Email Marketing Specialist | Scaling Businesses One Email At The Time 📈

I don’t like to advertise this fact, but I’d do this job for free.

Starting my career in email marketing seemed like a mismatch at first, given my bachelor's degree in Ecology. But life has a funny way of leading us down unexpected paths, and I quickly realized I had a natural talent for writing copy. This talent then spilled over into strategy and automation, and before I knew it, I was helping brands not just communicate but truly connect with their audiences through email.

One of my key strengths is spotting the untapped potential in both brands and people. I bring those hidden strengths to the forefront, making sure each email resonates with the right audience at the right time.

Maybe it’s time I toot my own horn a bit. As I usually say, "Email marketing works—when done right, that is."

- Generated over $74,000 for a client in the gaming industry, with 75% of revenue coming from automated flows and the rest from targeted campaigns.

- Increased email revenue by 44.68% for a client, with a 159.41% increase in form submissions through a new pop-up strategy and better A/B testing.

- Drove $349,230.52 in revenue for a brand, with 89% of that revenue coming from email automation.

- Helped a fashion brand grow revenue by 34.7%, reaching $375,818 in just nine months. Email open rates hit 58.16%, and click rates improved to 10.65%. SMS campaigns alone generated $211,904 during this period.

- Boosted form submission rates by 216.53% for a client, directly increasing lead generation and revenue.

- Helped a business generate $19,755.90 from a single email campaign, contributing to total revenue of $92,980.85 in just four months.

- Took an e-commerce brand from the ground up, increasing its revenue from $24,847 to $40,131 through engaging email campaigns and improved email strategies.

Enough of the boring numbers for now. Let's close out this "about" section.

When I’m not busy “emailing,” you’ll probably find me experimenting with new recipes in the kitchen or listening to some dope music (or both).

Reach out if you want to talk about email marketing, e-commerce, or pool (the one you play with a cue stick, not the swimming kind).

Stefan Boskovic

Attain Institute

Attain Institute helps non-technical decision-makers in medium-sized businesses to improve their product management. 

We provide training and advisory services on product strategy, new product development, product operations, product management, and solo PM programs on nocode and genAI.

We convert your expert knowledge into in-house training programs and online courses. 

Maersk, Standard Bank Group, SAP, Viessmann, Klöckner, and others trusted our advisory, training, and community-building expertise. Over 400 PMs and 100 executives have benefited from our training. 

Best practices from Amazon, Google, and other tech companies are adapted to your industry and your specific organization into a custom pm program you can launch within 4 weeks. The programs include:

- Bootcamps with live work sessions in virtual cohorts 
- Self-service online course content by ex-AWS and ex-Google trainers
- Exercises customized to solve the specific challenges of individual participants
- Templates, Assets, NoCode, and genAI resources to solve the common pm challenges
- Everything you need to build an internal product community: events, challenges, content

Launch your custom product organization in 3 months:
- PM role definition, role profiles, leveling guides, job descriptions, incentives, performance metrics
- Internal PM community: templates (pm intranet, events, channels, news), governance, standards
- PM best practices: templates (PRD, BRD, Roadmaps, Methods), OKR and KPI, stakeholder management
- Product portfolio: governance, taxonomy, product strategy, product teams
- PM training: strategy, research, visioning, development, launch, growth, operations, retention, sunsetting, product lifecycles, roadmaps, product hacks, technical debt

Here is how we work together:
1. month - analysis: identify needs & gaps, benchmark, current state map
2. month - design: define program, customize content & tooling
3. month - launch: pilot cohorts, train the trainers

Transform in 3 months.

Chief Product Officer

Amazon Web Services (AWS)

Bring expertise on platform businesses, marketplaces and digital ecosystems to AWS customers. Principal Advisor to senior executives and product teams for key Customers in FSI and Manufacturing across EMEA. Helping to identify new business opportunities and incubate new products and services. Leading business conversations to drive innovation and improve customer’s capabilities. Helping to improve their business by using the full potential of AWS. Building solutions from the customer perspective. Advising top management on their digital transformation.

Principal Advisor

Hotpatch

Product Agency. Projects & Clients:
- Led squad at McKinsey to develop an internal suite of products to create presentations.
- Advised Datev Executive on creating an Executive Briefing Center.
- Created a platform for Google to host an international event.
- Led User Research for Leonardo, a children's TV producer.

Quit after catching Covid and taking a parental leave.

Hypoport AG

introduced design thinking and agile approaches into the company. 
fired for disagreeing with the strategy to create a holacratic organisation

Director People and Change

Mind-x AI

Cofounded startup on AI driven workshop experienced. Failed due to timing & tech capabilities.

Berlin School of Digital Business GmbH

consulting & coaching companies in digital transformation, focus on design thinking and agile, created AI training experiences for VW and Klöckner

Head of Innovation

GE Power Conversion

introducing new approaches to collaboration, social networking and design thinking to GE, used ML to visualise internal social network, built internal communication platform product

Innovation Lead

spent about 3 months in china training managers in design thinking. Managed teams in the design thinking wave, building products for SAP customers

Project Lead, Design Thinking

Alexander von Humboldt Institute for Internet and Society

co-created and co-founded the premier internet research institute in Germany with funding by Google. Worked with Google policy leads and Berlin universities to create a conference and research network including Harvard, Oxford, and Keio Universities.

Co-Founder, Program Manager

Ingosu

Productizing design thinking for corporate  customers. 
Created product concepts, prototypes and trainings for Haufe, Fraunhofer Society, Avato, Bertelsmann, and various SME.

Chief Product Officer, Co-Founder

Your business is eating up all your time, but you can't just keep hiring people. You're overwhelmed by technology and feel like you're not leveraging it properly.

That's where I come in.

Imagine developing and running your products with half the time you are putting in today. 

Imagine making informed decisions in days, not weeks. 

Imagine running 20+ experiments per month instead of 2 per year. 

If you are struggling to: 
- create digital products your customers love
- improve productivity in your business
- spending time working on the business instead of inside it

Let's talk and I'll show you how you can improve your workflows and processes.

Attain modern product operations with AI and automation. 

Past customers:
McKinsey, Amazon, Google, Facebook, SAP, Maersk, TUI, Standard Bank, DB, Haniel, Haufe, TUI, Mercedes, Volkswagen, Sanofi, Zurich, Deutsche Bank, +30 more. Ask me for the full list and 1-page case studies. 

----------

Customers I helped had the following challenges:

☒  Manual production of products & services, with no established systems 
☒  Products that don't perform and lack an effective product strategy
☒  Inefficient product development and delivery processes

Here is how I helped them:
☑︎  Build systems that create new products and services reliably
☑︎  Develop high-performing products with an effective product strategy
☑︎  Introduce best practices in product operations

ahmet acar

Last saturday, I held a 1-day AI workshop for the Freemasons in Nairobi.
The 3 things people were most interested in:

1) How can I make this work in my company?
2) How can I teach this to my kids? 
3) Can you help us with this?

This was part of a charity fundraiser (so, I did it pro bono). 
And I wasn't trying to fish for business.

Yet, out of 90 people, 10% followed up with me directly.
It probably would have been more if I actually had done some sales.
I don't know. 

In any case, there is strong interest across industries towards an AI transformation. But, I see major challenges everywhere. Here are the main challenges I see & what to do about them. 

Leaving aside that all of the below will need training, capability building, and technology investment...

A) genAI hype

I can show people the "boring AI" that actually does stuff. Or I can show people the "I just put you in a dress genAI" tools. Guess what draws more interest?

People asked me more about genAI platforms than about the other AI technologies I talked about. This is a problem. I think in discussing this topic, one has to overstress the fact that there are many issues with genAI (starting with the economics of it).

B) let's introduce it / quick wins

Hold your horses, hoss. There are lots of things that can be done in sales and marketing. Also some smaller automations in workflows. Great, but is that what you should be working on? What will move the needle? 

Working on the right part of the business is key. It's important to slow people down and make the right decisions here. Else you join the 95% failed pilots.

C) lets build / train our own xyz

While simple prompts seem to produce impressive results, making anything work consistently, securely, and at scale is another beast. Making something work on your own data and on your own systems is even more challenging.

Especially in sectors like manufacturing (but anywhere really), I consistently see a lack of data and process. To quote Sherlock Holmes: “Data! Data! I can't make bricks without clay.” 

D) tool use isn't adoption

Learning how to use an AI tool is as much adoption as learning how to use photoshop is figuring out photography. Mastering word doesn't make you an author. Becoming a powerpoint wiz doesn't make you a storyteller.

I get the shiny new tool syndrome - I'm guilty of it, too. But this goes back to "what outcomes do you want" before "can AI help with it".

So now I have a bunch of business owners who'd like some help in their AI transformation, but it still feels like a can of worms. 

My sense is that a lot of the new "AI services" companies out there are peddling the same type of transformation BS as the big consultancies do. 

I'd be interested in learning about those who do good work & also eat there own dogfood. If you know of any, please comment below.

Your AI Transformation Partner | AI Advisory | AI NED

When they say that LLM are a “dead end”, it’s in relation to AGI. 

Assuming AGI is possible (even with world models).

But that doesn’t mean LLM are useless. It can be very useful to crunch language in certain contexts. 

That’s why Meta is still pumping money into it. Their main platforms are based on language and its manipulation. They don’t really need world models for their business model, which is peddling attention and user data. 

That might be the bigger reason for him to leave. The company isn’t interested to support that research. They are happy making money with LLM, so he takes his ideas elsewhere.

Founder & CEO, Curiouser.AI | Berkeley Instructor | Building Values-Based, Human-Centered AI | LinkedIn Top Voice in AI

Yann LeCun just left Meta.
Let that sink in.

One of three people who invented modern deep learning. Turing Award winner. 

Chief AI Scientist at a company spending billions on AI.

Gone.

Not to a competitor. Not to retirement. 
To a startup building technology 
Meta apparently doesn't prioritize.

This matters more than most people realize

LeCun has spent 40 years being early and right. 

In the 1980s, he couldn't find a PhD adviser because machine learning was "a dead end." 

Three decades later, that dead end became the foundation of modern AI.

Now he's saying large language models, the technology behind ChatGPT, Gemini, and Meta's Llama, are another dead end.

"Nobody in their right mind would use LLMs of the type that we have today" within 3-5 years, he said at MIT last month.

His alternative: world models. Systems that learn from visual data and build causal understanding, like a child learning physics by dropping things thousands of times.

Current LLMs? 
They predict the next word. 
They don't understand gravity. 
They don't know that objects exist when you're not looking at them.

LeCun compares their intelligence to a cat's. He thinks the cat is smarter.

Here's what troubles me:

Meta is spending countless billions on technology that its own Chief AI Scientist publicly called a dead end. 

Either LeCun is wrong (possible, but his track record suggests otherwise), or Meta is chasing market narratives over technical reality.

His departure suggests he's betting on the latter.

The implications for current AI investments are significant. If LeCun's 3-5 year timeline holds, companies built entirely on LLM scaling may face architectural obsolescence.

That's not a guarantee. Even geniuses miss sometimes.

But LeCun has never been wrong about something this big before
********************************************************************************
The trick with technology is to avoid spreading darkness at the speed of light.
Stephen Klein
Founder & CEO, Curiouser.AI
Instructor, UC Berkeley Haas School of Business
WeFunder (link in comments)

Sources:

Wall Street Journal - LeCun departure and startup plans (November 2024)
MIT Symposium - LeCun remarks on LLM limitations (October 2024)
Meta FAIR Research Publications - World model architectures
Turing Award Archives - LeCun, Bengio, Hinton recognition (2018)

95% of AI pilots fail? That's a lie.
A white lie, maybe. Here's some context to this.

If you read the actual studies and look deeper into the context of reports published by big consultancies, you'll notice 3 things:

1) the pilots are all done at large enterprises / corporations
2) the pilots were all large projects and transformations
3) the focus was on large outcomes

I worked with AWS, McKinsey, Google, SAP, GE... and on various large scale customer projects. Here's what I learned about those:

They mostly don't work (well).

You'll have a cloud transformation program with 5 tracks, beautiful project swimlanes, RAG dashboards, documentation everywhere, a program lead with multiple project leads, and a dozen teams on both sides.

The "data track" typically is able to clean some sh*t up.
The "migration track" is able to move some workloads into the cloud.
The others? Meh. Some fizzle. Others crash n burn.

But people report A to G and R to A because that's the game.

If McK, BCG, Gartner & Co looked away from enterprise into small and midsized companies, freelancers, and agencies, here's what they would find:

- invoice automations that cut DSO by half
- proposal automations that cut 90% time and effort 
- 10x of internal tooling builds, removing IT bottlenecks

I've personally helped people with this stuff in the last months.
No big brand names. Small teams, momNpop shops, family businesses.
But the studies and reports aren't looking at those (prove me wrong please).

Even though these companies represent 90% of the global economy.

I personally don't want to work with huge customers anymore, because most of the energy and effort there just goes down the drain. You barely make a blip.

Which explains the 95%

other industries that do this (eg automotive) are regulated, ai isnt. while the tech and concepts are interesting (and in a lot of ways useful), the impact should be considered as well. but it doesnt even come up in discussion

Ecologies of Technology - research, writing, engagement, workshops, project and proposal development

Here’s an investigation I did for Truthdig.... The great AI bubble has made Nvidia, by far the leading producer of AI GPU chips, the world’s first 5 trillion dollar company, and its CEO and AI Accelerator-in–Chief Jensen Huang, the world’s 8th richest man. Earlier this year Nvidia very quietly released its sustainability report for 2024, and nobody noticed. That suited Nvidia and Jensen Huang perfectly, because the report revealed that last year Nvidia’s carbon emissions rose from 3.8 million tons to 7.15 million tonnes; a rise of 87 per cent in one year alone.

That is because alongside the massive build out of AI data centers, we are now seeing by far the most rapid expansion of the semi-conductor industry in its history – and that is to make the GPU chips to fill those AI data centres.

AI looks like a digital platform business, but actually, it’s entirely different; AI is a massively heavy industry – and it has all the energy, resource, social and ecological impacts that a heavy industry entails.

Like data centers, advanced semi-conductor factories - called fabs - require electricity power at gigawatt scale, and vast amounts of water. But semi-conductor fabs also produce thousands of tonnes of contaminated water and waste, including huge amounts of PFAS “forever” chemicals; they emit gasses 35,000 times more warming than co2, and they subject their workers to life-threatening contamination. This is also an industry with a long history of union busting, deep political influence – as well as ecological destruction.

In this investigation I followed the AI supply chain to campaigners and communities in South Korea, where a 10 gigawatt semi-conductor “mega-cluster” is being built, that will require a 7th of South Korea’s electricity and will consume half the water of Seoul. And also to Taiwan, where all the most advanced microchips are currently made – and where the semi-conductor industry overshadows the entire economy. I then spoke to campaigners in the US, where semi-conductor fabs are returning at scale as a result of the CHIPS act - and many communities are already seeing the toxic impact.

I talked to a lot of amazing people for this article – in particular Judith Barish of CHIPS Communities United, who is co-ordinating with workers and communities around the new US semi-conductor fabs, and Sara Marcucci at AI + Planetary Justice Alliance. 

Sam Altman recently shared that OpenAI plans to have 250GW of data centres online by 2033. That is around the entire electricity capacity of India –supplying 19 per cent of humanity.

With today’s cutting edge GPUs, a leading fab will produce roughly enough chips to power 13.25 GW of data centres. That means alongside the build out data centers, 10 massive semi-conductor fabs will also need to be built to supply OpenAI alone.

So this presents us all with the question: what is this industry offering us that is worth this immense ecological cost?

Link in the comments....

Gemini 3.0 just came out. Here are a few things I tried & a treat for you.
Some first impressions...

1) Created a website

Wanted to compare to loveable, manus, codex etc. basically to see its agentic coding capabilities and how it would handle requests. 

It created a simple, but well structured and well designed site very quickly. Pretty comparable to ai coding tools, but without the preview or any added functionalities.

2) Add animations to the site

It turned the logo into an svg and animated it, then provided a revised version of the html.

3) use the tools to create applications

I activated canvas for this one, but it was able to add a booking system to the simple website it created.

4) create a brand video 

It used the images on the website, turned one into a brand video, then embedded the video into the site. 

5) other experiments

Voxel art, 3d models, small games - they've created pretty broad capabilities. the video attached is from a simple game built with gemini 3.

6) turn this process into a gem

Combining the steps above, I created a gem that can automate the steps 1 - 4 and create interactive websites quickly. I also noticed that there aren't many guides out there on how to create gemini gems.

If you have never used gems:

These are custom versions of Gemini that allow you to save and reuse specific instructions to automate repetitive tasks. You can create little automations and agents to save time. But it can be tricky to use. 

So, I created a small course focused on Gemini Gems: 

- New Capabilities in Gemini 3
- Using Gemini 3 through Google AI studio
- Creating prompts and Context for Gemini 3
- Building Gemini Gems 
- Testing & Improving Gems
- Combining Gemini and Veo
- Using Gemini 3 in Google Docs, Sheets & Gmail

Mostly to document what I'm doing for myself, but I'll share this with my customers in the future. Right now, you can get the rough & dirty version here for free. I'd just appreciate some feedback on it.

I no longer have a pro account, so to share it:

1) connect with me so I can send you a message
2) comment "course" so I get why you're connecting
 
I'll send you a google drive link with the content. 
Feel free to remix and share it.
And again: send me feedback!

Like & Share this post with others, if you feel like they could need this, too.

Don't believe the BS peddled with consultancies that don't have a technical background and / or haven't actually worked in a real business before.

Here's what I see:
- no data / consistency
- no metrics or rigor in measurement
- no systems, no trainings, little investment
- no "been there, done that" leaders to move it forward
- few (if any) experiments to push efforts from pilot to production
- little true leadership in highlighting the risks, the challenges, the pain

I read every major publication from the big consultancies.

Sorry, but it's mostly a big pile of 💩 

Want to know the secret to transformation? 
Here, I'll save you 250K in consulting fees.

1) pick one team, one small challenge
2) solve it. share with others.
3) repeat.

Fix one thing every week (for good), and by the end of the year, you'll see the transformation in place. Instead, it's huge complex frameworks with lots of resources and giant projects with multiple workstreams.

Like, sure you can have the bit ambition and map it out. 
Think big, of course.
But start small.

25+ years in AI | 38k+ Followers | MBA (AI) - BSc (Stats) - BCom (Finance) | Follow for real-world insights on AI, data & automation

Bain just released their new “Guide to AI Transformation”

It is beautifully designed, wonderfully optimistic, and completely allergic to reality.

Reading it feels like watching someone explain how to build a spacecraft using only Post-it notes, duct tape and confidence.

It makes AI transformation sound like a strategy workshop with pastries, rather than the organisational identity crisis it actually is.

I thought I could waffle, but combining “vectors of disruption”, “strategic clarity”, “value pools”, and “outside-in perspectives” in one sentence deserves an award.

AI transformation fails for one embarrassingly simple reason:
Most companies do not understand how they actually work.
Not how they say they work.
Not how the org chart pretends they work.
How they actually work.

This is the part Bain glosses over with the enthusiasm of a realtor describing a flat with no windows as “cosy” and “intimate”.

Their report answers the questions CEOs wish AI would solve, not the ones that determine whether AI works at all.

And I get why.
 - Big bets justify big programmes
 - “Organisational activation” scales better than data architecture
 - Framing AI as “strategy” avoids the operational entropy
 - “Pick your battles” beats admitting most battles are lost in the trenches
 - Case studies are flawless because the real ones are politically radioactive chaos

It reads less like a guide to AI transformation and more like a guide to selling AI transformation.

AI has pushed consulting firms into a strange dual role: interpreters of the future and vendors of the present. The result is frameworks that sound transformative while avoiding the operational truth.

The bit Bain will not say out loud:
AI transformation is not blocked by a lack of vision.
It is blocked by a lack of organisational self-awareness.

Most companies:
 - Do not know how their workflows run
 - Do not trust their own data
 - Cannot explain their decision loops
 - Have incentives built to resist automation
 - Rely on long-tenured staff holding the place together with memory and vibes

AI does not fix this.
AI exposes it.

It drags dysfunction into the light like a forensic lamp in a dodgy hotel room (good luck getting that image out of your head).
Confusion.
Incoherence.
Process entropy.
Hero culture.
Zombie workflows everyone insists are “temporary” but are in year nine.

Ironically, the most valuable lines in Bain’s report are accidental. They hint that AI is really about speed, clarity, and organisational honesty. On that, I agree.

AI is not a technology revolution.
AI is an organisational honesty revolution.

The companies that success with AI will not be those spending the most on models. They will be the ones finally willing to confront how they operate… then rebuild with clarity, trust, and ruthless simplicity.

Far less glamorous than a 40-page slide deck.
Far more effective.

Afraid of AI? 
This should scare you:

All of the US AI companies are massively overvalued. 

The large tech companies are all losing money on AI. 

At the same time, there are massive investments with no path to profitability in sight. 

If (when?) this leads to a crash, it’s going to impact:

- all AI startups building on these platforms

- all companies in the same ecosystem, from services to consultancies building on it

- all companies that serve them, think energy, water, cleaning, etc. 

- all saas companies that have these as customers

- all companies that serve the employees of the companies that will go bust

This will create ripple effects taking down the entire global economy. 

This is much much worse than Covid, housing crisis, or .com crash combined. 

People will lose their livelihood, their savings, investments, houses…

When something like this happens, it typically leads to global conflict. 

Given the fact that geopolitics is already heated up, this could be the spark that blows up the world. 

You should fear this more than some imaginary AI super intelligence taking over. 

So, next time some idiot talks about AGI on stage, throw something at them.

Imagine having a capable colleague (ah, who doesn’t dream of that?)… someone who researches market trends while you sleep, drafts reports based on you

Open article: AI Agents: Your Digital Workforce by ahmet acar • 5 min read

Everyone is using AI tools. 
But this will give you an edge:

1) write a note by hand

Not on a device. On paper. With a real fountain pen and real ink. Try your best at the calligraphy. Rip it up and try again. Put effort into it. 

Create beauty with your own hands. 

2) Critically think about something 

Go to the forest. Take a walk and ruminate on one thing. Just that one thought all day long. Look at it from different angles. Don’t read. Don’t talk. 

Sit with your thoughts. 

3) Give a speech about something

Forget PowerPoint. Forget notes and preparation. Put yourself in a public square. Get on to a box. Start talking. Try to gather a crowd. 

Share yourself. 

When everyone is glued to their screens, those who don’t need any will have the edge.

25 years ago, NASDAQ fell 80% in the .com crash. It took 15 years to recover.
Today, 25% of S&P 500 is basically AI expectations. 95% AI use at zero ROI.

You don't need to be a financial genius to see what can happen.

"Markets can remain irrational longer than you can remain solvent." - JMK

Bureaucracy just seems to pile up everywhere. In companies, nations, even small communities. Radicals offer a simple solution. One decision maker.

But that doesn't fix democracy, just gets rid of the conversation.
Communication is fine. It's the entropy that's the problem. 
Here's how it starts in my experience:

- you start with good intentions
- adding steps feels safer than removing them
- over time, these accumulate into layers that nobody questions

Most bureaucracy isn't necessary. It's just fear dressed up as process.
Cut it: 

Step 1: Question every requirement

Attach a name to every process step.
If someone says "compliance requires this," ask who and why.
The real test: what would actually happen if we stopped doing this?

Step 2: Trace it to the source

Walk backward from each procedure to its original justification.
Ask: is this still a valid reason?  
If not, you've found internal policy masquerading as necessity.

Step 3: Delete aggressively

Stop doing it for two weeks.
If nothing breaks, you've found dead weight.
Most things survive because nobody asks if they should exist. 
Remove first, improve second.

Step 4: Attack approval chains

Approvals add delay and diffuse responsibility.
The person doing the work isn't accountable. They're just executing. 
The approver isn't accountable either. They're just checking boxes.

Nobody owns outcomes.
Replace approvals with authority. 
Give people decision-making power.

Step 5: Make bureaucracy visible

Calculate hours spent on a process. 
Show what that represents in salary costs.
Make people realize a report nobody reads costs $5000 in labor.

Step 6: Create forcing functions

Left alone, bureaucracy regenerates and increases. It's entropy.
You need structural counterpressure:

1) Sunset clauses. A process expires after 2 years unless explicitly renewed.
2) Headcount limits: to add a process, you must identify what to stop doing.
3) Accountability: a process needs a named owner to justify its existence. 

No anonymous "company policy."

The pattern I've seen work:

Start with one painful /process everyone agrees on. 
Question every step. Delete what you can. Simplify what remains.
Document the time saved. Use this as proof that reduction doesn't mean chaos.

Then do the next one.

No huge transformation games, initiatives etc. Fix one thing at a time.

Repeat after me:
Bureaucracy is NOT the same as rigor.

Simple answers to questions on AI:

1) What is AI?

Don't trust anyone who'll give you a two sentence answer here. Go read up on wikipedia. Read the history, the various schools of thought, the fields within the space. It's fascinating. But not "the future", not "revolutionary". Things were happening for decades. 

2) Will AI kill us all?

The likelihood of your toaster killing you is higher than the likelihood of AI killing you. Unless you're talking targeted drone strike through a CV guided system... no.

3) What problems does AI solve?

Finding patterns, matching patterns, creating patterns... in data. If your data sucks, your AI sucks. That's why we're just seeing "the revolution" now. 

4) Why is AI happening now?

Internet + mobile + cloud compute + high performance graphics chips + too many people bored during covid = tons of data = genAI. Took the past 10 years to get here.

5) What role do people have in an AI future?

There's no "AI future". There might be a "climate resilient future" if we get it right. As for AI, it's another tool. There's no AGI, no superintelligence. It's all BS. We don't know enough about "reasoning" to build it, let alone model it.

6) Is AI a hype?

Well, ai companies spend $3 for every $1 they make and are celebrated for it. If that isn't hype, i'll open a bar and serve $300 bottles for $30. Looking for investors.

That said, there is useful tech. You use it every day. Route-finding in google maps. Facial recognition in iphoto. It's everywhere. The cool stuff is the boring stuff.

7) Do you have an AI agent?

Sure, check the image.

My $20 feature phone fell from the roof and survived.
My $700 iPhone dropped from the table and broke apart.
WTF?
 
The iphone was in a "robust" case and it has the ceramic shield "scratch resistance", but none of that did squat when it fell from 1m onto a tile floor.

Meanwhile, the cheapo SQ7700 "military" phone (with 10KmAh battery btw) fell from the 2nd floor window, bounced from the roof a few times, then fell on the stone driveway. Total height... I don't know? 

It's obviously still possible to build phones that last forever and can standby for 10 days for $20 (oh, and it takes 4 SIMs). 

We keep talking about sustainability, climate change, product quality, and yet... 
 
Build a brand, make it glossy and add a zero to your price.

Same for software, btw. There are tons of good, small, indie tools out there, but everybody gets expensive subscriptions instead for features they don't need.

Do you know what % of features the majority of people use in MS Word?

It's 15%

So, on the one hand, we have products that are overpriced and don't deliver on the most basic thing. On the other hand, we have products that are overbloated and need a degree to figure out.
 
It's damn hard to build a good product. 
I get that.

But I said it before, and I'll say it again:
Products today are designed to decay, break, and be replaced quickly. 
 
We live in a world with sh** products that simply turn natural resources to landfills. And sh** services that are supposed to solve problems and save time, but do the opposite instead.
 
At the same time, we pay outrageous amounts to these companies. 
 
Well, here's a cheer to the small, under-recognized companies that build crazy rugged $20 phones.

People think of critique as something negative. When it stops is when you should worry. 

If someone cares enough to provide their perspective and all you see is negative feedback, you already decided to oppose them instead of working as a team. 

The point is:

If you have someone who actually knows what they are doing in the thing they talk about, you better take their lead. 

Just because you hand over the lead on occasion doesn’t mean you loose power or are no longer heard. 

It just means you know when to follow. 

Learning how to follow is the first and most important thing to do.

In Tango, you can’t lead if you don’t know how to follow. 

But it’s not just a tango thing.

Ooooph. 

So sad when this happens but - how many managers know how to “play the ball not the man”? 
How can we lift conversations to the level of ideas without insecurities getting in the way? 

If managers are incentivised to be the ones with the answers; does their openness to learning atrophy?

I'm probably unemployable at this point. 
But I've spent some time researching the top remote job sites. Hope this helps:

Remote100K - High-paying $100k+ remote jobs at top companies
We Work Remotely - One of the largest with global opportunities
Arc - Quality remote developer positions with vetted companies
Wellfound - Startup jobs with equity and remote options
Contra - High-quality creative and marketing freelance gigs
SEOjobs - SEO and content marketing remote positions
Working Nomads - Perfect for location-independent professionals
RemoteOK - Popular for both tech and non-tech roles
NoDesk - Remote-first jobs for digital nomads
SkipTheDrive - US-focused remote and flexible positions
HiringCafe - Curated remote opportunities across industries
Built In - Tech jobs with strong remote filter options
DailyRemote - Daily curated remote job postings
Jobspresso - Hand-picked remote jobs across multiple industries
JustRemote - Simple, clean remote-only job board

Just look for the links on google.
Good luck.

Basically this is why to exit any IT stock next year. Unfortunately, it'll also hit lots of other industries. If you've built on a platform that will no longer be around...

Wealth Manager ( I am a very rare Investment Manager and Financial Adviser). All views are my own and may not be shared by my employer . Fellow of CISI (Chartered Institution of Securities & Investment)

AI and Silicon Valley are hiding A $200 Billion Bomb In Plain Sight and Michael Burry from the Big Short Film fame, has just done what he did back in 2008. 

An aggressive big short position in bubble stocks, then he shuts his fund to stop investors liquidating and stopping him from massive profits because they cannot stomach short term pain.

His reasoning

Every 12 months, Nvidia releases chips that make last year’s hardware obsolete.

In their accounts the companies buying them pretend they last 6 years and depreciate them accordingly.

That lie is about to cost the world at least an estimated $200 billion.

Here’s What They’re Not Telling You:

Microsoft, Google, Amazon, and Meta are depreciating AI servers over 72 months while Jensen Huang (ceo oh nividia) openly mocks people still using chips from 18 months ago. 

When Amazon tested cutting just one year off these assumptions in February, it triggered a $920 million loss in a single quarter.

One year. One company. Nearly $1 billion gone.

Now do the math: $200 billion spent annually on AI infrastructure that becomes economically worthless in 24 months but gets written off over 72 months. That creates $150 to $200 billion in fake profits that will reverse as violently as they appeared.

Palantir trades at 449 times earnings. NVIDIA at 54 times. These valuations exist because companies are legally delaying the recognition of obsolescence. 

The man who predicted the 2008 housing collapse just bet that this accounting fiction ends in catastrophe, then immediately deregistered his fund to avoid watching the fallout.

What Happens Next:

When Blackwell chips appear on secondary markets at 50 cents per dollar in late 2026, the fiction ends. 
Depreciation gets slashed. 
Earnings get restated. 
Debt covenants get breached. 
The $10 trillion AI market capitalization represents not future cash flow but present accounting fraud.

This is not a correction. This is Enron mathematics applied to the most hyped sector in human history.

The 24-month clock started when Hopper launched.

We’re already halfway through.

Very few are ready for what comes next.

What skills do kids need in an AI future? Roughly 20% of people in my workshop asked that question. The answer might be counter-intuitive. First off, 

Open article: What skills kids need in an AI future by ahmet acar • 5 min read

5 Risks of Betting Your Company on Western AI
If you're building on OpenAI, Gemini, or Claude, here's what you should know.

A quick factoid first: 
AI doesn't actually require massive data centers. 
While Western tech giants went for scale, researchers worldwide continue developing small models trainable on mobile devices. 

But US companies chose the "build data centers, burn energy" path instead.

Risk 1: The Scale Approach Is Resource-Capped

Even if scaling works (it currently doesn't), it demands more water and energy than the US or Europe can provide. Both regions underinvested in infrastructure for 20 years. China didn't—energy there is 40% cheaper. You can't power this gap with fossil fuels without destroying the climate.

Risk 2: US Tech Is US Geopolitics

If America decides to pressure your region economically, its tech companies become tools of that pressure. This is colonization through technology. Most countries already run on US or Chinese tech. There's no meaningful European, African, or South American alternative.

Risk 3: The Economics Are Unstable

Chinese AI companies have better unit economics—cheaper energy, stronger infrastructure, and chip restrictions forced them to innovate. If you're building on OpenAI, consider whether it survives when the economics collapse.

Risk 4: Biased Data Sets

Most global information comes from a US perspective. When tech companies build AI data centers in EMEA, they're extracting resources, not integrating regional perspectives. Regional efforts exist but remain marginal.

Risk 5: AI Is Becoming Chinese

Over 50% of AI researchers are now from China. More publications emerge from China than anywhere else. Combine that with manufacturing dominance, robotics capability, energy infrastructure, and long-term state support—the trajectory is clear.

Btw, I'm not advocating that you should switch to Chinese models instead.

But you should know what dependency you are getting yourself into.
And you should know that your politicians are probably full of BS.
Especially if they're letting one or the other side "set up shop"
Instead of investing into STEM education and infrastructure.

If you don't own the tech, whoever owns it, owns you.

AI Agent roadmap

1️⃣ 𝗣𝘆𝘁𝗵𝗼𝗻

Learn debugging, logging, testing, and deployment fundamentals

✅ CS50P – Harvard's Intro to Python

🔗 https://lnkd.in/e8wpyEh8

✅ 100 Days of Code: Python (Udemy)

🔗 https://lnkd.in/eBsh5EkJ

✅ The Missing Semester – Shell, Git, Linux basics

🔗 https://lnkd.in/eAgP-Cs9

..

2️⃣ 𝗡𝗲𝘂𝗿𝗮𝗹 𝗡𝗲𝘁𝘄𝗼𝗿𝗸s

Understand backprop and optimization


✅ Neural Networks: Zero to Hero – Karpathy

🔗 https://lnkd.in/eizC6jid

✅ Deep Learning Specialization – http://DeepLearning.AI

🔗 https://lnkd.in/eFhfGCH9


3️⃣ 𝗟𝗟𝗠𝘀 & 𝗡𝗟𝗣

Embeddings, tokenization, transformers

✅ Hugging Face Course

🔗 https://lnkd.in/e2iaprtY

✅ Hands-On LLMs – Alammar & Grootendorst

🔗 https://lnkd.in/er_yFyZh

✅ LangChain + LLM Projects

🔗 https://lnkd.in/eUygbC_h

4️⃣ 𝗥𝗔𝗚 + 𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀

Integrate external tools, APIs, memory

✅ Introduction to RAG (Coursera)

🔗 https://lnkd.in/envujVJz

✅ AI Agents with RAG & LangChain (IBM)

🔗 https://lnkd.in/eVp4S6fq

✅ LangChain for LLM App Development (http://DeepLearning.AI)

🔗 https://lnkd.in/eXq3pG7q

✅ LangGraph for Production Workflows

🔗 https://lnkd.in/ecfM9CQ6


5️⃣ 𝗦𝗵𝗶𝗽 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱

Production tools: FastAPI, Docker, vector databases

✅ Production RAG Templates & Examples

🔗 https://lnkd.in/e6GDFx2d

..

6️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟𝗢𝗽𝘀 & 𝗦𝘆𝘀𝘁𝗲𝗺 𝗗𝗲𝘀𝗶𝗴𝗻

→ CI/CD, monitoring, scaling, failure handling

→ The difference between demos and production systems

✅ Designing ML Systems (Chip Huyen)

🔗 https://lnkd.in/epMDAwc3

✅ Made With ML – End-to-end MLOps

🔗 https://madewithml.com/

✅ System Design for ML Engineers

🔗 https://lnkd.in/eyqRNBK3

Learn engineering fundamentals.

When you're building something, your job is to turn uncertainty into certainty.
That's why most of the AI posts here are wrong.

Certain Outcomes
- creating features
- admin tasks
- internal comms
- email management
- data entry

these can be done using ai tools or delegating them

Uncertain Outcomes
- customer discovery
- positioning & messaging
- testing marketing channels
- finding product market fit

these should be done by you

taplio

The Battle

Stefan Boskovic

ahmet acar

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taplio

The Battle

Stefan Boskovic

ahmet acar

Followers

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Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}ahmet acar's LinkedIn Performance

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