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I am a business developer, sales professional, and FinTech strategist who strives to make an impact and change the world. For the better.

Although I have a bachelor's degree in Economics (with a major in Economics and Politics), I have been actively following and/or working in the Financial Services field since the very first year at university.

Because of the early start of my career and given opportunities to try myself out in different roles and different players in the world of finance (I worked at Western Union, Danske Bank, Norne Securities), quite quickly and easily I found myself being the most passionate about Financial Technology and its enormous potential to reinvent conventional finance and change the world for the better.

Now I am working in FinTech for more than 6 years, and every day I try to be a bit better and learn something new (and there are lots to learn!).

Currently, I am a part of Flutterwave, where I am scaling the business and leading company's expansion into Europe. Flutterwave is a Mastercard, VISA, Greycroft, Y Combinator, and Worldpay FIS-backed payments technology company headquartered in San Francisco. We're simplifying payments for endless possibilities. Flutterwave is trusted by leading companies like Uber, Facebook, TransferWise, Netflix, Jumia, Azimo, Binance, among other 90,000+ businesses. We were recognized as the #2 Most Innovative Company in Africa in 2019, according to Fast Company. We're part of 50 Future Unicorns🦄 according to CB Insights & Fast Company. Also, CNBC has included us in the Upstart 100, which lists the brightest, most intriguing, young startups promising to become the great companies of tomorrow. I am super excited to play an important part in the Financial Technology revolution, and having the ability to move the financial disruption forward.

I am highly interested in FinTech/TechFin, digital innovation (especially in banking, payments, and finance per see), and emerging technologies.

P.S. If you're passionate about FinTech as well as Blockchain & Cryptocurrency and always want to stay up-to-date, make sure to subscribe to my newsletter 🔔linas.substack.com🔔 and join over 111,000+ professionals globally who follow my work.

Linas Beliūnas

Anthropic is going deeper into financial services: Claude Opus 4.6 just dropped, and it’s basically an AI model that can now do junior analyst work end-to-end 😳

Opus 4.6 is a workflow automation for regulated, high-stakes knowledge work.

The wild part?

The model isn’t just “smarter.”
It’s structured enough to operate inside real financial tasks.

What changed:

↳ 1M-token context → can ingest entire filings, earnings decks, and research threads at once

↳ Beats OpenAI’s GPT-5.2 by ~144 Elo on GDPval (finance/legal knowledge work benchmark)

↳ Built to produce spreadsheets, financial analysis, and presentations - not just text

↳ Designed to run multi-step “agent” workflows inside Claude Cowork

↳ Market reaction hit data incumbents like FactSet and S&P Global within hours

For 20+ years, finance software monetized interfaces to data and models.

Now the model is becoming the interface.

If AI can read the filings, build the model, stress test assumptions, and output the deck, what exactly is left for the traditional tool stack?

My takeaway is this:

AI in finance is shifting from “assistant” → “operator.”

Less time gathering data.
Less time fixing spreadsheets.
More leverage for the people who actually know what questions to ask.

The bar for entry-level work drops.
The bar for judgment rises.

Old edge was all about access to tools.
New edge is all about knowing what to tell the tools to do 🤖

Most importantly, finance isn’t getting replaced by AI.

It’s getting restructured around AI-native workflows.

And that’s one of the biggest shifts for financial services ever.

P.S. also check out How I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

This ad Claude made for Super Bowl is one of the best AI ads I’ve ever seen 👏😂

“Ads are coming to AI. But not to Claude. Keep thinking.”

Whoever made this at Anthropic deserves a massive raise.

OpenAI got cooked.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

This is Peter Steinberger, and his story is insane 😳

- Sells his first company for $100M+
- Spends 3 years in existential crisis
- Becomes jacked
- Comes back from retirement
- Vibe-codes 43 failed projects
- Project 44 is ClawdBot
- Goes viral
- Anthropic sends you trademark lawsuits
- Renames to MoltBot
- Crypto scammers hijack your accounts in seconds
- Secret rebrand to OpenClaw 
- Hits 180K GitHub stars
- Gets acquired by OpenAI

No VC funding.
No 100+ person team.
Just a solo founder + shipping.

Probably one of the wildest tech & AI stories of all time.

Legend.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

This is Peter Steinberger, and his story is insane 😳    - Sells his first company for $100M+  - Spends 3 years in existential crisis  - Becomes jacked  - Comes back from retirement  - Vibe-codes 43 failed projects  - Project 44 is ClawdBot  - Goes viral  - Anthropic sends you trademark lawsuits  - Renames to MoltBot  - Crypto scammers hijack your accounts in seconds  - Secret rebrand to OpenClaw   - Hits 180K GitHub stars  - Gets acquired by OpenAI    No VC funding.  No 100+ person team.  Just a solo founder + shipping.    Probably one of the wildest tech & AI stories of all time.    Legend.

The meme has been updated, and it’s the most honest picture of the AI era I’ve ever seen 👏😳

AI is slowly yet surely becoming the great leveler.

Especially for people who always had speed in their head but friction in execution.

→ Solo builders are now shipping MVPs in days with tools like Claude Code
→ Founders drafting decks, financial models, and outbound before breakfast
→ Creators offloading executive function to prompts, checklists, and agents
→ Small teams producing output that used to require entire departments

One person can now operate at team scale.

Leverage becomes internal, not inherited.

AI is amplifying the ones who were underestimated.

AI + Humans is the future.

Taste + Clarity + AI = unfair advantage 🚀

P.S. also check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

The meme has been updated, and it’s the most honest picture of the AI era I’ve ever seen 👏😳    AI is slowly yet surely becoming the great leveler.    Especially for people who always had speed in their head but friction in execution.    → Solo builders are now shipping MVPs in days with tools like Claude Code  → Founders drafting decks, financial models, and outbound before breakfast  → Creators offloading executive function to prompts, checklists, and agents  → Small teams producing output that used to require entire departments    One person can now operate at team scale.    Leverage becomes internal, not inherited.    AI is amplifying the ones who were underestimated.    AI + Humans is the future.    Taste + Clarity + AI = unfair advantage 🚀

Google quietly dropped one of the most interesting findings about current LLM architecture. Turns out, copying your prompt twice can 4x AI model’s accuracy 😳

No fine-tuning.
No new weights.
No extra “thinking” tokens.

In a December 2025 paper, Google Research tested 7 models across 7 benchmarks.

The intervention was almost silly:

Prompt → Prompt.

47 wins out of 70 tests.
Zero losses.

One example:

→ Gemini Flash-Lite jumped from 21% to 97% accuracy
On a basic name-retrieval task.
By pasting the same input twice.

LLMs process text left → right.
Every token can only look backward.

So when you write 50 lines of context and end with a question, the context never “knew” the question was coming.

Repeating the prompt gives the model a second pass.
A cheap approximation of bidirectional attention.
Like letting it re-read the exam before answering.

And it gets more interesting:

When chain-of-thought reasoning is enabled, the gains from repetition mostly disappear (5 wins, 1 loss, 22 ties).

Which suggests something bigger.

Part of what we pay 5-10x more tokens for in “reasoning mode” is simply the model giving itself another look at your input.

That’s not higher intelligence.
That’s an architecture workaround.

So the real gap isn’t scale.
It’s causal attention leaving performance on the table.

The teams that crack efficient bidirectional inference will compress the reasoning tax to near zero.

Everyone else will keep selling you more tokens.

The next AI battle will be about attention, not parameters.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

Google quietly dropped one of the most interesting findings about current LLM architecture. Turns out, copying your prompt twice can 4x AI model’s accuracy 😳  No fine-tuning. No new weights. No extra “thinking” tokens.  In a December 2025 paper, Google Research tested 7 models across 7 benchmarks.  The intervention was almost silly:  Prompt → Prompt.  47 wins out of 70 tests. Zero losses.  One example:  → Gemini Flash-Lite jumped from 21% to 97% accuracy On a basic name-retrieval task. By pasting the same input twice.  LLMs process text left → right. Every token can only look backward.  So when you write 50 lines of context and end with a question, the context never “knew” the question was coming.  Repeating the prompt gives the model a second pass. A cheap approximation of bidirectional attention. Like letting it re-read the exam before answering.  And it gets more interesting:  When chain-of-thought reasoning is enabled, the gains from repetition mostly disappear (5 wins, 1 loss, 22

Google is re-inventing Chrome for AI Agents: $3.7T tech giant quietly launched an early preview of WebMCP in Chrome 146, which turns websites into APIs for AI agents 😳

The problem Google’s solving is simple: browser agents today kind of suck.

They squint at screenshots.
Parse messy DOM trees.
Click buttons like confused interns.

Slow. Fragile. Expensive.

WebMCP flips the model 🤖

Instead of forcing agents to mimic humans, sites can now expose structured “tools” directly to them via navigator dot modelContext.

Think:

From screen-scraping → to API calls.

Then, websites stop being pages.
They become callable services for AI.

Early tests show:

→ 89% fewer tokens
→ 97.9% task success rates
→ ~53% lower cost
→ Declarative APIs for simple forms
→ Imperative APIs for complex JS flows

Booking a flight?
Filing a support ticket?
Adding to cart?

No more pixel guessing.
Just validated JSON schemas + direct execution.

And this isn’t about a nicer browsing experience.

It’s about shifting control.

When agents can reliably act across sites, the default interface to the web becomes conversational.

And no, SEO won’t disappear.

But AEO - Agent Experience Optimization - will matter much more.

The web was built for humans. The next version is being built for agents.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

Google is re-inventing Chrome for AI Agents: $3.7T tech giant quietly launched an early preview of WebMCP in Chrome 146, which turns websites into APIs for AI agents 😳  The problem Google’s solving is simple: browser agents today kind of suck.  They squint at screenshots. Parse messy DOM trees. Click buttons like confused interns.  Slow. Fragile. Expensive.  WebMCP flips the model 🤖  Instead of forcing agents to mimic humans, sites can now expose structured “tools” directly to them via navigator dot modelContext.  Think:  From screen-scraping → to API calls.  Then, websites stop being pages. They become callable services for AI.  Early tests show:  → 89% fewer tokens → 97.9% task success rates → ~53% lower cost → Declarative APIs for simple forms → Imperative APIs for complex JS flows  Booking a flight? Filing a support ticket? Adding to cart?  No more pixel guessing. Just validated JSON schemas + direct execution.  And this isn’t about a nicer browsing experience.  It’s about shifti

Wild: at a $14 billion revenue run rate, Anthropic is the fastest-growing software business of all time. And it’s not even selling software - it’s writing it 😳

Claude Code alone is at a ~$2.5B run rate in under a year.

It now generates ~4% of all GitHub commits 🤯

The numbers are absurd:

→ $0 → $100M → $1B → $14B ARR in ~3 years
→ $100k+ customers grew 7x YoY
→ 500+ enterprises now spend $1M+ per year (40X growth)
→ $30B fresh capital at a $380B valuation
→ IPO prep reportedly underway for 2026

While others chased consumer virality, Anthropic went straight for enterprise codebases, recurring budgets, and workflows that teams can’t turn off.

Yes, they’ll spend billions on training and compute.
But revenue is scaling fast enough that burn is shrinking relative to sales.

We thought AI would be a feature inside software.

Instead, the model became the engineer.

If this trajectory holds, 2026 won’t be about better prompts.
It’ll be about who owns the AI layer writing the world’s code.

Oh, and Anthropic should IPO this year, and it will be the biggest tech IPO of all time.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

Wild: at a $14 billion revenue run rate, Anthropic is the fastest-growing software business of all time. And it’s not even selling software - it’s writing it 😳    Claude Code alone is at a ~$2.5B run rate in under a year.    It now generates ~4% of all GitHub commits 🤯    The numbers are absurd:    → $0 → $100M → $1B → $14B ARR in ~3 years  → $100k+ customers grew 7x YoY  → 500+ enterprises now spend $1M+ per year (40X growth)  → $30B fresh capital at a $380B valuation  → IPO prep reportedly underway for 2026    While others chased consumer virality, Anthropic went straight for enterprise codebases, recurring budgets, and workflows that teams can’t turn off.    Yes, they’ll spend billions on training and compute.  But revenue is scaling fast enough that burn is shrinking relative to sales.    We thought AI would be a feature inside software.    Instead, the model became the engineer.    If this trajectory holds, 2026 won’t be about better prompts.  It’ll be about who owns the AI laye

New AI playbook? IBM just did the opposite of what AI experts predicted - it tripled entry-level hiring 😳

The $260B giant everyone assumes would automate first and ask questions later.

Instead, they’re tripling entry-level roles in 2026 - including software developers.

While 37% of companies say they plan to replace early-career jobs with AI.

Here’s what they realized:

The old entry-level job?
AI can do most of it.

So IBM didn’t cut the role. They rewrote it.

→ Junior devs spend less time on routine coding, more time with customers
→ HR staff intervene on chatbot outputs instead of answering every ticket
→ AI literacy is now the fastest-growing skill in the U.S.

Same U.S. headcount - roughly flat after layoffs + new hires.
Different org design - broader at the base.

Because if you cut juniors when AI handles “grunt work,” you don’t get leaner.

You get a talent cliff in 3–5 years.

No pipeline.
No future managers.
No one who actually understands how your AI systems evolved.

Most importantly, this isn’t anti-AI. It’s post-hype AI.

Not “AI replaces juniors.”
More like: AI compresses apprenticeship.

Maybe the real AI strategy isn’t fewer humans - it’s better ones.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

New AI playbook? IBM just did the opposite of what AI experts predicted - it tripled entry-level hiring 😳  The $260B giant everyone assumes would automate first and ask questions later.  Instead, they’re tripling entry-level roles in 2026 - including software developers.  While 37% of companies say they plan to replace early-career jobs with AI.  Here’s what they realized:  The old entry-level job? AI can do most of it.  So IBM didn’t cut the role. They rewrote it.  → Junior devs spend less time on routine coding, more time with customers → HR staff intervene on chatbot outputs instead of answering every ticket → AI literacy is now the fastest-growing skill in the U.S.  Same U.S. headcount - roughly flat after layoffs + new hires. Different org design - broader at the base.  Because if you cut juniors when AI handles “grunt work,” you don’t get leaner.  You get a talent cliff in 3–5 years.  No pipeline. No future managers. No one who actually understands how your AI systems evolved.

Nailed it! Anthropic is airing ads during the Super Bowl mocking OpenAI's decision to put ads in ChatGPT, and they're absolutely hilarious 👏😂

OpenAI recently launched ads as a new revenue stream to support massive compute costs.

Meanwhile, Anthropic just responded by committing to no ads inside Claude, and running a national spot making fun of AI interrupting your life with promotions.

One side optimizes for scale + monetization.
The other is betting that “ad-free” becomes a trust moat.

Different revenue models.
Different incentives.
Different futures for AI.

When the AI becomes the interface, the business model becomes the product.

P.S. check out 🔔linas.substack.com🔔, it's the only newsletter you need for all things when Finance meets AI. For founders, builders, and leaders.

Epic: Somebody created an AI-generated ad portraying 2036 where humans pedal at Energym gyms to power AI after robots displace 80% of jobs 👏😂

Co-founded by Elon Musk, Jeff Bezos, and Sam Altman.

Brilliant.

h/t AiCandy

P.S. for more great stuff, check out 🔔linas.substack.com🔔, it's the only newsletter you need for all things when Finance meets AI. For founders, builders, and leaders.

RIP investment bankers? Anthropic just pushed Claude deeper into finance with new AI plugins in Cowork 😳

Claude Cowork just rolled out new finance-specific plugins that go far beyond just an AI assistant.

This is real workflow automation:

Model → memo → deck.
Excel → PowerPoint.
Data → decision.

In one continuous session.

Claude is no longer just summarizing finance. It’s operating inside it.

What’s new:

→ 5 dedicated plugins: financial analysis, investment banking, equity research, private equity, wealth management.
→ Direct institutional data access via FactSet and MSCI.
→ Partner integrations from S&P Global and London Stock Exchange Group.
→ Cross-app memory: update a model in Excel, auto-refresh the slide deck in PowerPoint.

In demos, tasks that used to take analysts days - parsing earnings, updating comps, drafting research notes - were completed in minutes 🤯

While impressive, this won’t eliminate bankers. At least not yet.
Judgment, negotiation, trust - still human, and still critical.

But the copy-paste layer of Wall Street?
The formatting marathons?
The midnight model updates?

That layer is getting automated fast.

The old edge was all about stamina.
The new edge is all about orchestration.

If you run a fund, a bank, or build for them, this isn’t incremental.

It’s a cost structure rewrite hiding inside a plugin update.

The real question now is not whether AI replaces you.

It’s whether you redesign the workflow before someone else does.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

RIP investment bankers? Anthropic just pushed Claude deeper into finance with new AI plugins in Cowork 😳  Claude Cowork just rolled out new finance-specific plugins that go far beyond just an AI assistant.  This is real workflow automation:  Model → memo → deck. Excel → PowerPoint. Data → decision.  In one continuous session.  Claude is no longer just summarizing finance. It’s operating inside it.  What’s new:  → 5 dedicated plugins: financial analysis, investment banking, equity research, private equity, wealth management. → Direct institutional data access via FactSet and MSCI. → Partner integrations from S&P Global and London Stock Exchange Group. → Cross-app memory: update a model in Excel, auto-refresh the slide deck in PowerPoint.  In demos, tasks that used to take analysts days - parsing earnings, updating comps, drafting research notes - were completed in minutes 🤯  While impressive, this won’t eliminate bankers. At least not yet. Judgment, negotiation, trust - still human, a

Turns out, Apple’s genius move to win the AI race was… do nothing 😳

While everyone else sprinted into a $100 billion compute arms race, Apple stepped aside.

No frontier LLM.
No splashy AI acquisitions.
No “we’re building AGI” manifesto.

Instead, it did a $1B/year licensing deal with Google for Gemini.

That’s it.

Meanwhile:

→ OpenAI is projecting a ~$14B loss in 2026
→ Anthropic is running ~$14B revenue, yet still burning tens of billions on training + inference
→ Hyperscalers are pouring $100B+ into chips, data centers, and power

Apple wrote a check, and focused on what it actually owns:

↳ 2B+ active devices.
↳ The OS layer.
↳ The upgrade cycle.

October 2025: Apple drops M5 chips that are optimized for on-device inference.

70B-class models are running locally now.

Different game 🤖

Frontier labs are drilling for oil.
Apple is selling the iPhone that runs on it.

If AI nudges even a 10-15% hardware refresh cycle, that’s tens of billions in high-margin revenue.

And no model risk.
No $100B infrastructure bet.

It seems that the real moat was never the model. It was the ecosystem.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

Turns out, Apple’s genius move to win the AI race was… do nothing 😳  While everyone else sprinted into a $100 billion compute arms race, Apple stepped aside.  No frontier LLM. No splashy AI acquisitions. No “we’re building AGI” manifesto.  Instead, it did a $1B/year licensing deal with Google for Gemini.  That’s it.  Meanwhile:  → OpenAI is projecting a ~$14B loss in 2026 → Anthropic is running ~$14B revenue, yet still burning tens of billions on training + inference → Hyperscalers are pouring $100B+ into chips, data centers, and power  Apple wrote a check, and focused on what it actually owns:  ↳ 2B+ active devices. ↳ The OS layer. ↳ The upgrade cycle.  October 2025: Apple drops M5 chips that are optimized for on-device inference.  70B-class models are running locally now.  Different game 🤖  Frontier labs are drilling for oil. Apple is selling the iPhone that runs on it.  If AI nudges even a 10-15% hardware refresh cycle, that’s tens of billions in high-margin revenue.  And no model

AI In-alignment? Sam Altman and Dario Amodei refusing to hold hands at the AI Impact Summit 2026 in Delhi pretty much sums up the state of AI today 👏😂

Amodei left OpenAI in 2021 over safety disagreements and co-founded Anthropic, which is currently worth $380 billion.

Today, both companies are racing to build frontier models while publicly calling for global coordination.

AI models are converging.

The founders? Not so much.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

Anthropic quietly dropped a 33-page blueprint for turning Claude into your operating system - and gave it away for free. You can now program Claude without writing a single line of code 😳

It’s called Skills - a folder that teaches Claude how you work.

The best AI users today don’t prompt better.
They build systems that prompt for them.

Here’s why skills are so powerful:

→ A Skill is just a folder with one required file: SKILL dot md
→ A tiny YAML header tells Claude when to activate it (specific trigger phrases matter)
→ Instructions load progressively, so you don’t burn tokens every session
→ You can build one in 15-30 minutes using the built-in “skill-creator” meta-skill
→ Teams can deploy skills org-wide for consistent workflows

Think of it like this:

MCP gives Claude the tools.
Skills give it the recipe.

Without skills:

↳ You re-explain your sprint planning flow every week
↳ 15 back-and-forth messages
↳ Inconsistent outputs

With skills:

↳ Automatic activation
↳ Fewer tokens
↳ Fewer retries
↳ Same workflow, every time

And that’s one of the biggest value unlocks for Claude AI.

We’re moving from “clever prompts” to encoded workflows.

Claude is no longer just answering questions.
It’s running playbooks.

If you use Claude daily and don’t have at least one Skill, you’re still operating manually.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

The Complete Guide to Building Skills for Claude

Anthropic just dropped their own OpenClaw version - you can now orchestrate AI agents to write code on your server straight from your phone, without tying any commands or code 😳

AI giant Anthropic just added “Remote Control” to Claude Code.

Here’s what that actually means:

→ Your terminal session keeps running on your laptop.
→ You walk into a meeting.
→ You monitor, nudge, or fix it from your phone.

No SSH gymnastics.
No tmux + Tailscale stack.
No duct-taped open-source agent.

For months, builders played with OpenClaw (formerly ClawdBot) - a plugin-based agent that chains tools, edits files, and orchestrates apps.

Powerful. Flexible. Model-agnostic.

But remote oversight was full of fragile workarounds.

Anthropic just collapsed that gap.

With one simple command.

OpenClaw gave you autonomy.
Anthropic just gave you continuity.

And continuity is what turns an AI demo into a daily workflow.

P.S. check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

Anthropic just dropped their own OpenClaw version - you can now orchestrate AI agents to write code on your server straight from your phone, without tying any commands or code 😳  AI giant Anthropic just added “Remote Control” to Claude Code.  Here’s what that actually means:  → Your terminal session keeps running on your laptop. → You walk into a meeting. → You monitor, nudge, or fix it from your phone.  No SSH gymnastics. No tmux + Tailscale stack. No duct-taped open-source agent.  For months, builders played with OpenClaw (formerly ClawdBot) - a plugin-based agent that chains tools, edits files, and orchestrates apps.  Powerful. Flexible. Model-agnostic.  But remote oversight was full of fragile workarounds.  Anthropic just collapsed that gap.  With one simple command.  OpenClaw gave you autonomy. Anthropic just gave you continuity.  And continuity is what turns an AI demo into a daily workflow.

The most insane AI comeback ever? Google just posted its latest financials, and the numbers clearly show AI didn’t disrupt Google - it upgraded it 😳

For two years, the narrative was that Google missed the moment.

This earnings report quietly killed that take.

Because AI didn’t disrupt Google’s business.

It upgraded the machine that was already printing cash.

→ Revenue crossed $400B for the first time, with core ads still growing double digits

→ Gemini now has 750M monthly users - already at global consumer scale

→ Gemini models are pushing 10B+ tokens per minute through APIs

→ Google Cloud exited the year at a $70B run rate, growing 48% YoY - largely AI workloads

→ Subscriptions (YouTube, Google One, etc.) hit 325M paid users, many pulled in by AI features

But the part most people missed is this:

Most AI companies are trying to find a business model.

Sundar Pichai used AI to make Google’s existing businesses more valuable 🤖

↳ Search gets smarter → queries get longer → ads get better.
↳ Cloud gets AI demand → margins expand.
↳ Consumer apps get AI features → subscriptions rise.

Most importantly, AI isn’t a side project at Google.

It’s becoming the operating system for everything they already own.

That’s a very different kind of AI company.

One where AI strengthens the moat instead of trying to dig a new one.

P.S. check out 🔔linas.substack.com🔔, it's the only newsletter you need for all things when Finance meets AI. For founders, builders, and leaders.

The most insane AI comeback ever? Google just posted its latest financials, and the numbers clearly show AI didn’t disrupt Google - it upgraded it 😳    For two years, the narrative was that Google missed the moment.    This earnings report quietly killed that take.    Because AI didn’t disrupt Google’s business.    It upgraded the machine that was already printing cash.    → Revenue crossed $400B for the first time, with core ads still growing double digits    → Gemini now has 750M monthly users - already at global consumer scale    → Gemini models are pushing 10B+ tokens per minute through APIs    → Google Cloud exited the year at a $70B run rate, growing 48% YoY - largely AI workloads    → Subscriptions (YouTube, Google One, etc.) hit 325M paid users, many pulled in by AI features    But the part most people missed is this:    Most AI companies are trying to find a business model.    Sundar Pichai used AI to make Google’s existing businesses more valuable 🤖    ↳ Search gets smarter

For two years, everyone said Apple was losing the AI race. Turns out Apple might have been playing a completely different game the whole time 😳

While the industry burned $1.4 trillion+ training frontier models, Apple quietly waited.

Then, in January 2026, it made a very Apple move - partnered with Google to power Siri with Gemini AI.

Not “build the biggest model.”
Pick the winner. Integrate it everywhere 🤖

And tomorrow, Apple flips the switch.

AI Siri → 2.5 billion devices.

While everyone else is fighting for AI users, Apple already owns the devices on which those users live.

The contrast is quite wild:

→ $1.4T+ spent across the industry training models since 2023
→ Apple pays ~$1B/year to integrate the best one

→ Rivals chase subscriptions ($20-$200/month)
→ Apple runs AI on-device: faster, cheaper, private

→ Startups race to build AI hardware
→ Apple ships iPhone, iPad, Mac, Vision, Watch powered by its own AI chips

→ Competitors fight for distribution
→ Apple flips a switch across 2.5B devices

Sure, Apple didn’t win the LLM race.

But the AI model race might not have been the real game all along.

The real game is distribution + hardware + ecosystem.

And in that game, Apple starts the match with billions of users already logged in.

Turns out “being late to AI” might have been the most Apple strategy imaginable.

Most importantly, the AI era may not be decided by who builds the smartest model.

It might be decided by who owns the device that model runs on.

P.S. check out how I Turned Claude Cowork Into My Personal COO that runs work while I sleep 🧠: https://lnkd.in/eS6JCk2G

For two years, everyone said Apple was losing the AI race. Turns out Apple might have been playing a completely different game the whole time 😳  While the industry burned $1.4 trillion+ training frontier models, Apple quietly waited.  Then, in January 2026, it made a very Apple move - partnered with Google to power Siri with Gemini AI.  Not “build the biggest model.” Pick the winner. Integrate it everywhere 🤖  And tomorrow, Apple flips the switch.  AI Siri → 2.5 billion devices.  While everyone else is fighting for AI users, Apple already owns the devices on which those users live.  The contrast is quite wild:  → $1.4T+ spent across the industry training models since 2023 → Apple pays ~$1B/year to integrate the best one  → Rivals chase subscriptions ($20-$200/month) → Apple runs AI on-device: faster, cheaper, private  → Startups race to build AI hardware → Apple ships iPhone, iPad, Mac, Vision, Watch powered by its own AI chips  → Competitors fight for distribution → Apple flips a sw

Nailed it! Ex-Google CEO Eric Schmidt on the future of AI:

"If you really want to make money, found an agentic AI company. I mean, build an agent to do something. This is the agentic period in AI. Everyone's going to build agents. The agents are all going to compete."

And he’s right. The LLM race is maturing, while the AI agent race is just getting started.

For two years, we were obsessed over parameters, benchmarks, and leaderboards.

Meanwhile, AI stopped just answering questions and started taking actions.

→ Enterprises are already deploying agents internally at scale (McKinsey has reportedly rolled out tens of thousands).

→ AI Agents can now update CRMs, reconcile invoices, draft memos, ship code, and trigger workflows.

But the most underrated part is this: the agent itself commoditizes fast.

The moat is proprietary data, embedded workflows, and distribution.

Build accordingly.

P.S. check out how to Turn Claude Cowork Into Your Personal COO 🧠: https://lnkd.in/eS6JCk2G

Europe has:

→  Hardware that makes ALL frontier AI possible: ASML

→ Voice AI that sounds more human than humans: ElevenLabs 

→ Frontier LLMs: Mistral AI

Oh, and Europe also has regulations to ban all of these 😂

P.S. on a more serious note, check out how I built an AI operating system to run a startup with Claude 🤖: https://lnkd.in/dv7-ZRVc

Europe has:    →  Hardware that makes ALL frontier AI possible: ASML    → Voice AI that sounds more human than humans: ElevenLabs     → Frontier LLMs: Mistral AI    Oh, and Europe also has regulations to ban all of these 😂

New AI gig economy? Someone just launched a website where AI agents hire humans to do the work for them 😳

A new platform, RentAHuman, lets AI agents pay real people to do physical-world tasks - attend meetings, visit locations, be present where software can’t.

→ 4,800+ humans already listed as on-demand “meatspace” operators
→ 11 autonomous agents connected so far
→ 134 completed human “rentals”
→ Payments handled in crypto so agents can transact without humans in the loop

We spent years asking how AI would replace workers.

Now we’re watching humans become APIs 🤖

P.S. check out how I built an AI operating system to run a startup with Claude 🦄: https://lnkd.in/dv7-ZRVc

BBS Bank 

Head of Finance

KPMG Botswana

KPMG is a global network of professional firms providing Audit, Tax and Advisory services. We operate in 144 countries and have 137,000 people working in member firms around the world. With a worldwide presence, KPMG continues to build on its successes thanks to clear vision, defined values and, above all, its people. 

KPMG delivers a globally consistent set of multidisciplinary services based on deep industry knowledge. Our industry focus helps KPMG professionals develop a rich understanding of clients' businesses and the insight, skills, and resources required to address industry-specific issues and opportunities.

Specialties
Audit, Tax, Advisory

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