I built and sold a business to HubSpot in just 17 months.

I co-founded Mindstream, an AI newsletter that scaled to 150,000+ subscribers. Today, Mindstream is part of the HubSpot Media Network, where I lead brand strategy for the AI media products. 

Along the way, I’ve grown a 200,000+ following on LinkedIn, sharing real, unfiltered lessons on AI, entrepreneurship, and business growth. The wins and the mistakes.

I believe action beats hesitation. The biggest risk? Never starting.

Adam Biddlecombe

Helping Entrepreneurs Build with AI | Founder, Mindstream (Acquired by HubSpot)

Co-Founder & CEO 

98% think they’re using Claude perfectly.

They open the AI chat box and start prompting.

That’s exactly why they’re leaving 80% of its power unused.

When I first started using Claude, 
I made the same mistake.

I treated it like a chatbot.
Ask question → get answer → repeat.

But now, as I’ve set up Claude properly, 
Everything changed.

It started behaving like a system that actually works with me.
I got:
- Better writing.
- Better research.
- Better coding.
- Better reasoning.

Most people never unlock this because they skip the setup.

That’s why I’ve created this simple breakdown of how you can set up Claude the best way for you.

Here are the key ideas behind a powerful Claude setup:
→ Install Claude properly (desktop unlocks more capabilities)
→ Choose the right model for the job
→ Create a personal voice file Claude can reference
→ Define what you reject so Claude avoids bad habits
→ Give Claude real folders and context to work with
→ Enable features like artifacts and code execution
→ Add job-specific plugins for specialized work
→ Build reusable skills and workflows

When you configure Claude correctly, 
It starts acting like a reliable AI operator.

Do’s:
✅ Set profile preferences so Claude understands your writing style
✅ Give clear instructions and structured prompts
✅ Connect tools and folders for real context
✅ Create repeatable skills for frequent tasks
✅ Test different models based on the problem

Don’ts:
❌ Don’t rely on vague prompts and expect perfect outputs
❌ Don’t leave API keys or secrets in prompts or code
❌ Don’t ignore Projects when organizing complex work
❌ Don’t share sensitive data without checking security settings
❌ Don’t skip testing new Claude features and tools

If you set this up correctly, 
Claude becomes extremely powerful for:
• Writing
• Coding
• Research
• Analysis
• Workflow automation

To see the full setup breakdown, 
Check the infographic below 👇 
It will save you hours of trial and error.

Want to master how to use ChatGPT at work?
Grab my free guide here: https://lnkd.in/eJPyZ-Qe 

Curious to know something:
What do you think about setting up Claude for your workflow?
Comment below 👇

98% think they’re using Claude perfectly.

They open the AI chat box and start prompting.

That’s exactly why they’re leaving 80% of its power unused.

When I first started using Claude, 
I made the same mistake.

I treated it like a chatbot.
Ask question → get answer → repeat.

But now, as I’ve set up Claude properly, 
Everything changed.

It started behaving like a system that actually works with me.
Better writing.
Better research.
Better coding.
Better reasoning.

Most people never unlock this because they skip the setup.

That’s why I’ve created this simple breakdown of how you can set up Claude the best way for you.

Here are the key ideas behind a powerful Claude setup:
→ Install Claude properly (desktop unlocks more capabilities)
→ Choose the right model for the job
→ Create a personal voice file Claude can reference
→ Define what you reject so Claude avoids bad habits
→ Give Claude real folders and context to work with
→ Enable features like artifacts and code execution
→ Add 

If you want to be ahead of everyone in 2026,

You need to build your team of AI employees.

I used to think scaling meant working more hours and doing everything myself.

But now I did something different.
I built an AI team.

Every AI tool acts as an employee.
One for marketing.
One for development.
One for design.
One for research.
One for automation.
And more.

And suddenly, 
Work that took weeks,
Started finishing in hours.

Here’s how people are using AI as employees today:
→ ChatGPT as Chief Strategy Officer
→ Perplexity as Research Assistant
→ Claude as Writer
→ Canva as Designer
→ Zapier as Automation Engineer
→ Replit as Developer
→ HubSpot as Sales Manager
→ Beehiiv as Newsletter Manager
→ Taplio and Tweet Hunter as Ghostwriters
→ Gamma and VEED as Presentation and Video Experts

Most people fail with AI because they treat tools like toys.
High performers treat them like roles.

Do’s:
✅ Do assign one clear role per tool
✅ Do give examples and standards upfront
✅ Do review outputs like a manager
✅ Do combine tools into workflows
✅ Do keep humans in the decision loop

Don’ts:
❎ Don’t expect perfection on first output
❎ Don’t overload one tool with many roles
❎ Don’t skip context and instructions
❎ Don’t blindly trust outputs without review
❎ Don’t chase every new tool without a system

AI doesn’t replace teams.
It replaces friction.

If you want to know all the tools,
Check the infographic below 👇 

It visually maps how each AI fits into a real team.

If you like this and want to get free access to our complete ChatGPT at Work bundle guide, get it here: https://lnkd.in/e6yDSf4F 

What you think about building your own team of AI employees using powerful tools?
Comment below 👇

If you want to be ahead of everyone in 2026,

You need to build your team of AI employees.

I used to think scaling meant working more hours and doing everything myself.

But now I did something different.
I built an AI team.

Every AI tool acts as an employee.
One for marketing.
One for development.
One for design.
One for research.
One for automation.
And more.

And suddenly, 
Work that took weeks,
Started finishing in hours.

Here’s how people are using AI as employees today:
→ ChatGPT as Chief Strategy Officer
→ Perplexity as Research Assistant
→ Claude as Writer
→ Canva as Designer
→ Zapier as Automation Engineer
→ Replit as Developer
→ HubSpot as Sales Manager
→ Beehiiv as Newsletter Manager
→ Taplio and Tweet Hunter as Ghostwriters
→ Gamma and VEED as Presentation and Video Experts

Most people fail with AI because they treat tools like toys.
High performers treat them like roles.

Do’s:
✅ Do assign one clear role per tool
✅ Do give examples and standards upfront
✅ Do review out

Stop using ChatGPT for everything.

The smartest builders use specialised AI tools instead.

Because the right AI tool can save you hours every week.

I’ve noticed something interesting.

Everyone around me kept opening the same tab.

ChatGPT.

For:
Writing.
Research.
Presentations.
Data analysis.
Video ideas.
Coding.

Everything.

But the truth is simple:

ChatGPT is powerful.
But not always the best tool for every task.

The real advantage comes from using specialised AI tools built for specific workflows.

Once I started experimenting with alternatives, 
My productivity accelerated overnight.

Here are some powerful AI tools most people should be using alongside ChatGPT:

Research → SciSpace
Presentations → Gamma
Brainstorming → Claude
Learning → NotebookLM
Data analysis → Rows
Data visualization → Julius
Video generation → Freepik
Image generation → Higgsfield
Marketing copy → Writesonic
Scriptwriting → Sandcastles AI
Content creation → Veed
Development → Replit
Website development → Webflow
Customer support AI → Expertise AI
Deep research → Perplexity Deep Research
Meeting notes → Granola

Each tool is designed for a specific problem.
Which means better outputs and faster workflows.

Instead of forcing one AI to do everything:
✔ Use the best AI for the job
✔ Combine multiple AI tools
✔ Build faster workflows

And the results compound fast.

Do’s:
✔ Use specialized tools for specialized tasks
✔ Combine multiple AI tools in one workflow
✔ Verify important outputs with reliable sources
✔ Use AI to speed up thinking, not replace it
✔ Experiment with different tools regularly

Don’ts:
✖ Do not rely on a single AI tool for every task
✖ Do not skip fact-checking for research outputs
✖ Do not assume AI-generated data is always correct
✖ Do not ignore domain-specific AI tools
✖ Do not replace human judgement with AI

If you like this and want to get free access to our complete ChatGPT at Work bundle guide, get it here: https://lnkd.in/e6yDSf4F 

To see the full breakdown of tools,
Check the infographic below 👇 

And I am curious
Which other AI tool alternative do you think works better than ChatGPT for a specific task?
Drop it in the comments 👇

Stop using ChatGPT for everything.

The smartest builders use specialised AI tools instead.

Because the right AI tool can save you hours every week.

I’ve noticed something interesting.

Everyone around me kept opening the same tab.

ChatGPT.

For:
Writing.
Research.
Presentations.
Data analysis.
Video ideas.
Coding.

Everything.

But the truth is simple:

ChatGPT is powerful.
But not always the best tool for every task.

The real advantage comes from using specialised AI tools built for specific workflows.

Once I started experimenting with alternatives, 
My productivity accelerated overnight.

Here are some powerful AI tools most people should be using alongside ChatGPT:

Research → SciSpace
Presentations → Gamma
Brainstorming → Claude
Learning → NotebookLM
Data analysis → Rows
Data visualization → Julius
Video generation → Freepik
Image generation → Higgsfield
Marketing copy → Writesonic
Scriptwriting → Sandcastles AI
Content creation → Veed
Development → Replit
Website development

Quality of learning always beats quantity of content.

We live in a world where information is infinite.
But attention is not.

For a long time, I thought staying ahead meant consuming more.
More books. 
More newsletters. 
More podcasts. 
More tutorials.

It does not.
It means learning with intention.

Early on, books like Find Your Why shaped how I thought about purpose and direction. 
They gave me language for decisions I could already feel but could not yet explain.

Today, I still return to ideas like that.

Just differently.

I use Shortform.

Not to replace books.
But to understand them better.

Shortform creates book guides that go beyond summaries:

- Deep coverage of core ideas
- Smart analysis and connections across authors
- Interactive exercises that make ideas stick

It feels like your smartest friend walking you through a book.

Three categories I personally spend the most time on:

- Business and entrepreneurship
- Psychology and decision-making
- Communication and negotiation

Here are my favorite books I recommend to learn from:

1. Magic of Thinking Big.
You’ll learn
- Think bigger to unlock higher confidence, ambition, and results.
- Beliefs shape actions, and actions determine success.
- Positive thinking enables better decisions and consistent progress.

2. How to Win Friends and Influence People.
You’ll learn:
- Genuine interest in others builds trust and influence.
- Avoid criticism to maintain strong relationships.
- Make people feel valued to earn cooperation.

3. How to Get Rich
You’ll learn:
- Persistence matters more than talent or luck.
- Wealth comes from execution, not ideas alone.
- Long term success requires resilience and self belief.

The best part is:
On Shortform, I can combine learning from these best books.
To know what I should focus on in the long term.

That is where real leverage comes from.

Shortform also releases new book and article guides every week.
And I get to vote on what gets covered next.

And when I am reading blogs or watching long YouTube videos, 
Their browser extension, Shortform AI, 
Helps me generate high-quality summaries instantly. 
Just in one click.

The result is simple:
- I spend less time skimming.
- More time thinking.
- More time connecting ideas.
- More time building judgement.

Learning compounds when the input is high quality.

If your goal is not just to consume content, 
But to sharpen how you think, 
Shortform is worth exploring.

You do not need more information.
You need better understanding.

Try here: shortform.com/biddlecombe 

Get a free trial and $50 off the annual plan from my link. 

What do you think about learning this new way?
Comment below 👇 

#shortformpartner

Most people think they understand AI.

Until they realise they only recognise buzzwords.

I’ve curated the 40 important AI terms here to fix that gap.

A few months ago, I noticed something strange.

Smart professionals were using AI every day.
But struggling to explain it clearly.

They could say "LLM", "agents", or “hallucinations”.
But not understanding what those actually mean or why they matter.

That is the difference between using AI.
And thinking with AI.

So I broke it down here.

Why this matters more than people realise:
→ Clarity compounds faster than just using AI tools
→ Understanding beats memorising prompts
→ Vocabulary shapes how well you use AI

A few terms most people misuse daily:
• Bias → When training data tilts outputs
• Tokens → How models read and write text
• Inference → Using learned knowledge in real time
• Overfitting → When AI memorizes instead of generalizing
• Hallucinations → Confident answers without factual grounding

Do’s:
✅ Learn concepts before tools
✅ Connect terms to real workflows
✅ Understand limits, not just capabilities
✅ Revisit fundamentals regularly
✅ Explain ideas in your own words

Don’ts:
❎ Chasing tools without understanding basics
❎ Confusing AGI with current LLMs
❎ Ignoring training and data quality
❎ Treating prompts as everything
❎ Assuming AI is always correct

If you want leverage from AI,
You must speak its language first.

To get a quick scan of all 40 AI terms,
Check the infographic below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

How much time do you invest in learning AI every day?
Comment below 👇

Most people think they understand AI.

Until they realise they only recognise buzzwords.

I’ve curated the 40 important AI terms here to fix that gap.

A few months ago, I noticed something strange.

Smart professionals were using AI every day.
But struggling to explain it clearly.

They could say "LLM", "agents", or “hallucinations”.
But not understanding what those actually mean or why they matter.

That is the difference between using AI.
And thinking with AI.

So I broke it down here.

Why this matters more than people realise:
→ Clarity compounds faster than just using AI tools
→ Understanding beats memorising prompts
→ Vocabulary shapes how well you use AI

A few terms most people misuse daily:
• Bias → When training data tilts outputs
• Tokens → How models read and write text
• Inference → Using learned knowledge in real time
• Overfitting → When AI memorizes instead of generalizing
• Hallucinations → Confident answers without factual grounding

Do’s:
✅ Learn concepts before too

I’ve seen people waste months jumping between random AI hacks.

So I’ve created this one all-in-one AI cheat sheet for you.

To help you master everything important in one go.

Let me be brutally honest.

While you were writing one-line prompts, 
AI pros were using the RTCROS framework to get better results.

While you were guessing prompts, 
They were using chain-of-thought prompting to make AI think step by step.

While you were struggling with manual work, 
They were automating entire workflows with tools you've never even heard of.

Here's what the best AI users know that you don't:

Prompting tips to get 10x better outputs:
→ Zero-Shot gets instant results with no examples
→ Few-Shot guides AI with just 2-3 samples
→ Chain-of-Thought makes AI show its reasoning
→ Persona-Based makes AI adopt expert roles
→ Iterative Refinement keeps making outputs sharper

Tools they're using daily:
✅ Google Nano Banana Pro for stunning visuals in seconds
✅ Cursor for writing code 
✅ Zapier for automating tasks while they sleep
✅ Consensus for research that used to take weeks
✅ Gamma for presentations that look professionally designed

The AI models dominating right now:
→ ChatGPT-5.2 for adaptive multimodal thinking
→ Claude Opus 4.6 for deep reasoning and complex tasks
→ Grok 4.2 for live data and stronger creativity
→ Gemini 3.1 Pro for long-context multi-step workflows
→ Kimi K2 Thinking for hard analytical problems

Some business use cases where AI helps:
→ Digital operations getting fully automated
→ Lead generation running on autopilot
→ Sales cycles being cut in half
→ Marketing campaigns launching without agencies
→ Finance management handled without analysts

The crazy part?
All of this is learnable. 
Right now. 
For free.

The gap between people who invest in learning AI daily and those who don't is growing.

In the next 12 months, 
The professionals who mastered AI prompting, tools, and workflows will be untouchable in their careers.

The ones who didn't will be wondering what happened.
Actually using it every day is where winners are made.

Learn AI for free: https://lnkd.in/euYZeAdb 

How much time per day do you invest in learning AI? 
Be honest, and comment below 👇

I’ve seen people waste months jumping between random AI hacks.

So I’ve created this one all-in-one AI cheat sheet for you.

To help you master everything important in one go.

Let me be brutally honest.

While you were writing one-line prompts, 
AI pros were using the RTCROS framework to get better results.

While you were guessing prompts, 
They were using chain-of-thought prompting to make AI think step by step.


While you were struggling with manual work, 
They were automating entire workflows with tools you've never even heard of.

Here's what the best AI users know that you don't:

Prompting tips to get 10x better outputs:

→ Zero-Shot gets instant results with no examples
→ Few-Shot guides AI with just 2-3 samples
→ Chain-of-Thought makes AI show its reasoning
→ Persona-Based makes AI adopt expert roles
→ Iterative Refinement keeps making outputs sharper

Tools they're using daily:
✅ Google Nano Banana Pro for stunning visuals in seconds
✅ Cursor for writing code 
✅ Zapier for 

Drowning in huge PDFs?

You don’t need more coffee.
You need AI assistance.

Here’s how to use AI for document analysis.

1. Go to ChatGPT
2. Click the ‘+’ icon
3. Add your document
4. Write your prompt
5. Get document analysis done
6. Follow up if you need more insights

Pros:
- Ultra-fast summarisation of long texts.
- Good at extracting key entities, relationships, and topics.
- Can handle multiple formats (text, tables) if converted appropriately.
- Provides a “first-pass” analysis so you don’t start from blank.
- Repeatable and consistent (for the same prompt & input).

Cons:
- May (rarely) hallucinate or invent details that aren’t in the document.
- Doesn’t always catch domain-specific nuance or technical subtleties.
- Performance degrades on very long documents (context window limits).
- No guarantee of source attribution or traceability.
- Biases in training data may skew interpretation.

Do’s
✅ Always give context or purpose before asking for analysis.
✅ Break large docs into smaller chunks for processing.
✅ Ask for both summary and insights/implications.
✅ Request sources or cite-backs for key claims.
✅ Iterate (ask follow-up clarifications) instead of expecting perfection on the first prompt.

Don'ts
❎ Don’t dump an enormous document in one go without context.
❎ Don’t trust the output blindly; always verify critical facts.
❎ Don’t ask vague prompts like “analyse this” without direction.
❎ Don’t ignore structure (tables, figures, sections); treat them explicitly.
❎ Don’t overload with too many goals in one prompt (summary + critique + rewrite + translate all at once).

To get a more detailed explanation,
Check the carousel below 👇 

What do you think of using AI for document analysis?
Comment below 👇 

Learn AI for FREE: https://lnkd.in/eAAacxSK  

♻️ Share this to help your network use AI for document analysis

How To Do Document Analysis For Free With ChatGPT

You don’t need to be technical. Just Informed (400k agree).

CEO of The AI Report — a 400,000+ subscriber daily briefing focused exclusively on practical AI for enterprise and B2B professionals.Our ecosystem:NewsletterTrusted by teams at Google, Microsoft, OpenAI, and 400,000 others. Sponsors include Salesforce, HubSpot, IBM, Zapier, Brave, and many more. Podcast – The AI Report LiveConversations with operators and founders behind today’s AI transformation.Past guests include leaders from Salesforce, Slack, Shopify, and Asana — exploring both how AI is deployed at scale and why builders do what they do.UpscaileOur enterprise arm delivering AI audits, corporate training, and implementation strategy.Partners include SHRM, Merz Aesthetics, Stanford, and enterprise leadership teams across North America. We help AI companies drive real adoption, enterprise teams deploy AI with clarity.If you're building or scaling AI for business, let’s connect.

Liam Lawson

I removed 4 tools from my workflow this week.

One thing replaced them.

Before, making a presentation looked like this:

→ Brainstorm in Claude 
→ Copy notes into Google Docs 
→ Open Canva or PowerPoint 
→ Rebuild the structure from scratch 
→ Go back to Claude to refine 
→ Update the deck manually 
→ Repeat every time thinking changed

Every tool switch costs you momentum. And momentum is where ideas die.

Now it looks like this:

1. Open Claude
2. Click "Connectors" in the bottom left
3. Search "Gamma" and click Connect
4. Start a conversation: brainstorm, outline, think out loud
5. Type: "Turn this into a presentation"
6. Gamma builds the deck inside Claude
7. Keep refining in the same conversation
8. Deck updates every time your thinking does

No copy-paste. No tab switching. No rebuilding from scratch.

The tools I removed: 
→ Google Docs (for drafting) 
→ Canva (for slides) 
→ Notion (for storing half-finished ideas) 
→ Email threads (for sharing drafts)

Claude + Gamma replaced the whole stack.

Setup takes 30 seconds. One prompt builds your deck.

This is what AI is supposed to feel like.

P.S. Want to learn more about AI?

1. Scroll to the top
2. Click 'Visit my website'
3. Sign-up for our free newsletter

Get started with Gamma: https://lnkd.in/eXY7u_Gi

I Removed 4 Tools From My Workflow This Week

ByteDance just dropped Seedance 2.0.

And it's not just a better video model.
It's a different kind of tool entirely.

Here's what changed and how to actually use it:

Previously, AI video meant type a prompt and hope for the best.
Seedance 2.0 gives you real inputs to work with.

You can now feed it:
→ Up to 9 images at once
→ 3 video clips as reference
→ 3 audio clips
→ Plus your text prompt

All in a single generation.
That's not a small update.
That's a completely different workflow.

𝟭. 𝗖𝗼𝗺𝗽𝗹𝗲𝘅 𝗺𝗼𝘁𝗶𝗼𝗻 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸𝘀 𝗻𝗼𝘄

Pair figure skating, multi-person fight scenes, large crowd interactions.
Previous models fell apart here.
Seedance 2.0 holds up.
Physical accuracy is the big unlock.
Characters move like they have actual weight.

𝟮. 𝗬𝗼𝘂 𝗰𝗮𝗻 𝗿𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝘀𝘁𝗼𝗿𝘆𝗯𝗼𝗮𝗿𝗱𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆

Drop in a hand-drawn shot list and the model reads composition, camera movement, and pacing from it.
Directors and content teams, this one's for you.

𝟯. 𝗩𝗶𝗱𝗲𝗼 𝗲𝗱𝗶𝘁𝗶𝗻𝗴 𝗶𝘀 𝗻𝗼𝘄 𝗯𝘂𝗶𝗹𝘁 𝗶𝗻

Not just generation anymore.
You can target specific clips and change characters, actions, and storylines without starting from scratch.

𝟰. 𝗩𝗶𝗱𝗲𝗼 𝗲𝘅𝘁𝗲𝗻𝘀𝗶𝗼𝗻 𝗶𝘀 𝗿𝗲𝗮𝗹

Generate a clip, then tell it to keep going.
It continues the shot logically, not randomly.

𝟱. 𝗗𝘂𝗮𝗹-𝗰𝗵𝗮𝗻𝗻𝗲𝗹 𝘀𝘁𝗲𝗿𝗲𝗼 𝗮𝘂𝗱𝗶𝗼

Background music, ambient sound effects, and character voiceovers all generated together and synced to the visual rhythm.
ASMR, cinematic audio, dialogue.
It handles all of it natively.

Seedance 2.0 isn't perfect and they admit it themselves.
Multi-person lip sync still needs work.
Hyper-realism has limits.
Text rendering accuracy isn't there yet.

But the gap between "AI video toy" and "production tool" just got a lot smaller.

Who should pay attention:
→ Video marketers running ad creative
→ Content teams doing product demos
→ Agencies exploring AI production pipelines
→ Solo creators who can't afford a full crew

This isn't replacing editors.
It's giving individuals studio-level starting points.

P.S. Want to learn more about AI?

1. Scroll to the top
2. Click "Visit my website"
3. Sign-up for our free newsletter

The #1 AI Newsletter & Podcast for Business Leaders (400k agree).

Meetings eat 23 hours a week for the average manager.

Nobody remembers what was decided.

Here are 8 AI tools that fix that:

𝟭. 𝗢𝘁𝘁𝗲𝗿.𝗮𝗶
→ Transcribes meetings in real-time
→ Highlights action items automatically
→ Searchable archive of every conversation
→ Stop taking notes, start participating

𝟮. 𝗙𝗶𝗿𝗲𝗳𝗹𝗶𝗲𝘀
→ Records, transcribes, and summarizes meetings
→ Integrates with Zoom, Teams, Google Meet
→ Auto-shares notes with the team
→ Never ask "what did we decide?" again

𝟯. 𝗙𝗮𝘁𝗵𝗼𝗺
→ Free AI meeting assistant
→ Summarizes key points instantly
→ Clips important moments to review later
→ Works across all video platforms

𝟰. 𝗚𝗿𝗮𝗶𝗻
→ Records meetings and creates highlight reels
→ Perfect for sales calls and customer feedback
→ Share key moments without full recordings
→ Helps teams learn from best conversations

𝟱. 𝗧𝗮𝗰𝘁𝗶𝗾
→ Chrome extension for instant transcripts
→ Works with Google Meet, Zoom, Teams
→ Creates summaries with one click
→ No bot joining the meeting

𝟲. 𝗦𝗲𝗺𝗯𝗹𝘆
→ AI meeting assistant with task tracking
→ Identifies risks and next steps automatically
→ Creates meeting minutes in seconds
→ Integrates with project management tools

𝟳. 𝘁𝗹;𝗱𝘃
→ Records and timestamps key moments
→ Multi-language transcription
→ Searchable library of all meetings
→ Free plan for unlimited recordings

𝟴. 𝗙𝗲𝗹𝗹𝗼𝘄
→ Meeting agendas that actually get used
→ Collaborative note-taking during calls
→ Action items assigned and tracked
→ Connects meetings to workflows

The pattern across all 8?

They eliminate the worst parts of meetings.
Taking notes while trying to contribute.
Remembering who committed to what.
Recapping for people who missed it.

Most teams waste 5-10 hours weekly on meeting admin.

These tools cut that to zero.

Pick one tool. Test it for a week. Track the hours saved.

P.S. Want to learn more about AI?

1. Scroll to the top
2. Click 'Visit my website'
3. Sign-up for our free newsletter

8 Meeting Tools That Save Hours Every Week

“I think we’ve taught computer science wrong in schools.”

That’s how Anthony Salcito frames one of the biggest challenges in preparing people for the future of work.

For years, education systems have focused on teaching technology for technology’s sake:

Learn a programming language.

Build a robot.

Understand a specific tool.

The problem? Many of those tools won’t even exist by the time students enter the workforce.

Anthony’s argument is simple:

Technology should never be the starting point.

It should be the fuel.

The real starting point should be:

 • A problem to solve
 • An opportunity to explore
 • A challenge that requires creativity

When technology is connected to real-world problems, something interesting happens.

Students move faster.

They see the relevance.

They become more creative in how they apply tools.

And the same principle applies inside organizations adopting AI.

If teams are learning tools without connecting them to real problems, adoption stalls.

But when technology is framed as a way to solve meaningful challenges, people lean in.

→ The future of learning isn’t about teaching tools.

It’s about teaching people how to use tools to solve problems.

Google just dropped Nano Banana 2.

And it’s not just a faster image model.
It’s a different kind of creative tool.

Here’s what changed and how to actually use it:

Before, you had to choose.
Speed or quality.

Nano Banana 2 combines both.
Pro-level reasoning at Flash speed.

That’s not incremental.
That’s a workflow shift.

1. It understands the world
It pulls from Gemini’s knowledge and web grounding.
Landmarks, objects, concepts render more accurately.
Infographics and diagrams are actually usable.

2. Text inside images works
Legible marketing copy.
Greeting cards.
Localized visuals in different languages.
Less fixing in Photoshop after.

3. Subject consistency holds up
Up to 5 characters and 14 objects in one workflow.
Storyboards don’t collapse.
Characters don’t morph between frames.

4. It follows instructions better
Lighting, framing, aspect ratios, object placement.
You get closer to what you actually asked for.
Fewer re-prompts.

5. Production specs are built in
From 512px to 4K.
Vertical social to wide-screen backdrops.
Ready to ship.

It’s not perfect.

But the gap between “AI image toy” and “production tool” just got smaller.

Fast + grounded + consistent.

P.S. Want to learn more about AI? 

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AI gives you more answers but you still can't decide?

The problem isn't the tool.
It's how you're asking.

You dump a fuzzy problem into ChatGPT.
It generates a 500-word response.
You read it all.
You still don't know what to do next.

This feels like progress.
It's just expensive procrastination.

You're using AI to think more.
You should use it to decide faster.

𝗥𝗲𝗳𝗿𝗮𝗺𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗮𝘀𝗸
Don't ask: "Should I do X or Y?"
Ask: "What's the one reason not to do X?"
Specific questions get useful answers.

𝗖𝗵𝗲𝗰𝗸 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗸𝗻𝗼𝘄
Most decisions don't need more information.
They need courage to act on what you have.
Ask: "What would I decide with zero new data?"

𝗥𝗲𝗾𝘂𝗲𝘀𝘁 𝗰𝗼𝗻𝘀𝘁𝗿𝗮𝗶𝗻𝘁𝘀, 𝗻𝗼𝘁 𝗼𝗽𝘁𝗶𝗼𝗻𝘀
Don't ask for 10 strategies.
Ask: "Which one option works with [constraint]?"
Limits force clarity.

𝗦𝘂𝗿𝗳𝗮𝗰𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗿𝗶𝘀𝗸
Ask: "What's the worst realistic outcome here?"
Not catastrophizing.
Realistic downside assessment.

𝗗𝗲𝗺𝗮𝗻𝗱 𝗼𝗻𝗲 𝗮𝗰𝘁𝗶𝗼𝗻
End every prompt with:
"Give me one thing I can do in 30 minutes."
Executable beats theoretical.

Before: "Help me think about this problem"
After: "Help me stop thinking and start moving"

AI works when it closes loops.
Not when it opens more rabbit holes.

P.S. Want to learn more about AI?

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IF AI DISAPPOINTS YOU, READ THIS

Anthropic released the complete guide to Claude Skills.

Here’s what to expect in the guide:

→ Skill structure and requirements
→ Planning and design best practices
→ Testing and iteration workflows
→ Distribution and sharing methods
→ MCP plus Skills integration
→ Troubleshooting patterns

Who this is for:

→ Developers building repeatable workflows
→ Power users automating Claude tasks
→ Teams standardizing Claude usage
→ MCP builders adding workflow intelligence

This guide is worth reading if you use Claude seriously.

P.S. Want to learn more about AI?

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3. Sign-up for our free newsletter

The Complete Guide to Building Skills for Claude

“Do you think AI is a force for good?”

When asked directly, Christian Lund gave an unexpectedly candid answer:

“In the greater perspective, I honestly think no.”

Not because AI doesn’t bring value. It absolutely does.

But because technologies of this magnitude reshape incentives, power structures, labor markets, and governance, often in ways we don’t fully understand until much later.

Christian’s point wasn’t fatalistic.

 It was pragmatic.

AI will create extraordinary upside:

-Productivity gains
-Scientific acceleration
-New business models

But it will also introduce:

-Concentration of power
-Workforce disruption
-Security and governance risks

And here’s the reality: it’s already in motion. There’s no rewind button.

So the question isn’t whether AI is inherently good or bad.

It’s whether leaders are prepared to:

-Maximize the upside
-Actively mitigate the downside
-Build responsibly while moving quickly

Optimism without guardrails is naïve.
Pessimism without action is unproductive.

→ The real work now is shaping outcomes, not debating inevitability.

#AI #FutureOfWork #TechLeadership #Innovation #ResponsibleAI

Seven years ago, the bet was simple: globalization wouldn’t last.

As Peter Swartz explains, the founding vision wasn’t about incremental improvement. It was about a structural shift that rising geopolitical tensions, especially between the U.S. and China, would eventually force companies to rethink how and where they build.

That thesis hasn’t changed.

The assumptions underpinning decades of globalization... just-in-time sourcing, frictionless trade, stable geopolitical alignment, were already showing cracks.

You could see:
-Security competition increasing
-Strategic decoupling accelerating
-Supply chains becoming fragile rather than resilient

The global system wasn’t collapsing overnight.
It was fragmenting.

And in a fragmented world, businesses don’t just need suppliers.
They need platforms that allow them to rearrange supply chains dynamically.

What looked contrarian seven years ago now feels structural.

→ The future of global trade won’t be defined by efficiency alone.
It will be defined by resilience, optionality, and geopolitical awareness.

#Globalization #SupplyChainStrategy #Geopolitics #EconomicTrends #BusinessStrategy

Every company has customer data.

Almost none of them know what it's actually saying.

Here are 10 insights AI can find that change everything:

𝟭. 𝗖𝗵𝘂𝗿𝗻 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 𝟵𝟬 𝗗𝗮𝘆𝘀 𝗢𝘂𝘁
→ AI spots patterns before customers cancel
→ Decreased login frequency plus support tickets
→ Identifies who to save before they leave
→ Saves 30-40% of at-risk accounts

𝟮. 𝗧𝗵𝗲 𝗛𝗶𝗱𝗱𝗲𝗻 𝗛𝗶𝗴𝗵-𝗩𝗮𝗹𝘂𝗲 𝗦𝗲𝗴𝗺𝗲𝗻𝘁
→ Small group spending 10x more than average
→ Different behavior patterns than typical customers
→ Often ignored in broad marketing campaigns
→ Worth targeting specifically

𝟯. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗡𝗼𝗯𝗼𝗱𝘆 𝗨𝘀𝗲𝘀 (𝗧𝗵𝗮𝘁 𝗖𝗼𝘀𝘁 $𝟱𝟬𝟬𝗞)
→ AI tracks actual usage versus perceived value
→ Shows what customers click versus what they say
→ Kills projects that don't matter
→ Redirects resources to what works

𝟰. 𝗧𝗵𝗲 𝗨𝗽𝘀𝗲𝗹𝗹 𝗧𝗶𝗺𝗶𝗻𝗴 𝗦𝘄𝗲𝗲𝘁 𝗦𝗽𝗼𝘁
→ AI finds the exact moment customers want more
→ Not too early, not after they've found alternatives
→ Usually 3-4 months into usage
→ Doubles upgrade conversion rates

𝟱. 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 𝗧𝗶𝗰𝗸𝗲𝘁𝘀 𝗧𝗵𝗮𝘁 𝗣𝗿𝗲𝗱𝗶𝗰𝘁 𝗘𝘅𝗽𝗮𝗻𝘀𝗶𝗼𝗻
→ Certain questions signal growth not frustration
→ "Can we add more users?" buried in tickets
→ Sales never sees these buying signals
→ AI surfaces them automatically

𝟲. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗖𝗼𝗺𝗯𝗼𝘀 𝗧𝗵𝗮𝘁 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝟯𝘅
→ Customers using Feature A + Feature B never leave
→ Single feature users churn at normal rates
→ Most companies never connect these dots
→ Changes entire onboarding strategy

𝟳. 𝗧𝗵𝗲 𝗦𝗲𝗮𝘀𝗼𝗻𝗮𝗹 𝗣𝗮𝘁𝘁𝗲𝗿𝗻 𝗡𝗼𝗯𝗼𝗱𝘆 𝗡𝗼𝘁𝗶𝗰𝗲𝗱
→ Revenue spikes or dips at specific times
→ Tied to customer industry cycles
→ Humans see randomness, AI sees patterns
→ Helps forecast and staff appropriately

𝟴. 𝗡𝗲𝗴𝗮𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗧𝗵𝗮𝘁'𝘀 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗣𝗼𝘀𝗶𝘁𝗶𝘃𝗲
→ Complaints about advanced features mean engagement
→ Users who complain stick around longer
→ The quiet customers are the ones leaving
→ Changes how support prioritizes issues

𝟵. 𝗧𝗵𝗲 𝗥𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝗦𝗼𝘂𝗿𝗰𝗲 𝗧𝗵𝗮𝘁 𝗔𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗪𝗼𝗿𝗸𝘀
→ 80% of referrals come from one customer type
→ Not the biggest accounts or longest tenured
→ Usually mid-tier customers in specific industries
→ Focus referral programs there

𝟭𝟬. 𝗣𝗿𝗶𝗰𝗲 𝗥𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 𝗧𝗵𝗮𝘁 𝗜𝘀𝗻'𝘁 𝗔𝗯𝗼𝘂𝘁 𝗣𝗿𝗶𝗰𝗲
→ "Too expensive" actually means "don't see value"
→ AI correlates objections with onboarding gaps
→ Shows what features to demonstrate first
→ Turns price objections into sales

Most companies analyze customer data quarterly.

By then the insights are history.

The data is already there.
The insights are already there.
The question is whether AI finds them first for competitors.

P.S. Want to learn more about AI?

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10 Customer Insights Hiding in Your Data That AI Can Find

The biggest challenge with AI adoption isn’t technical. It’s cultural.

In a recent conversation, Anthony Salcito highlighted a pattern we see in every major technology shift.

When a new paradigm emerges, organizations naturally focus on tools, infrastructure, and strategy.

But the real barrier is often something else: fear of displacement.

Many employees aren’t resisting AI because they lack access to the technology.

They’re asking a much more personal question:

“If I learn this tool, will it eventually replace me?”

That’s why successful AI adoption requires more than training programs.

It requires clear leadership alignment.

Organizations need to communicate, consistently and convincingly and that AI is meant to amplify human capability, not eliminate it.

The goal should be to help skilled employees:

- Work faster
- Think more creatively
- Unlock new opportunities for themselves and the business

As Anthony emphasizes, this mindset can’t just live in presentations or strategy documents.

It has to be embedded in the DNA of the company.

Because when people see AI as leverage instead of a threat, adoption becomes a lot easier.

→ Technology transformation only works when the culture evolves with it.

#AI #FutureOfWork #Leadership #DigitalTransformation #AnthonySalcito

OpenAI just introduced ads into ChatGPT. That’s a structural shift, not a feature update.

As of February 9, free users and those on the new $8/month plan in the U.S. will see clearly labeled sponsored content below responses. Think: helpful output, followed by a contextual ad.

Plus, Pro, and Enterprise tiers remain ad-free.

On paper, this looks incremental.

In reality, it marks a major evolution in the AI business model.

Internal projections reportedly estimate:

- ~$1B in ad revenue this year
- Scaling toward $25B by 2029
- $200K minimum buy-in for early advertisers

OpenAI says ads won’t influence answers and advertisers won’t access user conversations.

But the strategic shift is undeniable.

The company that positioned itself as building AI for humanity is now building an advertising platform alongside it.

Meanwhile, competitors like Anthropic are publicly differentiating on this exact issue.

This isn’t just monetization.

It’s the beginning of a philosophical and structural divide in the AI industry:

Ad-supported intelligence
vs.
Subscription-first, privacy-positioned intelligence

→ The question for leaders isn’t whether ads work.

It’s what incentives shape the systems your teams rely on.

#OpenAI #ChatGPT #AIIndustry #DigitalAdvertising #FutureOfTech

Most people treat prompting like a paragraph. Arturo treats it like a system.

In the episode, Arturo Ferreira makes a subtle but powerful point:

When you write a prompt, you’re asking the AI to make dozens of judgment calls on your behalf.

If you’re vague, it fills in the gaps.
If you’re unstructured, it guesses.

One simple technique he emphasizes? Hashtagging your prompts.
Not for social reach, for structure.

Hashtags act as signposts.

 They tell the system:

-What section is what
-Where each idea belongs
-How to organize the response

You’re not just writing instructions.

You’re labeling your tools.

Instead of one long block of text, you create clear segments:
 #Context
 #Task
 #Constraints
 #OutputFormat

That structure reduces ambiguity and improves alignment instantly.

You’re organizing the frame so the system knows exactly where to look.

This isn’t about tricking the model.

 It’s about thinking clearly enough that the model doesn’t have to.

→ Better structure = better judgment calls.

#PromptEngineering #AIWorkflows #GenerativeAI #DigitalSkills #FutureOfWork

Enterprise sales has flipped. It no longer starts with the buyer. It starts with the user.

For years, enterprise software followed a familiar playbook: sell to one executive, push the tool across thousands of employees, and hope adoption followed.

That model is breaking.

Today’s reality inside companies looks very different:

-Employees experiment with tools on their own.
-“Shadow IT” introduces solutions before leadership approves them.
-Adoption happens at the user level first, procurement second.

And honestly, that’s not a bad thing.

It forces products to prove their value in the real world.

If users don’t immediately see the benefit, they simply won’t use the tool, no matter how large the enterprise contract is.

That changes the role of enterprise sales.

Instead of pushing software top-down, companies now have to align with what users are already trying to do and build around that behavior.

In other words:
Adoption → then procurement.

Not the other way around.

→ In the AI era especially, the tools that win will be the ones users pick up instantly.

#EnterpriseSales #ProductAdoption #FutureOfWork #SaaS #AIProducts

In the AI era, brand isn’t getting less important. It’s getting more important.

In the conversation, Christian Lund makes a point that often gets overlooked in tech cycles:

When new technology moves this fast, trust becomes the deciding factor.

Not just trust in the output.

But trust in the company behind it.

Organizations aren’t just evaluating tools anymore. They’re asking:

•⁠ ⁠Can we rely on this platform long term?
•⁠ ⁠Will this company still be here in two years?
•⁠ ⁠Is their vision clear enough that we understand where this technology is going?

That’s where brand actually matters.

Not in the form of polished slides or glossy marketing, but in how a company communicates what it stands for and where the technology is heading.

Christian points out that something interesting is coming back: thought leadership.

In moments of uncertainty, the companies that win aren’t just building products.

They’re helping people understand the future those products are creating.

The strongest brands today are the ones that can clearly articulate:

What this technology means today.

What it will mean in a year.

And how it will reshape their industry.

Because in emerging technology markets, clarity builds trust.

→ And trust ultimately builds the brand.

#BrandStrategy #ThoughtLeadership #AI #TrustInTechnology #FutureOfWork

Gemini 3.1 Flash-Lite is here.

$150 workflows now cost $1.50.

Google launched Gemini 3.1 Flash-Lite with pricing that breaks the cost model for volume work.

$0.25 per 1M input tokens.
$1.50 per 1M output tokens.
45% faster than Gemini 2.5 Flash.

Claude Sonnet charges $15 per million outputs.
GPT-4o charges $10 per million outputs.
Gemini 3.1 charges $1.50.

That's 6x to 10x cheaper with comparable quality.

The innovation isn't just price.
It's adjustable thinking levels.

You control how hard the model works per task.
Light thinking for simple jobs.
Deep thinking for complex reasoning.
Pay only for the intelligence you need.

This kills the old trade-off where you either overpay for simple tasks or settle for lower quality to control costs.

Now you optimize both per task.

Available in Google AI Studio and Vertex AI (preview).

Test it on bulk content creation, UI generation, customer data processing, automated reports, mass personalization.

Anything touching thousands of items just dropped 90% in cost.

Projects you shelved because unit economics didn't work?
Recalculate with $1.50 per million outputs.

Most just became viable.

P.S. Want to learn more about AI?

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Sprint planning without test planning?
That's technical debt waiting to happen.

Here's how KaneAI by TestMu AI (formerly LambdaTest) helps QA teams turn user stories into test coverage before the sprint even starts.

Five ways to use it:

→ Turn user stories into test scenarios

Paste your user story into KaneAI.
It generates structured test cases instantly.
You review and prioritize which ones matter most.
Outcome: test plan ready before development starts.

→ Generate acceptance criteria tests

Feed KaneAI your acceptance criteria.
It creates executable tests in plain English.
You validate the logic matches requirements.
Outcome: definition of done includes automated tests.

→ Create API validation tests from specs

Share your API documentation with KaneAI.
It builds test suites with intelligent assertions.
You decide which endpoints need coverage.
Outcome: backend tests ready when feature ships.

→ Build regression suites for new features

Describe the feature behavior in natural language.
KaneAI generates regression test scenarios.
You organize them by priority and risk.
Outcome: protect existing functionality automatically.

→ Estimate test coverage gaps

Show KaneAI what you're building this sprint.
It identifies areas missing test scenarios.
You decide if the gaps need coverage now or later.
Outcome: no surprises when QA starts testing.

The best part?

No coding required during planning.
KaneAI handles the technical translation.
You focus on strategy and priorities.
Tests export to any framework you use.
(You still validate before running anything)

Want to see how natural language testing works?
Check out KaneAI and try it for free for 7 days: https://lnkd.in/dPAHSNb3

The future isn't AI vs humans.
It's AI × humans.

Hassaan Raza & Quinn Favret just broke down
what most companies are missing about AI.

Here are 7 insights that change everything:

𝗛𝘂𝗺𝗮𝗻 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴
→ Machines no longer require humans to adapt
→ The interface changes when tech meets people where they are
→ Natural communication becomes the default
→ Access expands through alignment

𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗦𝘆𝘀𝘁𝗲𝗺
→ Words alone are insufficient
→ Meaning emerges from timing, tone, gesture, and silence
→ Signals only work in relationship
→ Fluidity defines connection

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗶𝘀 𝗛𝘂𝗺𝗮𝗻 × 𝗠𝗮𝗰𝗵𝗶𝗻𝗲
→ Purely human is insufficient
→ Purely machine is limiting
→ Hybrid systems unlock empathy with scale
→ The middle space creates leverage

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗶𝘀 𝘁𝗵𝗲 𝗛𝗮𝗿𝗱 𝗣𝗮𝗿𝘁
→ Perception captures data
→ Understanding creates meaning
→ Context determines the right response
→ Interpretation is differentiation

𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗕𝗲𝗰𝗼𝗺𝗲 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀
→ Static tools fade away
→ Software evolves with the user
→ Proactivity replaces navigation
→ Trust becomes the metric

𝗘𝗺𝗼𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 𝗘𝘅𝗽𝗮𝗻𝗱𝘀 𝗔𝗰𝗰𝗲𝘀𝘀
→ For many, the alternative is no support at all
→ Emotional intelligence unlocks reach
→ Alignment matters more than purity
→ Impact is measured by availability

𝗖𝗿𝗮𝗳𝘁 𝗢𝘂𝘁𝗹𝗮𝘀𝘁𝘀 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝘆
→ Speed without belief decays
→ Taste shows up in small decisions
→ Conviction compounds over time
→ Enduring systems are built with care

The shift isn't about AI replacing you.

It's about AI amplifying what makes you human.

P.S. Want to learn more about AI?

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The Agentic Enterprise Advantage

𝗣𝗲𝗿𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗱𝗼𝗲𝘀 𝘄𝗵𝗮𝘁 𝗳𝗶𝘃𝗲 𝘁𝗼𝗼𝗹𝘀 𝘂𝘀𝗲𝗱 𝘁𝗼 𝗱𝗼.

One system now handles complete project workflows.

✦ Research phase gathers information and analyzes data.
✦ Design phase creates plans, mockups, and architectures.
✦ Code phase writes and debugs implementation.
✦ Deploy phase ships and launches projects.
✦ Manage phase maintains and updates ongoing work.

It routes each task to the best model from 19 options.
Runs agents in parallel instead of sequential.
Maintains context across hundreds of active projects simultaneously.

1. You define the project.
2. Computer breaks it into sub-tasks.
3. Routes each to the optimal model.
4. Delivers complete output.

"Build a landing page for [product]" becomes a finished deployment.
"Research competitors and create analysis report" becomes a complete deliverable.

Max subscribers get access now.
10,000 monthly credits plus 20,000 bonus credits.

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OpenAI just introduced ads into ChatGPT and as Peter Swartz highlights, this is bigger than it sounds.

Free users and those on the new $8/month U.S. plan will now see clearly labeled sponsored content beneath responses. Pro and Enterprise tiers remain ad-free.

On the surface, this looks like incremental monetization.

Strategically, it’s a shift in incentives.

Reported projections suggest:

~$1B in ad revenue this year
Scaling toward $25B by 2029
~$200K minimum buy-in for early advertisers

OpenAI states ads won’t influence responses and that conversations remain private.

But this is a structural change.

The company that positioned itself as building AI for humanity is now also operating an advertising platform. At the same time, competitors are explicitly differentiating on remaining ad-free.

This creates a clear divide in the market:

• Ad-supported intelligence
• Subscription-first, privacy-positioned intelligence

For business leaders, this isn’t just a revenue story.

It’s an incentive design story.

The business model behind a system shapes its long-term behavior, whether directly or indirectly.

→ What incentives do you want embedded in the tools your organization depends on?

#OpenAI #ChatGPT #AIIndustry #DigitalAdvertising #BusinessStrategy

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