order entry, digital adoption & instant documentation, customer pain point analysis, agentic customer support, sales support & qualification, multi-agent platforms for service operations.

• Generative AI/Content Creation: Generative AI models for brand-specific visuals, SDKs for digital human avatars, knowledge warehouse for content structuring, speaker diarization in audio/video, content creation platforms for comics and webtoons.

• Education Tech: AI learning assistants linked to school LMS, virtual classroom collaboration platforms.

• Fintech: Treasury management automation, open finance data bureau, trade finance with AI, democratized lending, instant access to financial data, estate planning modernization.

• Climate/Environmental Solutions: Water risk management via SaaS, climate risk analytics for asset management, automated environmental tracking in supply chains.

• Engineering/Construction: AI-powered engineering for power supply design, project risk management, automation of construction takeoffs, vision/AI for farm tractors/robots.

• Media and Entertainment: Music rights transactions powered by AI, digital storytelling for comics/manga/webtoons.

• Restaurant/SMB Tools: Tools to enhance SMB restaurant efficiency and customer experience automation.

• Travel/Logistics: AI insurance and travel care, autonomous outdoor logistics robots.

• Messaging/Communication: AI-powered platforms for buyer-seller connections over messaging, WhatsApp-centric engagement automation, browser extensions for context-aware autocomplete/text generation.

• Spacetech: Satellites and foundational AI for satellite imagery analysis.

The Takeaway

With 11 out of 53 specifically building agentic AI platform solutions, Google’s Gemini Founders Forum cohort is Agentic AI dominated with wide span of use cases with a good mix of Gen AI plus vertical integration. the trend based on the selection however signals AI development for startups depends on deep specialization, not general-purpose platforms. With cloud interoperability and big tech offering modular agent-building tools, startups must become experts in distinct industry domains and solve tangible, specialized problems. Open protocols mean not everything needs to be built from scratch and collaboration and plug-and-play integrations are key.

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Some of the world’s fastest-growing startups are called “AI-first”these days, but what does that actually mean? What does it mean for a founder to built an AI-first startup from the ground up, and how does this approach changes how startups are run.

We’ are comparing this new model with old ways of working, and breaking down the differences through simple charts, to create a practical roadmap for founders looking to place AI at the heart of their business.

What is an AI-First Company?

Being an AI-first company that is leaner, simpler, flatter, equipped with AI-native talents, dreams bigger, and builds faster means redesigning the entire organizational structure with artificial intelligence as the central nervous system, and not just adding AI to existing processes but fundamentally reimagining decision-making, workflows, and talent to maximize speed, creativity, and efficiency.

Instead of adding agents and AI tools here and there, and using AI wherever possible, because everyone else is doing it? from answering emails to planning new products. the founders strategically plan and start by asking: “How can AI help us do this better or faster?” This means artificial intelligence (AI) isn’t just a tool they run everything around, but it helps with big decisions, daily tasks, and how work is done, from top to bottom.

An AI-first company places artificial intelligence at the core of its business, ensuring that every strategic decision, process, and solution is informed and powered by AI. Instead of simply experimenting with tools, these organizations build from the ground up with AI infrastructure, continually asking how AI can automate, improve, or accelerate every activity.

A Leaner, Simpler, and Flatter Organization

This table diagram below presents the big differences between old-style of working and how the new AI-First startups way of working looks like in comparison. The diagram contrasts two ways of building companies: “traditional” versus “AI-First” and how AI-First startups rethink everything, from structure and decision-making to innovation and growth, helping founders see how much their priorities and also results change with an AI-led approach.It also reflect on how AI changes the way teams work, make decisions, and grow.

• Traditional companies are hierarchical and make decisions based on experience, whereas AI-First startups are flat, agile, and rely on data.

• Talent in traditional firms focuses on business, while AI-First companies value tech skills and recruit AI-savvy people.

• Tech strategy in traditionals is slow and IT-led, but in AI-First, it’s driven by AI and rapid innovation.

• Growth and innovation are secondary and occasional in traditionals, but AI-First startups place them at the core, pushing for continuous progress.

By minimizing bureaucracy and reducing layers of management, AI-first organizations can operate with fewer employees and flatter hierarchies which is a must for a startup which are initially built leaner and have fewer roles but extra all around responsibilities. AI can streamline information flow, enable the leaner management and make it more efficient, give employees greater responsibility and autonomy. What happens as a result is that decision speed increases, and teams can move quickly to execute ideas, supported by agile AI systems rather than complex approval chains.

• A recent Harvard Business Review study (2025) found that companies embedding AI into decision-making had “dramatically reduced the cost and time of decisions,” with AI-driven teams reimagining workflows and seeing much faster growth.

• McKinsey’s 2025 research explained that “future-built” AI companies achieve up to 5 times the revenue growth and 3 times the cost reduction compared to firms sticking to traditional models. Rapid, data-driven decision-making is core to this transformation.

• As per another report from BusinessChemistry.org (2025), The AI-first startups move “7x faster in decision speed” and expand their markets “62% quicker” than traditional startups, due to automation and data-driven strategies.

These studies underscore that AI-first companies fundamentally change their pace and impact.

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AI-Native Talents and Simple Infrastructure

AI-native talents are professionals who are deeply skilled at working with AI tools and systems infusing AI into every role, function, and process. The infrastructure is simple and adaptable, often cloud-native, designed for rapid iteration and scalability, with minimal overhead. Its important to understand the AI infrastructure from typical or Network Infrastructure, while still part of the enterprise infrastructure and based with in, the core and supporting the entire infrastructure. AI infrastructure uses special hardware (like GPUs), much bigger data storage, and advanced cloud platforms designed to train, test, and run complex AI models and machine learning tasks. So when you add AI infra, you upgrade your systems to handle large data loads, faster processing, and more automation, making your business able to run AI applications.

Founders don’t need to know everything about coding, but they do need to bring people who feel comfortable working with not just AI tools and understanding of how AI can be integrated with in the enterprise, its stack and the elements. they should understand the why and what tasks needs building Agents or multi-agent systems and where it’ll be useful to implement gen AI use cases instead.

Its also possible that not everyone is keen on adapting AI. Some recent surveys show more than 60% of employees feel anxious or resistant towards new AI tools at work. Therefore, It’ also becomes a challenge of creating a culture where learning and change are normal, and the tech is flexible and easy to update, and people are willing to do it. AI-first companies need to encourage continuous experimentation with AI, instead of continuing with traditional methods.

McKinsey found that such organizations achieve up to 10x productivity gains over traditional firms by encouraging ongoing innovation and making it easy to update and use new tech. Progress happens when teams are supported to try, fail, and learn with AI, showing that a growth mindset and flexible technology are as important as the tools themselves.

Dreaming Bigger and Building Faster

If you’re starting a new company or reimagining an old one, making it AI-first gives you the freedom to move faster, dream bigger, and solve harder problems.

The chart above shows, in simple terms, how that AI-First companies do much better than Traditional ones in every key area like speed, innovation, and growth. For example, AI-First companies like Google and Duolingo use AI to automate tasks, test ideas faster, and personalize products, helping them innovate and expand quickly.
Studies from Deloitte and the UK government found that companies adopting AI see up to 25-30% lower costs and large jumps in revenue and new jobs compared to traditional firms. According to research in ScienceDirect and BCG, AI-First businesses grow sales, market value, and creativity much faster than others, with some achieving multi-million dollar revenues with small teams in months, something traditional companies rarely match.

This massive performance difference seen in the chart, where AI-First companies consistently score at the top, prove that AI is a powerful driver of business success. While these studies quote data from big companies, Small startups can also move quickly by picking one main problem to solve with AI, using flexible tools and cloud solutions that don’t require big budgets.

AI- Driven Business Framework

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On Tuesday August 5 Google launched Gemini Storybook feature and I remember the date because I was immediately hooked, and so while I thought I’d wait until weekend to experiment, I ended spending my next 40 hours just creating storybooks.

For those who don’t know me well yet, I love illustrations, specially pencil and watercolors, I am also a photographer and writer, and have mostly focussed on landscapes and travel. I was always writing, but started blogging using google blogger in 2005 or 6 and was consistently working on it until end of 2015, when my sales job got exceptionally demanding, and I had no time to write anymore. there was also a shift in keeping your experience personal or so. so the love for storytelling is nothing new, and it made perfect sense that I’d jump on the opportunity to create some cute storybooks even on a hump day!

My first Gemini storybook

On Wednesday morning, I created my first storybook, my first thought, AI, my second a woman, and my third, well adventure. - none of these three are alien to me, because I live an breathe them every day. I called her AIRA ( my niece’s name) I wanted to give her a fun, quirky adventure! and so I did.

the prompt was simple, unstructured. the first looked a bit sad, but I got some perfect illustrations, ( One of my favorite above) and second was one hilarious tale. ( in the youtube video) and this is exactly how this story of about 2 dozens of storybook or rather the AI storytelling experiment began.

Edit - in my flow I forgot to mention. this is how you access the app. its integrated with in gemini chat window. you can follow the URL or select the storybook in Gemini chat sidebar.

In the beginning there was chaos -

So before I start on the the experiments and lessons, Lets get rid of the clutter and outside voices first. there’s been lot of chatter past 3 weeks about goods and bads of this with different take on what it could be used for, and also ethical issues. here’s a quick review of what everyone’s been talking about.

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Social Media Reactions : Mixed Reception Across Platforms

Google's launched Gemini Storybook feature branding it as a tool for parents to turn in to digital storyteller themself and help ‘em tell their kids their personalized bedtime stories, of course, including explaining the kids what they do in a simple fun way. the reaction was lovely, from parents of course, but the storytelling writers went up in arms. for past 20 days have generated significant discussion across social media platforms, with reactions ranging from enthusiastic adoption to creators’ hating it for quality? or like they hate everything AI, and I also saw some serious concerns about safety, as the results being dangerous.

Reddit was interesting :

The r/singularity subreddit showed strong positive reception, with one post receiving 185 votes and users calling it "crazy good". However, some users expressed philosophical concerns, with one commenting: "You could have envisioned it yourself, but instead, you opted to have an AI do the imagining for you". Not very different from Publishers Weekly reporting mixed industry reactions, with children's book advocates expressing concern about AI-generated content displacing human creativity. Martha Brockenbrough, a children's book author, compared AI-generated stories to "serving them powdered orange juice made in an unregulated factory".

Oh but they like the technical capabilities
In r/Bard, users discovered the feature was "wild" with its ability to write and narrate custom stories in seconds. The discussion included practical questions about access, with users sharing direct links to the feature at gemini.google.com/gem/storybook. also the character consistency was praised across scenes, acknowledging it "handles that impressively well". But most had mixed feeling about the launch as google’s implementation kind of overshadowed their own AI-driven narrative projects.

On LinkedIn?

professionals praised Gemini Storybook for its creative uses beyond kids’ stories. Marketers called it a “game-changer” but suggested it should work as a co-author for deeper educational value. Others used it to turn business reports and career stories into engaging, illustrated narratives. Still, some, like attorney Danielle van Lier, voiced concerns about character inconsistency and inappropriate content, noting issues like unexplained changes in character ethnicity, especially in coloring book styles.

Twitter/X -

Was all around excited, As Google's official channels promoted the feature actively, with the Gemini App account highlighting that "Storybook is a way to create personalized, illustrated stories that you can read, listen to, print, and share". Many users expressed excitement about the immediate practical applications.

And Substack -

has been all over with the commentary and long form analysis. and considering an AI survey that substack did, I am not surprised by the reactions.

But overall, Gemini Storybook is getting praise from marketers and educators for its creativity, polished results, and versatility in everything from brand storytelling to teaching complex ideas.

Here Are Few Common Themes Across Platforms

also helps us understand how multi-purpose this tool could be. Let me put some emoticons to add-in my experiences, just to state what I haven’t experienced / din’t come across yet.

Positive Reactions:

• Speed and ease of use ✅

• Creative applications beyond children's stories ✅

• Business and educational potential ✅

• Accessibility across languages and devices ✅

• Professional presentation quality ✅

Critical Concerns:

• Image safety and inappropriate content generation ❓❌

• Character consistency issues ✅

• Racial bias in default representations ✅ ❌

• Lack of child development expertise ❌

• Quality compared to human-created content ❌

• Copyright and artist compensation issues ❓

Notable User Innovations:

• Language learning applications with graded readers ✅

• Business presentations and brand storytelling ✅

• Therapeutic and educational content creation ✅

• Family memory preservation ✅

Laura Holmes, a former Google product manager and founder of Wanderly, published a comprehensive critique titled "Why I'm offended by Google Gemini's Storybook". Her analysis identified five major concerns: I am adding my experience with it.

1. Image Safety Issues: Holmes documented that simple prompts about mermaids and aliens generated inappropriate images containing nudity ❌

2. Excessive Assumptions: The system makes decisions about character names, appearances, and motivations without user input ✅

3. Racial Bias: Default characters are consistently portrayed as "light-haired, fair-skinned" children ❌ ✅

4. Uninformed Child Development: The tool suggests coping strategies without understanding the root causes of children's concerns ❌

5. Uncontrolled Environment: Children could access inappropriate content with minimal safeguards ❌

the traditional biases like portraying Bosses and Seniors as Male with ties, and using default characters appearance and names as white, have been plaguing all the AI tools, so I am not going to crucifying Storybooks for just that, but with that thought here comes the first important lesson.

Craft your prompt well and You’d get exactly what you want.

and I learnt it but not in the hard way because it was fun to experiment and experience the whole process but it took long and lessons came in batches too. so here I took a tea break from writing this article to think what could be the based way to share it. should i make it from lessons with stories as examples, main stories and lessons learnt from them, or what all you could do with lessons and stories with in, and felt the second works better with the flow of story so here we go!

Chapter 1 - Aira’s adventures

This is where we go back to my first ever storybooks about AI’s adventure. as a habit now, Just like I would do for a video, or image, I created a prompt using perplexity and passed it on to storybooks

• No defining pages

• No giving instructions on the images or text

• no uploads

• Just giving a storyline theme and a characters’ sentiments, and gemini run with it.

I love the story and images, as they came out, but it looked a bit sad, so I went for another round and asked for humor and it turned out as in the video above. one word - fantastic.

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Lessons learnt :

• not just bedtime stories for children, no.

• You can experiment, and create random and unique stories through your imagination. Huge scope to build any storyline and build on it.

• Image quality was great. storytelling not so bad.

• pro / enterprise gemini account have sharing restrictions so if you want to create a share link, you need to either change your settings, or go to your non-pro gmail account and create the stories there.

• gemini chat has all the history mixed up with storybooks and research and videos all in same sidebar, together. the icon helps to identify the storybooks but its a mess.

as I edited to make my storybook more fun, than a sad tale, I learnt my entire storybook had changed - illustrations and text both, I obviously wasn’t bothered but then came the second chapter where editing with in begins.

Chapter 2 - Past Travel stories ( Pdfs, Images, Blogs)

So in my next round of experiment I decide to pick few of own and family’s past adventures. and here comes the bias in plenty.

I uploaded the pictures from my family’s travel, and it automatically used the name Priya and Raj for my parents. they had to have white hair both of them and my brother, well got a typical london haircut, and was in shorts consistently through out the 10 pages even though I uploaded 2 pictures of him on the beach.

Lessons learnt :

• you can upload pictures. and it’ll reflect the background and imitate some pictures as it is.

• there is character biases, in heavy.

• if you’d like to make changes in how the characters look and who they resemble, it may take some instructions successfully while other may fail spectacularly. - I told it my mom’s hair are long and black. it did change the hair to long but kept them white. my mother’ wasn’t very happy. - On the other hand, my brother had six-pack and was shirtless through out, he said he doesn’t look like that right now, so he wasn’t happy either. he’s back in the gym now. so I guess it was motivating?

• you can get the name changed, but it hallucinates, so if you continue to edit the storybook through multiple prompts, it may keep changing the storybook and even going back to previous one and failing entirely.

Now that I knew a little bit of how it works, I decided to go with pdf uploads that had both pictures and text with storyline, I took two of my very different travel blog articles, one from 2010 and another from 2015, and moved to my regular gmail -gemini account so I dont have any sharing issue.

The ladakh memoir ended up with at least 10 versions. first and foremost, Priya, gemini’s favorite indian female name - earlier my mother , and now me, both we called Priya. this is when the pdf clearly specifies my name, as you can see on the left above. as it turned out a friend also was creating a storybook and his female character was automatically named Priya and looked almost like me.! - well I din’t like it so I decided to change.

added some quotes on cover as well, and I believe I should have a copy with just quotes on the pages with images but I can’t find it right now. so here Lets go straight to the lesson learned.

Lessons learnt :

• gemini automatically decided the storybook is meant for a child or adult. my first story was categorized adult, which I think gemini decided after reading the script. the ladakh tale was categorized child friendly, but no specifications in there.

• if you provide photos and text story - it takes the flow but you need to specify the tone, and additional details about who’s narrating the story.

  

• if its throwing the errors, better start afresh.

• you can make specific changes, by specifying the page number or text details, but better do it one by one, or it may take one instruction and forget the other.

  

• here I added a prompt for multiple pages at the same time, ( not a very fine prompt to go look in to pdf but i was just chilling so )

• I could changed the language easily with single prompt for entire storybook

• tried adding the chapter headline for each pages and adding a quote, but that din’t work. which means » it takes the text in one full passage with no breaks.

  

for my 2015 story I was able to get the collage of the exact photos that I uploaded in the pdf file along with some new images generated based on the story told.

For those complaining about consistency of the images, if you do not provide multiple pictures the character look are auto generated based on the story and used through out the storybook which can be a bit boring, so prompt it to use the unique landscape and clothing and specify the character looks.

Chapter 3 - Chatlogs, NotebookLM, Multiple characters

As you can see from the screenshot, I can’t scroll back anymore so I kind of lost the thread, as I find out just now, but never mind. for this experiment, I picked up a brief from my notebookLM notes, about top 10 and 20 moments from a chat. since by now I had figured out the number of pages is limited to 10 to 12. ( it gave me 12 once ) i gave it 10 moments for 10 pages with 10 characters ( a few repeat) from different backgrounds and it resulted in multiple copies to fix, from names to ethnicities. the images were still great and it picked up the story edits well, but it as never perfected. for example - below two different generated pages for same moment.

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to try it further I gave it a story with 4 women with different background on a trip to greece. and while it was able to create the storyline the images were not only biased but gemini had hard time fitting 4 people in the same frame.

results, it kept on duplicated the same character, getting it right sometimes and wrongs almost all the time. !

Lessons learnt :

• keep it simple for now, use 1-2 characters at max.

Chapter 4 - Movies and Series

If you gotten tired reading it, I apologies, this is the second last chapter and this is also where the fun actually begins. While I could create some great looking storybooks so far, it was kinda tiring to go on editing spree, and It so happened that google limited the daily quota, on non-pro accounts as well. By now I also learnt that I wanted to craft my prompt per page as finely as possible, so i have to make least amount of changes once the storybook is created.

and here I skipped perplexity and instead created this prompt on google AI mode after looking for the storyline of one of my favoriteGreek film from 1988 called Topío stin Omíchli ie "Landscape in the Mist". I asked it to create the pages, edited the prompt to add the image type, colors etc and the storyline text for each page, before taking it all to the storybook. below was the prompt.

Okay create the story book from below details of the a black and white movie that i loved the story of the movie is - Theo Angelopoulos's 1988 film, Landscape in the Mist, tells the poignant story of two young siblings, 11-year-old Voula (Tania Palaiologou) and 5-year-old Alexandros (Michalis Zeke), who embark on a journey across Greece in search of their supposedly German father.

Their mother has fabricated the story of their father's existence to keep them content, but Voula and Alexandros, yearning for their father, escape their unstable home life and set out to find him.

Their journey is a harsh and perilous coming-of-age odyssey, exposing them to the raw realities of the adult world. They encounter various individuals, some kind like Orestis (Stratos Tzortzoglou), a roadie who offers them temporary shelter and companionship, while others are cruel and exploitative.

The search for their father, a potentially non-existent figure, becomes a metaphor for their search for belonging, safety, and a sense of identity in a desolate and uncaring world.

The film uses evocative imagery and a slow, contemplative pace to portray the children's journey as a dreamlike odyssey filled with both moments of harsh reality and surreal encounters, like witnessing a giant marble hand emerging from the sea. I have also created suggestive pages details you can use to build the story.- 

Here are 10 illustration prompts based on the story of "Landscape in the Mist":

Page 1: The Escape: Illustrate young Voula and Alexandros, a girl and a little boy, sneaking out of their home, carrying small bags, with a glimpse of a town in the background. Perhaps include a sense of determination and a hint of trepidation on their faces.

Page 2: The Train Station: Show Voula and Alexandros at a bustling train station, trying to board a train, appearing overwhelmed by the crowds, and eventually being led away by a police officer.

Page 3: The Uncle's House: Depict the children at their uncle's home, eavesdropping as the uncle explains to the officer that their father is in Germany and that their mother has been telling them a lie.

Page 4: Snowy Departure: Illustrate the children escaping during a blizzard, the snowflakes swirling around them as they disappear into the distance, reinforcing a sense of isolation and resilience.

Page 5: The Roadie: Show the children on a desolate road, perhaps walking or hitchhiking, and encountering Orestis, the friendly roadie, offering them a ride in his broken-down bus. Convey a sense of cautious hope and the beginnings of companionship.

Page 6: The Traveling Theatre Troupe: Illustrate the children's time with Orestis's travelling theatre troupe, showing them engaged and possibly finding a sense of belonging amidst the performances and the communal life of the actors.

Page 7: Crossing the Border: Illustrate the children attempting to cross a border, perhaps showing a barbed-wire fence or a natural barrier, emphasizing the challenges and risks of their journey.

Page 8: The Desolate Landscape: Show the children traversing a vast and lonely landscape, emphasizing the mist and the expansive nature of their journey, symbolizing their search for something beyond their grasp.

Page 9: The Giant Hand: Illustrate a surreal and powerful image of the giant marble hand emerging from the sea, showcasing the film's dreamlike and mythical qualities and the children's wonder at the sight.

Page 10: The Landscape in the Mist: Illustrate the final image of the lone tree emerging through the mist, symbolizing the children's arduous quest and the ambiguous nature of their destination and the enduring search for meaning.  

the result : https://g.co/gemini/share/80e70843be73

This is by far one of my best storybook, I could almost recreate what I wanted to from the imagery point of view, including the landscape and characters. the story was also very well told with in 10 pages / chapters only.

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I wanted to try one more so I went with another favorite but completely different movie called the “the winged migration” in the same thread and it did an alright job - remember the crowd from chapter 3? Yup. too many elements and it slips.

Lessons learnt :

• I loved everything about this method, and workflow, it was the most smooth - dont’ blame me if people go about creating their favorite series storybooks but yeah this is FUN. and Oh I’d love to see if you do so do share.

• Google AI prompt does work way better than the perplexity Ai created prompt, at least for storybooks.

• You can create multiple different storybooks in the same thread.

• While creating a public sharing link, gemini takes away your name from both the cover page and the right top on every page. :/ so good luck with the copyrights.

• There is lag in the voice which you have to deal with while we are still in storybook experiment phase. I converted few stories in hindi and the audio was faster, so if you record and upload those , you may need to slow it down a tad bit.

• Did i forget yet? Just like Priya and Raj, gemini also love Ella and Silas and would use those name even after you detail them out. So yes no option but to edit it.

and alas here comes the final chapter and this is the shortest.

Chapter 5 - Actual AI storytelling

So after all this experimentation and learning, I decided to try gemini storybook for one project that I wanted to do for ages. which is learning and translating the ancient text in easier way. the ancient stories from the Hindu literature, something I skim from time to time but haven’t had a chance to regularly read. so this give me a fun way to interact with those granthas and I can also bring in some consistency and make it a habit.

One of the main challenge with such a project is the creating the collection of stories and bringing in the same atmosphere in the imagery that reflect the same timeline and culture. I have created 4 stories so far in both english and hindi - for hindi i used the same prompt but prefixed it with hindi most of the times. but as I went with the process of creation i wanted to create slightly different images for each version. so i created separate prompts.

To take it this project seriously I also created a dedicated channel just for this - https://www.youtube.com/@Aranyakah and here’s one of the story video.

Lessons learnt :

• very much like making a movie but 3 minutes and its still images only, so slightly easier that way then video scripting and making.

• this can be hard work but not so much if its well thought out and if you enjoy the process. do it if you love it.

• any story is possible, for adults, for kids, those both can share..!

Conclusion

AI storytelling isn’t stealing creativity or jobs of creative writers or storytellers. To create a good storybook you need to work equally hard, While, unlike traditional creative tools, AI storytelling platforms can quickly produce entire narratives—often personalized, illustrated, and interactive—using advanced language and image models, to get good content the background work, and thought process is still much the same. so it should be used as a form of art on its own. AI storytelling can democratize content creation, making it easy for anyone—regardless of writing or artistic skill—to bring their ideas to life, opening up endless possibilities for personalization, multilingual storytelling, and accessibility.

Like Google Gemini storybook, There are plenty of similar AI-powered creative and storybook apps, that are used for everything from children’s bedtime stories and educational materials to business presentations and creative experiments. Apps similar to Gemini Storybook include ChatGPT (with tools like DALL-E for images), Claude, Simplified, Meta AI, Microsoft Copilot, Poe by Quora, and ModelsLab—many of which allow users to generate stories, images, and even voice narration, sometimes for both kids and professional uses. As more people experiment with these platforms, AI storytelling is reshaping how we create, share, and experience stories in the digital age. However, while the technology is fun and powerful, it also raises questions about quality, originality, safety, and the influence of algorithms on culture and creativity.

Google Gemini overall has over 82 million monthly active users as of Q2 2025, with 284 million total visits in February 2025 alone and a sharp rise in use since launch. Storybook-specific data isn't yet public, but its rapid adoption is highlighted by educators, parents, and business users since its global launch in August 2025.

I may have missed few points but this article has already taken me almost over 6 hours to write. ✍️ and its 3:47am 😢 So closing it and publishing now. I hope you enjoyed the breakdown and its motivating enough for you to try storybook yourself. Write your questions in the comments and share your stories, and have fun storytelling!

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The Audio podcast above provides a great recap of the conversation as it flew from reflections to serious discussion on the future direction of the AI and participants alike.

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Core Quotes and Insights from the Fellows

Here are the key quotes/points made by each participant:

Poonam Parihar – Driving Ongoing Community Conversations

Poonam Parihar organized and facilitated the recurring calls, viewing them as fun, free‑flowing chats where AI topics naturally emerged. She said the Perplexity Fellowship had generated a lot of “PR pumping” for the company, effectively making fellows “guinea pigs” for testing new features, “pre‑customer research” and product testing. She advocated continuing community conversations, such as this concluding series and starting a new focussed series with a brand new perspective and format, to keep up with the rapid pace of AI evolution, which she expected would stabilize by 2026 or 2027. She also shared that she was working on a deep‑tech project involving AI 5G implementation with AI chips (NPUs), which she viewed as a game‑changer for on‑device processing.

Uvika Sharma – Learning 10x and Advocating for Women in AI

Uvika Sharma, an “AI tool junkie” but cautious about data privacy, had already been a Perplexity Pro user before the fellowship. She described the fellowship journey as “phenomenal” and said her learning had increased “10x” through interactions and diverse perspectives. She valued hearing “uninterrupted wisdom” from leaders like Jensen Huang and Ali and expressed a desire to see more women in tech and AI—specifically a female AI leader—in fireside chats. She agreed with Poonam and found the fellowship model “very clever”. She also expressed discomfort and concern about connecting Comet to her Gmail due to privacy implications, especially regarding private contracts.

Chon “Choon” Chua – Inspired by Leaders and Diversity

Chon “Choon” Chua expressed gratitude for the fellowship’s “amazing exposure” and “lineup of the speakers,” noting that the organizers had likely leveraged their social networks to attract top leaders like Jensen Huang. She was impressed by Huang’s “unfiltered” and “heartwarming” character. She mentioned that she had switched from using Anthropic Claude as her main tool to using Perplexity “a lot more” and that she “really loved the concept of Comet.” She also appreciated the diversity of talent within the fellowship.

Stephanie Goutos – Balancing AI Benefits with Privacy Risks

Stephanie Goutos, an attorney and limited partner in a venture fund, shared that she had connected Comet to her personal Gmail, accepting the privacy risk because the time‑saving benefits outweighed her concerns, but she had not connected it to her work email. She agreed that the fellowship was “really smart” for Perplexity, generating “a ton of free publicity” and exposure across industries.

Ravi Vinnakota – Championing Single‑Person Startups

Ravi Vinnakota, initially suspicious of the fellowship’s value, said he had ultimately found the journey very good. As an engineering head, he had introduced AI tools like ChatGPT from day one for product requirements, code generation, and deployments. Inspired by Roy Bahat’s session, he was passionate about building “single‑person billion‑dollar startups” or “as lean as possible” teams, believing AI made this feasible. He predicted AI tools would stabilize by 2026 or 2027, similar to how organizations settle on cloud providers, and he strongly believed “agentic AI” would dominate by then, performing at least half of human work.

Khusshboo Mehta – From Prompt Engineering to AI Literacy

Khusshboo Mehta described the fellowship as a “phenomenal journey” and noted the unusually active WhatsApp group. She highlighted the shift from prompt engineering to context engineering and the access to tools like Comet. She emphasized that “AI literacy training” was crucial groundwork for AI adoption within teams and organizations.

Adarsh S Lathika – Privacy‑Focused Builder of Personal AI Apps

Adarsh S Lathika said he actively practiced digital privacy by using a secondary phone for social media and avoiding financial apps on his phone. He seconded Poonam on building personal AI apps and informed he’s been working on for his own needs, such as an apartment management system and games he used to play. He found Perplexity did a “much better job in report generation” than ChatGPT, though it occasionally hallucinated, requiring data validation. He stressed that AI output quality depended on the user’s input quality, domain expertise, and effective prompt design.

Yogesh Panchal – Balancing Data Sharing and Productivity

Yogesh Panchal agreed with the view that Perplexity’s PR strategy had been successful. He said he was willing to share personal data with AI tools if it increased productivity and improved the technology, noting that “technology always asks something from us.” He supported individuals building personal AI apps for their own use, likening it to customizing one’s own car. He also suggested using a “burner” email address for privacy‑sensitive AI interactions.

Delia Green – Humility and Hands‑On AI Adoption

Delia Green, coming from a finance and accounting background, said she had developed a growing passion for technology. She found the fellowship gave her exposure to numerous AI tools she had not known about and greatly appreciated the community’s support in solving specific issues, such as automating emails using Make.com. She learned the importance of humility and keeping an open mind (“keep down your ego”) since everyone could learn something new. She planned to apply AI in her daily corporate tasks and was also considering opening her own venture. She noted being surprised by the very fast pace of AI change.

Sukhdeep Singh – Bringing AI to Traditional Industries

Sukhdeep Singh, a sales professional from the consumer goods industry, described the fellowship as a “zero‑to‑one” learning journey, calling it “100x phenomenal.” Before the fellowship, he had not heard of Perplexity and had only used basic ChatGPT. He now actively bookmarked and made efforts to apply all mentioned AI tools to his work. He agreed with Poonam that there was huge potential for AI beyond the tech sector, especially in “primitive industries” like mining, natural resources, and agriculture. He saw AI combined with IoT as a “killer” combination in the physical world and had started learning about IoT. He regarded AI chips (NPUs) as a game‑changer for hardware integration.

Srinivasamurthy Mohan – Learning Through Weekly Peer Calls

Srinivasamurthy Mohan, from a non‑technical background in sales operations and pricing, did not work with programming languages but understood the tech landscape. He had been exploring multiple AI tools and new topics like Copilot Studio and Make.com to learn. He emphasized the value of weekly calls for “learning by hearing from others,” which, he said, helped condense information that would otherwise take hours to find.

Giulia Felicetti – Bringing Perplexity to the Italian Market

Giulia Felicetti had been directly involved in bringing Perplexity to the mass market in Italy and had witnessed the product’s significant growth. She was impressed by the quality of people on both the Perplexity side and among the fellows. She admitted finding it challenging to stay updated in AI but recommended habits like listening to podcasts and, most importantly, actively using the tools. She also highlighted the value of taking notes and bookmarking important information from the WhatsApp group.

10 Reflection Points

Here are 10 unique reflection points based on the live discussion, offering consolidated insights and observations from participants:

1. Transformative Fellowship Learning Journey
   The Perplexity Fellowship was described as "phenomenal" and a profoundly transformative experience that accelerated learning by "10x"–"100x". Exposure to leaders like Jensen Huang and Ali provided "uninterrupted wisdom" that inspired participants to explore AI more deeply.

2. Active and Supportive Community
   The highly engaged WhatsApp group became a cornerstone of the fellowship’s success—fostering friendships, trust, and collaboration while offering diverse perspectives and mutual help.

3. Perplexity’s Strategic Fellowship Design
   Participants saw the fellowship as a clever strategic move—doubling as “pre-customer research” and a live product-testing ground for features like Labs and Comet, generating valuable feedback and significant free publicity.

4. Managing the Rapid Pace of AI Evolution
   With new tools and models emerging almost weekly, participants found it challenging to keep up and differentiate genuine advances from hype cycles. Many expect the market to enter a stabilization phase by 2026–2027.

5. Rise of Agentic AI and Lean Innovation
   By 2026, agentic AI is predicted to perform at least half of human work, enabling “single-person startups” and widespread development of personal AI apps tailored to specific needs such as smart home systems or niche applications.

6. AI’s Expansion into Traditional and ‘Primitive’ Industries
   Beyond the tech sector, AI is expected to revolutionize industries like agriculture, mining, and natural resources, enabling applications from precision farming to resource optimization.

7. Convergence of AI with Hardware
   AI chips (NPUs) combined with IoT and 5G are viewed as a game-changer for on-device processing, unlocking faster, more private, and location-aware AI applications in physical environments.

8. From Flashy Demos to Daily Productivity
   There is a need to shift AI narrative and marketing from one-off novelty use cases to demonstrating how AI can deliver tangible time savings and productivity boosts in daily professional work.

9. Privacy Concerns and Adoption
   Attitudes toward connecting AI tools to sensitive accounts like Gmail vary. Some prioritize convenience, others use measures like burner accounts, and many stress the need for stronger privacy safeguards.

10. AI Literacy as a Foundational Skill
    Equipping people and teams with foundational AI knowledge is seen as critical groundwork for successful adoption. AI literacy enables confident use, informed decision-making, and leadership in AI integration.

10 Key Future Directions & Learnings.

Participants identified the following forward-looking themes for AI’s evolution, adoption, and continued learning:

1. Stabilization of AI Tool Ecosystem – Moving from rapid churn toward a more stable set of trusted tools by the mid-2020s.

2. Dominance of Agentic AI – Significant automation of human workflows, leading to marketplaces for independent AI agents.

3. Cross-Industry AI Integration – Deep adoption across both high-tech and traditional sectors.

4. Hardware-Enhanced AI Capabilities – NPUs, 5G, and IoT driving embedded, real-world AI uses.

5. Focus on Personal AI Applications – Individuals creating AI-driven tools for their unique needs.

6. Marketing AI for Everyday Workflows – Showing practical, productivity-driven results rather than just novel demos.

7. Evolving Privacy Standards & Trust Models – Balancing convenience with data protection.

8. Organization-Wide AI Literacy Initiatives – Structured training to embed AI readiness.

9. Community-Driven Learning Networks – Sustaining the collaboration and knowledge-sharing seen in the fellowship.

10. Prioritizing Real-World Value Over Hype – Critical focus on proven impact and measurable benefits.

What fellows are doing to keep up with the evolution of AI and their take ways for both personal and professional development in AI that you could use

Individual Commitment to Continuous AI Learning and Application: Participants are actively planning to keep learning and applying new AI tools and models to their work and personal lives. This involves dedicating time to test and use the tools as they emerge. Delia Green, for example, is keen to understand how to apply AI in her daily corporate tasks and potentially open her own venture. Sukhdeep Singh, a sales professional, is making a conscious effort to bookmark and use all mentioned tools to apply them to his work, recognizing AI's potential beyond tech. Srinivasamurthy Mohan, from a non-technical background, plans to continue exploring multiple AI tools and new topics like Copilot Studio and Make.com to gain learnings.

Focus on Building Personal AI Applications and Lean Startups: A significant future direction is the idea of individuals building "personal AI apps" for their own needs, rather than solely for commercial purposes. This includes managing personal aspects like apartment systems or creating custom games. This aligns with the concept of "single-person startups" or "as lean as possible" teams, empowered by AI agents, as championed by Ravi Vinnakota, who is passionate about this model.

Strategic AI Literacy Training for Organizations: For broader AI adoption, a crucial future step involves establishing "AI literacy training" as foundational groundwork within organizations. This indicates a recognition of the need to equip teams with a basic understanding of AI to facilitate its successful integration and "AI leadership" across various contexts.

·Anticipating the Stabilization and Dominance of Agentic AI: Looking ahead, participants anticipate a stabilization phase in the AI tools market by 2026 or 2027, where organizations might settle on specific tools rather than constantly switching. More significantly, agentic AI is expected to heavily dominate the world, with real-world use cases emerging by 2026, potentially performing "at least 50% of human work". This suggests a future where marketplaces for independent agents could become prevalent.

Expansion of AI into Diverse Industries and Hardware Integration: The future plans for AI involve its expansion beyond the traditional tech sector, significantly impacting "primitive industries" like mining, natural resources, and agriculture. This widespread integration includes the convergence of AI capabilities with hardware, particularly AI chips (NPUs), which is seen as a "game-changer" for on-device processing and physical world applications, especially when combined with 5G and IoT. Poonam Parihar mentioned working on a project involving AI 5G implementation with AI chips.

Shifting Focus in AI Marketing to Daily Productivity: There is a strong call for AI development and marketing to shift from showcasing "one-time, fancy" use cases (like planning a wedding or remodeling a kitchen) to demonstrating how AI can tangibly improve "daily professional tasks" and boost productivity, saving hours in an "8 to 10 hours of working day". This reflects a desire for AI to become a practical tool for efficient daily operations in both personal and business contexts.

Continuation of Community-Driven Learning and Interaction: To keep up with the rapid pace of AI development, there is a clear plan and desire to continue the community calls on a regular basis. This rhythm is seen as crucial for ongoing learning, hearing diverse perspectives, and maintaining the strong relationships and trust built during the fellowship. Poonam Parihar expressed a personal commitment to continuing these conversations, and other participants strongly agreed on the value of regular group discussions for staying updated.

Lastly Let me leave you with a very intriguing video courtesy Google notebookLM on this call.

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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

Key takeaways

1. Diverse AI Tool Capabilities and Limitations: Participants gained specific insights into the technical performance and limitations of various AI tools. Manus AI was highly effective for complex tasks such as generating detailed travel itineraries and rendering 3D objects using 3JS, where other AI models often failed. In contrast, OpenAI's agent was found disappointing for creating presentations, struggling with content accuracy, typography, and often producing inferior results despite significant processing time. Comet, described as an "agentic browser" comparable to Google's Project Mariner, enables "agentic tasks" and can observe and replicate user actions to create repeatable tasks, such as cleaning spam folders, though it requires the computer to be active and running.

2. Fundamental Data Privacy and Security Architecture in AI: A critical technical learning revolved around how AI models process data and the inherent privacy risks. It was clarified that AI models for tools like Comet do not run on the user's device but on the company's (e.g., Perplexity's) servers, meaning user data is transmitted to them. Despite claims of anonymization, participants expressed significant concerns about data privacy, citing that this anonymization depends entirely on trusting the company, drawing parallels to data practices of large tech companies like Meta and Microsoft. It was also highlighted that true data privacy with AI often requires running open-source models on private hardware, due to common data collection policies in commercial and enterprise software, though this comes with higher cost barriers and potential performance limitations compared to well-funded commercial options.

3. AI and Intellectual Property (IP) Ownership: A key technical-legal learning emphasized that users typically do not own the copyright to AI-generated content unless they hold an enterprise license. The content generated often remains the property of the AI companies, and user data, including prompts and file uploads, is frequently used to train AI models (unless explicitly stated otherwise for enterprise versions). This evolving legal landscape highlights the technical reality of how AI systems learn and produce output.

4. AI's Role in Enabling Non-Technical Development: Participants learned that modern AI tools are making development more accessible. Tools like Bolt, Replit, or Curser can enable non-developers to build applications and even WordPress websites without needing coding expertise. However, the quality of these AI-generated applications depends on their complexity.

5. Specific AI Application Challenges in Industry: It was noted that AI agents and tools, particularly those like ChatGPT's agent demo, frequently focus on hospitality use cases such as booking hotels or wedding venues. This implies a technical inclination or ease of application in certain domains, possibly due to structured data or common query types.

6. AI's Limitation in Design and Visual Generation: A specific technical frustration was highlighted regarding AI's ability to handle design and visual elements. Tools struggled with accurate image generation, typography, and precise alignment for visual outputs like Gantt charts or infographics, indicating a current technical limitation in AI's understanding of visual aesthetics and layout compared to text-based generation. Kuber Mehta specifically mentioned AI "sucks at" rendering 3D objects using 3JS due to insufficient good training data.

Mind Map Overview of the Discussion.

Full Summary

Here's a summary of the "Perplexity Monthly Catch Up" meeting in bullet points, covering key points discussed by the participants:

• Participants: Poonam Parihar, Adarsh S Lathika, Nitin Birla, Madhumita Rao, Kuber Mehta, c pandya, Giulia Felicetti, Khusshboo Mehta, Rohit Khanna, and Amit Kumar engaged in the discussion.

• General Topics: The meeting covered individual introductions and professional backgrounds, experiences with various AI tools (particularly Comet), and challenges related to AI adoption, data privacy, public understanding of AI, and the ROI of new AI technologies.

• New Individual Highlights & Backgrounds:

  • Nitin Birla (Google, San Francisco Bay Area): Leads hardware teams on the business side, with 15-20 years in consumer electronics hardware (previously Amazon, Sony). He is involved in spreading AI culture within his company and was curious about Comet's agentic browser capabilities compared to Google's Project Mariner (a Chrome extension for agentic tasks) and OpenAI's agent mode. He theoretically described Comet as "Project Mariner on steroids".

  • Madhumita Rao (Swindon, UK): A product person with over 25 years in geospatial earth observation, including physical mapmaking and geocoding features. She founded "Planet," an agri-tech platform using real-time satellite data and AI, and has extensive AI experience dating back before the term was coined, including fuzzy logic and current work in predictive analytics for crops using machine learning and computer vision. She discussed challenges in securing funding for her startup and plans for pilot programs in India. She moved to the UK in 2015 via an "exceptional talent visa".

  • Kuber Mehta: Early in his career, having just finished his first year of college. He has already developed innovations such as a new image file format called "meow" for AI image reading.

  • Giulia Felicetti (Italy): Works in the marketing department for innovative services at Telecom Italia, focusing on generative AI. She discussed Telecom Italia's successful partnership with Perplexity AI, chosen for its simplicity and ease of explanation to general customers, noting a "crazy" successful launch and high user appreciation. She also expressed concerns about Comet related to data privacy.

  • Khusshboo Mehta (Dubai, vacationing in Delhi): Transitioned from a German teacher to sustainability and strategic partnerships, currently working with WWF. She highlighted challenges of AI adoption in NGOs due to limited resources and budget. She is actively learning about AI literacy, prompt engineering, and custom GPTs, aiming for a career in AI because it doesn't always require coding or machine learning.

  • Amit Kumar: A product manager in a fintech firm. He shared initial reservations about Comet related to sharing credentials due to the extensive personal data in platforms like Gmail, despite appreciating its UI/UX, particularly the astrological elements. He advised against taking AI astrology tools seriously for life decisions.

  • Adarsh S Lathika: Expressed greater trust in Google with personal data due to a long-standing relationship and uses separate phones/accounts for sensitive information to maintain privacy. He is reluctant to adopt new AI tools like Comet due to already using and mastering several others (Gamma, Make, Relevance AI, Replet) and prefers to wait for Google's upcoming AI tools, having received a sneak preview of their advanced features. He emphasized that while AI is useful for preparing documents, real business deals still require human interaction.

  • c pandya: A consultant who tests all available AI tools due to his profession and passion for AI. He manages privacy by using Comet only for publicly available information, avoiding sensitive banking or email activities. He agreed that determining the ROI of new AI tools is crucial and often possible within the first half-hour of exploration. He found non-traditional use cases for Comet, such as having it observe and record actions on a browser to create repeatable tasks (e.g., cleaning a spam folder).

  • Rohit Khanna: Expressed concern that even professionals do not fully understand AI, and the general population lacks basic AI understanding. He is building a platform to demystify AI for the travel and hospitality industry. He noted the industry's slow adoption of technology as a significant challenge.

• Discussions on AI Tools & Concepts:

  • Comet: Described as an agentic browser, compared to Google's Project Mariner and OpenAI's agent mode. Users expressed reservations about data privacy. Its UI/UX was appreciated, especially astrological elements. It can record and repeat actions, but requires the computer to be active and running for tasks, a safety feature. The AI model runs on Perplexity's servers, raising privacy concerns despite claims of anonymization.

  • Google's Project Mariner: A Chrome extension for agentic tasks, seen as similar to Comet.

  • OpenAI Agent: Described as a workflow builder within ChatGPT's interface, using a small web version for its AI. One user had a disappointing experience using it to create a presentation, noting it took an hour and the output was inferior to what could be produced with other tools like Gamma, with issues like disappearing content and poor typography.

  • Manus AI: Another agentic tool that generated a detailed 5-day Thailand itinerary, including flights, hotels, and activities, in 20 minutes. It was also found to be better at handling complex tasks like rendering 3D objects using 3JS compared to other AI models.

  • Other AI Tools: Gamma, Make, Relevance AI, Replit, Bolt, and Cursor were mentioned as tools used or capable of building apps and websites. "Passion.io" was also mentioned as an AI-based app creation tool requiring upfront payment.

  • AI and Intellectual Property Rights: Discussion covered that users typically do not own the copyright to AI-generated content unless they have an enterprise license. Legal rulings in the US suggest content significantly generated by AI is not user-owned. Terms and conditions of AI services often require users to indemnify companies for malicious content, while the generated content is owned by the companies, and user data is often used for training models.

• Challenges and Concerns Related to AI:

  • AI Adoption in NGOs: Limited resources and budget hinder the adoption of new technologies like AI in non-profit organizations.

  • Data Privacy and Security: Major concerns were raised regarding AI tools like Comet. Enterprise versions, such as Copilot, offering dedicated servers, do not guarantee data privacy, only performance. True data privacy with AI often requires running open-source models on private hardware due to common data collection policies in enterprise software. The cost of developing private AI solutions is a significant barrier compared to commercial options. There is a general lack of concern among the public about security and privacy with AI devices like Alexa.

  • Public Understanding of AI: Many professionals and the general population do not fully grasp AI concepts, such as anonymous usage or the drawbacks of uploading pictures. Many people equate AI solely with GPT and believe they are AI experts if they know how to prompt GPT.

  • Return on Investment (ROI): The difficulty of establishing ROI for new AI tools beyond initial excitement was discussed by consultants.

  • Entrepreneurship and Risk-Taking: Indian culture often discourages risk-taking, favoring safer career paths, which changes later in life due to increased confidence and financial independence.

  • Technology Translation: A significant gap exists in the networking and telecom industries in translating technical concepts to non-technical people and connecting directly with consumers.

  • AI Limitations: Frustrations with basic AI issues, such as inaccurate image generation and competition among platforms to add features rather than fixing fundamental problems, were noted.

  • Responsible AI Tool Selection: The importance of responsibly choosing AI tools and identifying parameters for selection was highlighted as a crucial, often overlooked aspect.

• Other Noteworthy Points:

  • The meeting began with initial greetings and pet introductions (Coco and Bella).

  • Poonam Parihar reflected on the fellowship's journey, noting the significant growth in AI discussions and awareness among participants over the past few months, highlighting the value of the WhatsApp group for shared knowledge.

  • The Perplexity Pro license for participants is valid until March or April 2026, depending on activation.

  • Booking.com released a global AI sentiment report, acknowledged as potentially skewed.