A research into top leaders born and educated in Poland with focus on tech and Forbes 500 enterprises. It's all about Polish influence in tech and global leadership.

Jacek Olczak is a Polish economist and business executive who became CEO of Philip Morris International (PMI) in 2021, becoming the first Pole to lead the world’s largest publicly traded tobacco company. He was born in Łódź, Poland, studied economics at the University of Łódź, and began his career at PMI in 1993 in finance and general management roles before progressing through CFO and COO positions; he now lives near Lausanne, Switzerland.

"In just 10 years, we could be saying to people: ‘Remember when people smoked?’"

Wojciech Zaremba is a Polish computer scientist and co‑founder of OpenAI, known for leading work on GPT‑class models and earlier robotics projects such as a Rubik’s‑cube‑solving robotic arm. Born in Kluczbork, Poland, he studied mathematics and computer science at the University of Warsaw and École Polytechnique before completing a PhD at New York University; he now resides in the United States and has been a core figure behind systems like Codex and GitHub Copilot.

I thought that... if you have a sufficiently complicated mathematical system, it is possible to point the mathematical system back on itself. (Discussing consciousness and Gödel's theorem)

Marcin Żukowski is a Polish computer scientist and entrepreneur who co‑founded Snowflake and was a long‑time leader in its engineering organization, building on his earlier work on high‑performance analytical databases. He completed undergraduate studies in computer science at the University of Warsaw, then an MSc at the University of Warsaw and Vrije Universiteit Amsterdam, followed by a PhD at the University of Amsterdam, with his research on column‑oriented processing and systems like Vectorwise laying groundwork that later influenced Snowflake’s architecture.

Rafał Modrzewski is a Polish engineer and entrepreneur, co‑founder and CEO of ICEYE, a Finnish company that built the world’s first successfully miniaturized commercial synthetic aperture radar (SAR) satellites and now operates a leading SAR constellation. Born in Poland, he studied at the Warsaw University of Technology and later Aalto University in Finland, moved to the Helsinki region, and has led ICEYE’s growth from an early research project to a major provider of radar‑based Earth‑observation data.

The huge advantage of the ICEYE space vehicle is its ability to clearly see a cluster of even carefully camouflaged enemy mechanized units. (Ukrainian Defense Ministry describing ICEYE capability)

Mati Staniszewski is a Polish‑born mathematician and entrepreneur, co‑founder and CEO of ElevenLabs, a fast‑growing audio‑AI company focused on highly realistic text‑to‑speech and voice‑cloning technology. He grew up in a town just outside Warsaw (Zalesie is described as a suburb of the capital), later studied mathematics at Imperial College London, and previously worked in roles at Opera Software, BlackRock, and Palantir before launching ElevenLabs in 2022 and basing himself in the UK

The true solution is digitally watermarking synthetic voices to enable humans to discern real from fake. (On AI safety)

ElevenLabs is one of the fastest-growing software companies in history.

Piotr Dąbkowski is a Polish computer scientist and machine‑learning engineer, co‑founder and CTO of ElevenLabs, where he leads development of the company’s core voice‑synthesis technology. Originally from Poland and educated in the UK at the University of Cambridge (with later study or research affiliations also mentioned with Oxford in some profiles), he previously worked as a software engineer and ML specialist at Google and other companies before founding ElevenLabs in 2022 and is now based between London and the US tech ecosystem.

The list is non-exhaustive. Additionally to that I have more ideas on the same topic e.g. companies built in Poland, then expanded, tech leaders educated in Poland, american tech stars with Polish roots. The list can go on - so many interesting discoveries to make and to publish.

Hope you liked this one. Feel free to write me your ideas and suggestions to gostev@pm.me. Thanks for reading.

The modern workplace is in crisis. Despite rising productivity, employee engagement remains stubbornly low. As leaders, how many of us have felt that disconnect between what we produce and who we are? I remember leading a team through an 8 month delivery of a new product (online dating website, #47 in U.S. by amount of traffic) that looked successful on paper but left everyone feeling oddly empty. We delivered on time, but the spark in people's eyes dimmed.

As managers and executives, we focus on efficiency and performance while neglecting a fundamental question: what is work, truly? To answer this, we might look beyond management theory to ancient Greek philosophy, which offers surprising clarity on our modern problem.

The Greeks distinguished between two fundamental concepts: ponos (πόνος) and ergon (ἔργον). Ponos represented labor done from necessity – painful, toilsome exertion performed merely for sustenance or economic exchange. It wasn't expected to fulfill the human spirit. In contrast, ergon referred to one's proper function or purpose – work aligned with excellence, virtue, and human flourishing (eudaimonia).

This distinction resonates strikingly with today's divide between "just a job" and "meaningful work." According to Gallup, only 23% of employees feel engaged at work. Behind these numbers are real human beings searching for something more elemental - the satisfaction that comes when Monday morning feels less like ponos and more like ergon.

When I first encountered these Greek distinctions as a young manager (around 2008), they transformed how I approached my managerial practice. Instead of asking only what people could produce, I began asking what work would allow them to excel according to their nature.

But the Greek framework extends further, offering a rich vocabulary for understanding human activity:

Techne (τέχνη) represented skilled craft requiring specialized knowledge. Unlike routine labor, techne merged practical ability with conceptual understanding. The craftsperson knew not just what to do but why. Modern knowledge workers exercise techne, though often in fragmented ways that limit satisfaction. At companies like Patagonia, we see techne embodied in their approach to product design, where technical skill is inseparable from understanding environmental principles and customer needs.

Poiesis (ποίησις) meant creative production – bringing something new into existence. This generative activity goes beyond technical execution to creation itself. Think of poiesis as the difference between following a predefined process and creating something genuinely new. It's what we see when a team designs a breakthrough product or when a manager develops an innovative solution to a persistent problem.

Praxis (πρᾶξις) referred to action valuable in itself rather than for its products. Ethical conduct and civic participation exemplified praxis. Praxis manifests when a leader makes a difficult ethical decision not because it maximizes shareholder value, but because it's the right thing to do. It's leadership as an expression of values rather than just a means to outcomes.

Perhaps most provocative was schole (σχολή) – leisure not as idleness but as freedom for higher-order thinking and development. Our word "school" derives from schole, suggesting that true learning requires space beyond immediate productivity demands. Google's famous 20% time policy acknowledged what the Greeks understood millennia ago - that innovation requires space away from immediate demands.

What might these ancient concepts offer modern organizations?

First, they invite us to examine how work is conceptualized. When we reduce professional activity to mere ponos – labor exchanged for compensation – we shouldn't be surprised at disengagement. Employees instinctively seek ergon – work aligned with purpose and excellence.

Consider the case of Microsoft's revival under Satya Nadella, who shifted from a metrics-obsessed culture to one centered on purpose and growth mindset. Engineers who once viewed their work as mere coding tasks (ponos) began seeing themselves as creators solving meaningful human problems (ergon). Microsoft's market value has more than quintupled since this transformation began.

Second, this framework reveals blind spots in management practice. We've optimized for efficiency while neglecting the conditions that enable meaningful work. Research consistently shows that autonomy, mastery, and purpose drive intrinsic motivation – concepts remarkably aligned with Greek thinking.

The Greek concept of eudaimonia (human flourishing) wasn't just about feeling happy at work. It meant functioning excellently according to one's nature - something we might recognize today in the deep satisfaction that comes from applying our best talents to meaningful challenges.

Consider how organizations might reimagine work by deliberately incorporating all dimensions of human activity:

• Moving beyond ponos by connecting tasks to larger purpose
• Emphasizing ergon by aligning roles with individual strengths and excellence
• Fostering techne through continuous skill development with conceptual understanding
• Creating space for poiesis through creative problem-solving opportunities
• Valuing praxis through ethical leadership and community-building
• Protecting schole with time for reflection and learning

Notably, this approach doesn't require abandoning productivity. Aristotle and his contemporaries weren't advocating idleness but rather arguing that truly excellent work emerges when human activity aligns with deeper purpose.

These aren't just philosophical abstractions. They represent the quiet questions many of us ask ourselves late at night: Does my work matter? Am I using my talents fully? Would I do this even if I didn't need the paycheck?

For managers and executives, this suggests reframing how we define and evaluate work. Performance metrics remain important, but they tell only part of the story. Equally critical is whether work enables human flourishing – individually and collectively.

For researchers, these concepts offer fresh perspectives on persistent organizational challenges. How might worker satisfaction, innovation, and retention improve if we designed for the full spectrum of human work rather than just its economic dimension?

The ancient Greeks recognized something we've largely forgotten: work isn't merely what we do to make a living. At its best, it's how we express our humanity, contribute to something larger than ourselves, and fulfill our unique potential.

Our contemporary crisis of work isn't fundamentally about technology, global competition, or generational differences. It's about the eternal human search for meaning – something the Greeks understood thousands of years ago, and something we would do well to remember.

Further Reading

• Arendt, H. (1958). The Human Condition. University of Chicago Press.
• Aristotle. Nicomachean Ethics. (You can try different translations)
• Csikszentmihalyi, M. (1990). Flow: The Psychology of Optimal Experience. Harper & Row.
• Pieper, J. (1948). Leisure: The Basis of Culture. Ignatius Press.
• Pink, D. (2009). Drive: The Surprising Truth About What Motivates Us. Riverhead Books.
• Svendsen, L. (2008). Work. Routledge.
• Schwartz, B. (2015). Why We Work. TED Books.

Roughly three months ago, a friend of mine looked at my AI-generated article draft and asked a question that changed everything: "It's fine, but where are you in this?"

That's when I realized - it's the AI efficiency trap – you save time but sacrifice the very thing readers want: my perspective.

The hidden cost of letting AI drive

When you let an LLM take the wheel, you're trading your unique voice for convenience.

AI writing tools produce content that's technically correct but creatively bankrupt.

They mimic the average of everything they've seen – nothing more, nothing less.

Each time you accept a generic AI first draft, you're teaching yourself to be less creative.

What most discussions about AI writing miss is that LLMs excel at pattern recognition, not innovation. The breakthrough ideas come from humans asking "What if?" instead of machines asking "What's typical?"

According to research from SmythOS, AI systems can combine existing ideas in new ways, but they fundamentally cannot generate true conceptual innovation. They lack the contextual understanding that gives writing depth and resonance.

The taxi vs. personal car model

AI writing tools like taxis versus your own car.

The taxi (full AI generation)

Gets you there quickly. Goes where everyone else goes. Leaves no room for scenic detours.

Result: Fast but forgettable content 🔴

Your car (human-driven, AI-assisted)

You control the route. You decide when to take the scenic path. You make the memorable stops.

Result: Distinctive content worth reading 🟢

The efficiency gain (85% time savings) comes with a devastating creativity tax.

Three AI collaboration models that actually work

After months of experimentation, I've identified three approaches that preserve your creative sovereignty.

1. The research assistant

You: Define the topic and angle
AI: Gather relevant statistics and background info
You: Write the core narrative and insights
AI: Help polish rough edges and awkward phrasing

2. The idea expander

You: Provide your core concept
AI: Generate multiple perspectives on your idea
You: Select and integrate only what resonates with your vision
AI: Help connect disparate thoughts into a cohesive whole

3. The first draft partner

You: Create a detailed outline with your key points
AI: Generate a basic draft following your structure
You: Heavily rewrite to inject your perspective and voice
AI: Help with final edits and formatting

The key in all three models? You remain the creative director. The AI is just another tool in your kit.

Why this matters beyond your next article

When every company uses similar AI tools with similar prompts, market differentiation collapses.

Research published in Nature confirms that hybrid human-AI systems produce more innovative outcomes than either alone—but only when humans maintain creative control.

The organizations that will thrive in the AI era aren't those who use AI most extensively, but those who use it most strategically.

The three-layer framework for AI writing mastery

To maintain your creative edge while leveraging AI's capabilities, implement this framework:

Layer 1 - automation (where AI shines)

• Research gathering
• Basic formatting
• Grammar checking
• Synonym suggestions

Layer 2 - augmentation (the collaborative zone)

• Outline expansion
• Alternative phrasings
• Transition suggestions
• Tone consistency checks

Layer 3 - innovation (human area)

• Core message development
• Unique perspectives
• Personal anecdotes
• Original insights and arguments

As SearchEngineLand's research shows, this layered approach is what separates mediocre AI-generated content from truly compelling work.

Five practical steps to start today

1. Document Your Voice Fingerprint
   Create a reference file with your vocabulary preferences, sentence structures, and metaphor styles. Use this to guide AI interactions.
2. Set Clear AI Boundaries
   Before each project, decide which elements must remain 100% human-generated. Write these down to resist convenience temptations.
3. Use Specific AI Prompts
   Replace "Write a blog about customer retention" with "List 5 statistics about customer retention in SaaS businesses that I should consider for my article."
4. Implement the 50% Rule
   No matter how good the AI output seems, commit to rewriting at least half to ensure your perspective shines through.
5. Create Feedback Loops
   Use AI outputs as starting points for further refinement, not endpoints. The best results come from multiple human-AI iterations.

The road ahead

According to AI prompting research, the most sophisticated creative partnerships involve continuous human-AI interaction throughout projects.

The challenge isn't whether to use LLMs—they're too powerful to ignore—but how to use them without surrendering what makes your work worth reading.

Remember: the AI is just a passenger with an excellent map. You're still the one who should be driving.

Your readers aren't looking for perfectly structured, algorithmically optimized content. They're looking for your unique human perspective. Don't trade that for a few hours of convenience.

Last week, we had beers in Warsaw with tech guys of my age. One of them watched a CEO's face fall as his tech team explained why their much-hyped LLM implementation wasn't delivering the promised results. "But everyone's doing it," he said, confused. "What are we missing?"

This scene plays out in boardrooms these days. Companies rush to deploy large language models with the same vigor that drove the dot-com boom or the initial charm of generating human-like text masks deeper questions about real business value.

Boring, applications that solve specific business problems

I've spent the past year studying companies' LLM implementations, and I've noticed something fascinating: the most successful ones rarely make headlines. They're not the flashy chatbots or the automated email systems that companies love to showcase. Instead, they're careful, sometimes boring, applications that solve specific business problems.

Take the case of a mid-sized company I worked with. While their competitors raced to build customer-facing AI assistants, they took a different approach. They used LLMs to help their frontline staff write more accurate reports. Not exciting enough for a press release, perhaps, but it cut processing time by >30%. The key? They thought deeply about the human-AI partnership rather than chasing full automation.

This brings me to what I believe is the fundamental mistake in Rama Ramakrishnan's otherwise excellent analysis. While his "generative AI cost equation" provides a solid framework for evaluating LLM projects, it misses the hidden costs that often sink these initiatives.

I have first-hand example. It what happened at a software company that automated their first-level technical support with LLMs. On paper, it looked great - faster response times, lower costs. But six months in, they noticed something troubling. Their senior engineers were spending more time fixing complex problems because they weren't seeing the early warning signs that used to bubble up through routine support interactions. The "cost savings" were actually pushing problems downstream.

These hidden costs take three main forms:

The opportunity cost of taking the easy path. When you automate existing processes, you might miss chances to reimagine them entirely. The same as putting a faster engine in a horse carriage instead of inventing the car.

The human cost of fractured work. When LLMs handle the routine parts of knowledge work, the remaining human tasks often become more fragmented and demanding.

The institutional knowledge tax. As more work flows through LLMs, organizations risk losing the pattern recognition and tacit knowledge that come from humans doing the full range of tasks.

Treat these tools as intelligent assistants rather than replacements for human knowledge workers

There's a better way forward. The most effective LLM implementations I've seen share a common philosophy - they treat these tools as intelligent assistants rather than replacements for human knowledge workers.

Analogy would be - learning to cook with a sous chef. A good sous chef doesn't take over the kitchen - he amplifies the head chef's capabilities by handling prep work, suggesting ingredients, and maintaining organization. The chef remains firmly in control of the final product.

This "augmentation first" approach addresses many of the hidden costs while still capturing real productivity gains. More importantly, it creates sustainable competitive advantages by combining human judgment with machine capabilities in novel ways.

The future of LLMs in business isn't about replacing humans - it's about reimagining how humans and machines can work together. That's a harder challenge than simple automation, but it's where the real value lies.

The companies that win won't be those that move fastest, but those that move most thoughtfully

As for that confused CEO? His company changed the strategy with LLMs. After they stopped trying to keep up with competitors and started focusing on augmenting their employees' capabilities in specific, measurable ways.

The lesson is clear: in the rush to embrace LLMs, don't let the hype blind you to the real opportunities - or the real costs. The companies that win won't be those that move fastest, but those that move most thoughtfully.

I first noticed this pattern during an open data gathering where it was many words of support for transparency how it'll help build a better world through encouraging governments to publish more data. Then Steven De Costa joined with a proven solution - CKAN that is a successful vehicle for enabling open data publishing. I haven't observed the same level of enthusiasm when the conversation moved about concrete steps to make.

I was recently reminded of this gap between big dreams and concrete actions. Genevieve Guenther (a climate change activist) points out that: "... 59 percent of Democrats said climate change should be the Federal government’s top priority, but only 48 percent said they supported a phaseout (of a fossil fuels - author)".

That 11% difference. That's where the real story lives.

Think of it like wanting to get in shape. Nearly everyone supports the idea of "being healthy," but suggest skipping dessert tonight or waking up at 6 AM for a run, and watch how quickly theoretical support meets practical resistance.

Here's what's really happening: our brains process abstract concepts in the clever prefrontal cortex, where everything feels manageable and good. "Climate action? Of course! Who doesn't want a better planet?" But specific changes activate our more primitive brain regions, triggering loss aversion and status quo bias. Suddenly we're thinking about gas prices, job security, and whether we can still take that road trip next summer.

The societal implications are fascinating. Companies and governments often exploit this gap, making bold promises about future actions while resisting immediate changes. It's why organizations announce ambitious 2050 climate goals but fight tooth and nail against next year's emission regulations.

But here's the thing: understanding this pattern gives us a blueprint for driving real change. Instead of starting with grand visions, we might do better breaking down massive challenges into smaller, more digestible pieces. Rather than asking people to support an entire energy system overhaul, we could focus on specific, local improvements with visible benefits.

Ultimately, the path from 59% to 48% isn't just a statistical gap - it's the distance between who we aspire to be and who we're ready to become, one small decision at a time.

I realized something last week: I know more about some YouTubers' childhoods than I do about my own grandmother's life. This bothers me more than it probably should.

We all say we'll write our memoirs someday. We imagine sitting down in our later years, crafting the perfect narrative of our lives. But let's be honest - most of us won't. The task feels too enormous, too distant.

This hit me hard when my colleague Alexei lost his father unexpectedly. All those unasked questions, all those untold stories, gone forever. And that's when an unusual idea struck me.

What if we approached memoirs like we approach software development? Start with an MVP - a Minimum Viable Personal history. Not the comprehensive life story, just the essential threads that make us who we are.

The format would be simple: a trusted friend or family member interviews you for a few hours. No pressure to be profound or complete. Just natural conversation about the moments that shaped you, recorded and preserved.

I tested this concept with a friend. He's a quiet software engineer who never volunteers personal stories. But in just two hours, with some careful questions, I learned about the summer job that changed his career path, the teacher who believed in him when no one else did, and the hilarious disaster of his first attempt at entrepreneurship.

The recording isn't perfect. There are awkward pauses and tangents. Some stories remain half-told. But it captures something authentic that traditional memoirs often miss - the natural rhythm of human memory and conversation.

The technical implementation could be straightforward. A simple mobile app that provides question prompts, handles recording, and generates an organized transcript. Add some basic editing tools and the ability to attach relevant photos or documents.

But here's what makes this different from typical "save your memories" apps: it's fundamentally social. The interviewer isn't an AI or a form to fill out - it's someone who cares about your story, someone who can ask the follow-up questions that reveal the really interesting details.

The result won't win any literary awards. It won't capture every important moment or insight. But it will preserve something real - a snapshot of how you tell your own story, in your own voice, to someone who matters to you.

I keep thinking about what I would have given to have just two hours of recorded conversation with my grandmother. Not a polished autobiography, just her talking about her life with someone she trusted. The jokes she would have told, the inflections in her voice, the little asides that never made it into family lore.

Sometimes the perfect is the enemy of the essential. Maybe we need to stop waiting for the ideal moment to tell our full stories, and start capturing the fragments we can, while we can.

The next time I visit my parents, I'm bringing a microphone. I have some questions I need to ask.

As a product manager, your ability to argue effectively and debate well is crucial. Whether you're pitching a new feature, defending your product strategy, or navigating team conflicts, mastering the art of argumentation can make all the difference. Here’s how you can hone these skills to build consensus and steer your product towards success.

Know Your Audience

To argue and debate effectively, you need to know who you’re talking to. Ask yourself:

1. Who is your recipient?
2. What is important to them?
3. What are their interests, desires, hopes, fears, and dislikes?
4. What are their opinions, biases, background, and professional knowledge?
5. What beliefs or mistakes do they hold?
6. What objections might they raise during the discussion?

Understanding your audience allows you to tailor your arguments to resonate with them and address their concerns directly.

Why Avoid Manipulation

While it might be tempting to use manipulation to win an argument, there are two key reasons to avoid it:

1. Short-Term Effectiveness: Manipulation might work temporarily but is unlikely to yield long-term support.
2. Moral Integrity: Manipulating others is ethically questionable and can damage your credibility.

Overcome the Inertia Factor

People’s beliefs form a complex network where some are deeply ingrained (core beliefs) and others are more flexible (peripheral beliefs). Changing core beliefs is challenging because people often accept false information as true and vice versa. Recognize this rigidity and plan your arguments accordingly.

Understand Motivated Reasoning

Our desires, fears, biases, and expectations shape how we perceive information and arguments. This unconscious bias, known as motivated reasoning, means we often defend our beliefs rather than seek the truth. Julia Galef likens this to a soldier’s mindset, where ideas are seen as allies or enemies. Instead, adopt a scout’s mindset—be curious and open to contrary information. Ask yourself: Do you want to defend your beliefs or understand reality more clearly?

Convince Your Opponent

To persuade effectively, you must:

1. Understand the network of your opponent’s beliefs.
2. Acknowledge the rigidity of this network.
3. Identify their core values and beliefs.
4. Focus your efforts on addressing these central points and core elements.

Four Components of Persuasion

1. Clarity: Be clear to avoid misunderstandings.
2. Understanding: Grasp your opponent’s logic and core beliefs.
3. Meaningfulness: Explain and justify your points, placing facts in a broader context. Think systematically and holistically.
4. Openness: Honestly express your attitudes, interests, fears, and desires. Be genuine.

Three Debate Strategies by Daniel Cohen

1. Argument-as-War: There are winners and losers, with high emotions. This often leads nowhere and is best avoided.
2. Arguments as Proofs: This resembles a scientific discussion, evaluating the strength of arguments, confirming premises, and justifying conclusions.
3. Arguments as Performances: These debates are conducted before an audience, who learn from the process and judge the arguments like a jury. This is a rhetorical model.

By mastering these strategies and understanding the psychology behind argumentation, you, as a product manager, can build stronger cases, forge consensus, and drive your product in the right direction. Effective argumentation isn't just about winning—it's about understanding, communicating, and leading.

Have you been to extremely skillful and interesting teams or companies and vice versa? I'd share my experience from both worlds so it can be a start of the thinking process if you're interested in the topic. There'd be a short story about 2 organizations and their differences. Both are in tech, in the same city, approximately at the same time.

Famous

A startup that quickly became profitable, with 3 people on board, grew to 20, then to 30 people. Hiring strategy wasn't dependent on current needs.

The hypothesis that worked out well was that strong developers know other strong developers. So if it was an info that someone from the 1-2 handshakes was looking for a new opportunity or generally speaking is ready to a conversation, he/she was quickly immediately hired in 30-50% of times.

Misses in the hiring process happened but they were surprisingly rare. On the positive side, the team was so colorful:

• A blonde model-looking lady that did rather complex HTML/CSS/JS markup in 3 hours, when 2 days at least were expected. Then covered in a lush wooled cardigan and watched soap operas for hours until someone figured out how she can help. She effectively replaced a small department.
• A mountaineer that went through several complex routes, e.g. Matterhorn. Had probably 0.1% amount of fat. He worked on a backend and had a reputation of a meticulous guy. He written 30 pager of a backend architecture, it felt bigger as was dense. His wife was doing some enduro racing and jumping.
• A jazz-singer lady was doing customer support. I haven't worked with her, but she was very visible in the office, famous for making up stories about everything. I though it was her way to making fun out of increased dramatics.

Results were there both in terms of financial sustainability and expanding to new niches. Newest technologies used was an additional benefit for everybody who entered. Not just in terms of tech stack but also working in new niches. Idea-launch cycle could be as small as 2 weeks and it wasn't a rubbish that was released.

Infamous

A software house with 20 years history, some famous clients and the worst reputation in the city.

When they went to other locations, it was surprising how people don't know about the company reputation and aren't ashamed at all of sharing own affiliation. Which led into conclusion that employer reputation is very much location-based.

We were joking that there are 2 types hires:

• It's something wrong with a person.
• It's something wrong happened in his/her life.

It was fair that we were part of the company, presuming that we were the later type but the question was always open :D

Hiring happened for a position, through a formal process, social package was almost inexistent. Interns were hired with 0 salary for first 3 months, I'm still shocked by this fact. Salaries were a bit higher than the market, difference between lowest and highest could be as big as 30 times.

Disengagement was a standard mode of operation. Once I got a developer in a team that had a habit of disappearing for 1-2 weeks without real explanation. Folks told that he was binge drinking.

Everyone who had an interesting skillset and experience tried to make a unique agreements with management, to stay out of the regular management flows and working by rules that he or she sets how him/herself.

Each setting presents unique opportunities and challenges

A miracle happened in the uncool company. We turned it around, from a complete bottom it became an upper 50% on the market by my subjective view. I'm still extremely proud of this job. It feels like you made a decent movie for a cinema with $100 budget and a bunch of folks that never acted.

For the cool company it was very hard to get into consensus on almost any decision. A 8 h. discussion on how we should name a new game I still can recall. When 3 people were in the team it was highly productive but it was enough for having issues with communication and mutual ignorance. It was a thing of working with high-ego rockstars where unexpectedly it was important to push your opinion hard to make a product happen.

Each of these employments were equally challenging and rewarding. I proud of both as success wasn't granted from the start. It was easier to fail than to win. But it happened that our hard work, focus, goodwill, vision paid off.

Acknowledging the pace of your organization helps understand where are you in the spectrum and think through, how does it fit you.

Observing product development organizations for 15 years gave me a good perspective on how they operate in terms of pace. More precisely - how often news appear and how fast changes are expected.

I identified 3 groups of organizational pace:

• 1 day,
• 1 week,
• 1 month.

Means you either should deliver over this time period or internal situation changes with this cadence.

It was a time where jumping into a new idea didn't make sense as new ideas arrived approximately each 2 weeks, so by the time you made a progress on the new idea, it's already too old to work on.

1 day cadence: never ending sprint

It's a productive for doing many repetitive activities of the same nature: demo calls, standardized PoCs, articles writing.

God bless your wellbeing, if you like to run until you drop, you'd enjoy. People don't take vacations longer than 2 days in 3 years. Passive aggressive communication with notes of cynicism is a way for employees to communicate that something is wrong there.

1 day cadence example

It was an American 🇺🇸 scale-up. Owners were making money in the first place so that organizational work and processes were treated as a third-class citizens. Hiring and firing was done on a whim. It felt like the organization is something that no one loves, owners included. It existed as a money making machine or a something to be sold.

"Thinking is good but warp on speed". Meant, thinking is for losers, run!

1 week cadence: marathon

This pace allows both planning and action, sometimes observing results, if feedback cycle is quick. Unexpected events could be absorbed. Higher-paced days can be balanced with slower-paced ones. I scoped chunk of work with end result can be delivered.

1 week cadence example

It was a Ukrainian 🇺🇦 product development organization with 30 members. One flagship product that made it shine. One flagship product, 1st worldwide in a competitive category. Top-level colleagues, Aeron chairs, full-package of benefits could be imagined. Some politics but more for fun as top-management was healthy.

No one would want to leave this place on his/her own.

1 month cadence: promenade

With this pace one has a lot of time on it's own to test and try many things, before it's time to present progress. It could be either boring or calm, depends on the point of view. This pace allows a good time for thinking through many ideas and options before getting into action.

1 month cadence example

It was in Norway 🇳🇴 where rather often, a reply on email was expected within 1 month. If it's a job search, 3-4 months is OK, makes up exactly 3-4 feedback cycles. People are nice and never stressed out. Meaningful conversations are expected, quality is taken care of.

No pressure, you'd have 2 years to find out how things work here.

Recently, I read a post about a guy who decided to go to 6-months unpaid leave to steam-off after working from 8 to 20. It's an extreme example with overtimes. It's harder to spot from within that the pace is too fast when working hours are fine and colleagues are nice. People tend to blame ourselves for underperformance which could not be the case.

Were you ever surprised by a mere normality. When you're joining a new company and people you meet, your boss, your colleagues, they're just normal. So you keep waiting when it starts to become real. As reality is intense, and leadership positions are demanding both intellectually and emotionally. So it should be a mere wishful thinking.

Then nothing happens, I mean nothing bad. You're doing the job, have nice people around, want to contribute your best as the result. How cool is this. When it happened that a mere normality on a job became both great and unusual?

Quantitative research gives some clues:

• Inflation is relatively high so buying power of your colleagues is likely to decrease.
• 17% of U.S. employees are actively disengaged by Gallup.
• Tech layoffs are still around.

Although there are problems in economy, in politics, there is a war in Europe it still doesn't feel enough to lose normality at a scale. Well it doesn't touch the modern russia or the war trenches, both of which not normal by default. Each decade there're problems humanity faces. Reading news from 1960's, 1980's, 2000's shows that "in the moment" there're always things to stress about. So there should be a mental shift that led us to the current workplace culture.

So how it came that it's a big surprise when your work environment is just normal? What if it doesn't matter. Why analyze and think it through when you can be just enjoying, doing your work, growing in a healthy place.

I don't have the answer, really, and I doubt that it worth spending time on it. If you got lucky, it worth using it. If you don't, keep searching, Holy Grail is there to be discovered!

I like Superdry clothes - they fit well, materials are of a good quality, colors are to the point. Though pockets in their sweatpants and shorts are just terrible. They're not designed for anything bigger than coins or mint candies.

Because of these pockets I lost my phones twice (both times got returned, never worn these pants after it). This time it slipped from the pocket right into a spring while I was enjoying the sound of a running water.

This week I'm going to a tailor for a minor adjustment to the pockets, which should provide a solid cost savings in long term, a $4 update would de-risk a $1k device.This pocket drama made me think about infrastructure on a software projects:

Infrastructure investment today could be a difference between success and failure

Technical debt as an idea was coined to explain to CFO the need of working on infrastructure continuously (not only infrastructure, but let's focus). Comparing interest rate accumulation for financial assets with a risk or burden accumulation of constantly appearing flaws.

Thus not making the investment now or not paying negative interest that accumulates make issues mount.

I evidenced how a successful business ceased to exist as a social networking platform that was the main value proposition, failed an important update. It happened as it was only work on new features, never on infrastructure, refactoring hasn't happen in 5 years.

A flaw left unfixed is a mounting risk

You know about a problem, but it waits. It hasn't broken anything yet and for a long time, you got used to it. If the risk is tiny. Could 1% or even less. It's still a probability for unwanted event to occur. With infrastructure, the consequences could be dramatic, but it's never a time for such things in a busy calendar.

The solution is working on processes and infrastructure weekly. Dedicating at least 1 hour into keeping it smooth long term.

It's something huge when you can count on things you use

When your notebook is reliable, sneakers are durable, glasses are safe. It could cost to buy good things and they don't pay off right away. Then it's something more about it - you can just rely on them, don't bother and free up headspace for more important, more creative work.

Personal examples are:

• Moving to Mac from Windows didn't look like a win at first, then I never had to re-install operating system, fight with frequent OS and software crashes. It saved 100's of hours in 10 years.
• Aeron chair was pricy and I'm sure there are alternatives but it gave another level of comfort when working long hours in front of the screen didn't felt like a discomfort anymore.
• An 3-layer membrane raincoat made me do regular walks in the forest despite we moved to a place with much more raining.

Each of above-mentioned examples provided something more than just a boring reliability. They leveled up the quality of life. It elevated me from a problems I had into a creative state that made me do big positive changes in life.

Stock prices go up and down all the time, it's a modus operandi for open financial markets. If you know what a stock price will be, you're having an ethereal power and can get rich fast. If you're mistaken, you can loose everything. There are arguments that stock prices are exhibiting a "random walk" so they're unpredictable. While some traders believe in patterns that stock prices take, like Elliot Waves. Meanwhile there's a market of trade advices to help you. Or to mislead you?

One of the websites that provides information for retail traders and trade advice is Seeking Alpha. They have a feed with predictions and analysis sourced by authors that usually represent a firm or are advisors themselves. With investment advice it's always puzzling of who to listen, as at a particular point of time on one topic you can get two diametrally different opinions. One say Buy, another say Sell and reading through doesn't help that much as both are well written, analysis are fact-based, judgements are sound.

Seeking Alpha provides a context for a reader to understand the source of information better:

• What is the past performarce of the predictor?
• What is his/her the investment time horizon?

To find out:

• If his/her advise suits me?
• Can I trust him/her?

Each article supplied with a simplified recommendation: Sell, Buy or Hold. Then you see a historical price movement of a discussed stock and previous recommendations of this advisor.

Before reading the advice we check the content block above to get important context about the author. At this article he advises to Hold the stock. We see that he writes mostly after the stock dropped significantly and the advice is always the Strong Buy. The idea is simple, after a significant drop, a stock should bounce back, but for SNAP it didn't happen. Only once it bounced back then to keep declining.

It turns out that if you're an investor with a 1+ month time horizon, you'd be constantly loosing money by following advices of this author as when he advises Strong Buy it's actually better to Sell.

Lesson learned

Providing a context is a powerful way to deliver additional value or even build up a differentiation as in the abovementioned example with Seeking Alpha. It's important to understand what job your user would like to get done with the functionality you provide.

As the afterword I'd like to share the video by Kate Moran on why in UX Design it's no silver-bullet approach and you should think through each time, even if you crated the same functionality before. Each time the context is unique and it changes everything:

We're paying attention to User Experience (UX) and Customer Experience (CX), but we can  do more and build constructive relationships with our users. Relational Experience (RX) helps to form loyal behavior and increase the user's role in the product. For example, make it possible for a user to spend more on the service or spend more time on the a website pages for a continuous period of time.

Below we'll be talking about:

• relational model,
• transactional relationships,
• relational position;
• individualization and more.

It's rather evident that relations are important for living a healthy life. A number of studies link sense of relatedness and belonging to the health, happiness and life expectancy, while loneliness for a long period of time, can be harmful, for our health and mental state.

Yet we spend 8 or more hours each day, at work it's approximately half of our life (if we exclude sleep) according to Gallup research, only 15% of people at work are engaged. Think about it for a moment, something is completely wrong here and it's hurting us.

This is the problem to solve. Actually, one of problems, which RX (Relational Experience) Design can address.

Let me introduce Olaf. Here's the person I draw the knowledge from. Olaf is an academic in the field of relational psychology. It's a narrow area with only a few active academics worldwide. His works are highly recognized and applied. The company Olaf is leading works with governments and governmental institutions like the National Bank of Belgium.

Relational psychology brings a simple idea: We're together with you as partners in this, no matter what will happen. I will still be on your side.

It's an unconditional love and support. This is what everyone wants to hear. But it's not what is told.

Again, why to invest in relationships for a business?

Here's a detailed, mind-blowing research where different marketing and customer related approaches were tested:

• good service
• preferential treatment
• referral bonuses, etc.

The results shown that - customer appreciates benefits. He/she feels positive, and satisfied. He/she reports good net promoter score.

BUT

The customer does not commit. Enthusiasm, real commitment and engagement doesn't come, as the company never tells - we're in this together, no matter what. It's the root-cause. If relationship ticks - people go back, buy more often, they feel better.

So it's a model been developed with a name of R. Here are key facts about this relational technology:

• It happens in the now
• It's about a group
• "There's a sense that we're moving forward"

R is not:

• CRM stuff
• Personalized communication
• Getting knowing a customer better and serving him better

Essentially, it's not about people it's about systems.

R is about designing for a sense of moving forward. No heaviness, it's light, agile, projects good hopes for the future. Collectiveness is also there.

We don't measure time, it's important! As the latest research shown that trust and relationship can be built in 30 seconds if you say right things to the right people at a right moment.

Left side it's the current position (Customer Position) and to the right it's a position where we want the customer to be. (+) means that all good (-) at the top right means that Relational Position requires many things from the start. It's a discomfort a customer has when we want to build relations with him/her e.g. "I don't want relations, i'd just use your stuff and that's it".

The job of R is to convert (+) (-) position to (+) (+) position. From only a customer to a customer + partner.

The flipping point is the Goodwill (at the middle). It's the key metric that's measured with R.

If you've read Kahneman's "Thinking Fast and Slow", you know what system 1 (s1) and system 2 (s2) means. If you don't, basically s1 is a fast way to process information based on what we know, existing models, norms ect. it's a fast intuitive thinking, it has logical flaws and it's effortless.

While system 2 is slow thinking, it's the time when we turn login on when we're thinking deliberately. It's a laborious process and it's a logical thinking.

Here we have an extended cognitive model. Thousands of hours of research work are behind this one slide (according to dr. Hermans), so it's a simple representation of a complex concept.

Logical side is at the left. New info makes us think deliberately by the left side (so in 10 mins it becomes hard to do). Back of mind - at the right side. It's strategic thinking. If I'd saying all this speaking, you might immediately think: "what I can do with this info", "how it'll change my commitments", "is Alex really here for me".

Front and back work at the same time. Front - "it makes sense", back - strategy. s1, s2, s3 are a control system, r3 is a goodwill. We want to achieve a positive intervention instead of "I don't care". This is what Olaf Hermans added to the model.

The idea here is that if we can design for suspicious brain, we can take the suspicion away

This is how we communicate with a customer along his journey. Some information he finds by himself, while some is communicated directly to him/her. Everything is gray, it means that the information appeals to the front of our mind - it induces logic. It's about intuitive mind - "what's the logical next thing to do". Gray is the ongoing processes. s1 is less conscious than s2, during the day. No orange on the chart, means that there are no relations included.

Now, we're blending yellow into gray. First goes - relate & involve. There we're inducing (stimulating) bias. It's first and important step.

Humanization means bias. Without bias world would be de-humanized.

People naturally relate, they'll will respond to R in a natural manner.  We're starting with warming up (1st column), then we're moving into action (2nd column). Users participation will be more active if you feel that we're committed to him/her.

Please pay attention to the arrow at the center.

The line  starts gray and ends yellow. As it starts with a transaction (logged in,   account checked, etc.) but ends up in relations with an app (or a company). The only thing what  user will recall afterwards is that a friend asked him/her for something. He/she would not recall how it started but it ended as a friendship.

User came in the customer position, to leave in the relational position.

It's not about giveaways (gifts, money) It's about a goodwill. If one pays a customer for something, one doesn't know how to relate.

Here is the essence of the model, which consists of subsequent steps, that should be taken one after another

As we know already - time scale doesn't matter. The sequence and the algorithm matters.

As an example of communication: If a customer that enters a party, instead of saying: "welcome, this is what program looks like" and "would you mind filling out the evaluation form afterwards" we're trying to build relations, induce bias, create a mutual context.

The actual implementation can be of a different level of complexity. Building blocks for each specific case can be drawn from this table. This encompasses a full model, disassembled.

Also R is the umbrella term and can be applied throughout a spectrum of disciplines. You can take the above-mentioned principles and apply them to your area of responsibility.

It was nice to share all that with you. Right now I'm into setting up an internal marketing campaign with R, for 300 ppl company. Will be happy to share results and observations afterwards.

A guide for delivering smoothly against highest expectations.

You just got a client it was relatively easy to find… actually he found you as a high reputable implementer. Also he’s OK with paying premium to start working with you right now. The client is ready to give you all the power to define both strategy and implementation. It’s like a dream come true. What your gut, feel tells you, that something bad should happen.

Your client has a lot of ideas from the day one. In fact he has a big dream. You have a clue how it may come true, but they are too many unknowns at this point. You are brave, skilled and have a proven methodology. It was done before so it’s just another job to be done. You’re onboarding quickly, finding out as much as possible about what is expected and building a logical and efficient path to deliver up to these expectations.

Dream doesn’t have constraints, while you have

After first three months you make your first delivery. Then second delivery in six months. What you know for sure at this point is that your client is never happy with the job, despite your best efforts. You’re getting into analysis: maybe I missed something critical? Priorities were wrong? What if I didn’t understood a market ecology well?

After all these reflections you come to a conclusion - tangible results you produce are always compared to a dream. Dream is always better, it evolves constantly, it’s pure perfection. Dream doesn’t have constraints, while you have. In fact you have a lot of them. Moreover you never have enough time, because the impossible should be delivered as fast as possible.

Though it can be a great opportunity. What you need is to know the rules of this game. Knowing and adjusting to them make lose/lose become win/win. Here are some advice to start with.

Build prototype, MVP or proof-of-concept

It is crucial to test the idea early. It wouldn’t go easy, the resistance will be based on the notion that the basic product doesn’t include the complete idea therefore the test can’t be viable. Knowing that the whole idea is the dream that one can chase but not achieve.

Findings from this early test will be priceless as they will lay the ground for evolution of the dream. Early in the process you will divert the dream to the direction that has much more chances to be achieved.

Strong project management skills mean a lot here

Define scope in details. List risks and strategies how to deal with them. Develop a long-term plan split it into projects. Manage knowledge carefully by listing ideas, put decisions on the paper and rationale behind most important of them.

Communicate often

Narrowly defined scope wouldn’t save you on its own. It’s a journey, anything may happen. Communicating often, you will align steps taken now to the overall vision, to be able to adjust on the go.

Agree and deliver in projects

Don’t go too far before delivering something tangible. Be sure that none of what you deliver satisfy the customer right away. The trick is that your current delivery will be the basis for the dream in future and will be a non-discussable advancement after several increments. In order for this technique to work each project should deliver actual value and used by a target auditory.

Concentrate on positive

After all is a well paying client that cares. Your work is necessary for him. Emotional concentration should be on the long-term achievements not on the short term rumblings. It wouldn’t be the most pleasant experience but you will learn and try a lot, as a result becoming a better professional.

Summary

Working with visionaries is both a great opportunity and a perfect chance for disaster. Having a right strategy, using relevant techniques, having positive frame of mind and willingness to achieve impossible could make you both win against the odds.

Find out what is known, before testing hypothesis on your own.

When the focus is on listing and prioritizing things that you want to prove, for your new product it’s easy to miss out what is known already. It’s no need to re-invent the wheel. In this case it’s no need to prove what already been proven.

There are something had happened on the market (1), something is happening right now (2) and there are actual results (3). Why not to get into all this vast of knowledge that directly relates to what you plan to do. Chase for gems - the data that is meaningful, open and has relevant business value in it.

Where to find the data:

1. Industry news websites;
2. Competitor’s news section on an own website;
3. Expert reviews / Niche influencer posts;
4. Niche forums;
5. Product and company review websites.

Finding proven hypothesis

Let's go through some examples of what to look for and how to interpret the information. These examples aren't perfect, their job is to be a though igniters:

ℹ️ A competitor, company C sold to a provider of online tests, company H for $100 mln.

✅ The product you’re developing has a synergy with an educational company.

🤔 Are there possible synergies with your product and another vertical markets?

🤔 Are there possible synergies with an educational provider on the market?

ℹ️ A competitor settled a contract with a local bank for integrating own product.

✅ There is a perceived or actual need in your product by a bank.

🤔 Is there a perceived or actual need in your product by other players from banking or financial sector?

🤔 Is there a local market for our product that we can easily reach?

ℹ️ SEO analytics shows that a competitor gains 500k visitors per month to their blog.

✅ People are interested in the the theme of the product.

🤔 What is the gap between what is promised and what the competitor’s product actually delivers?

🤔 What other themes in this niche we can utilize?

Summary

It makes a perfect sense to do a market research at the start. For entrepreneurial endeavor it’s important to deconstruct news and happenings into proven hypothesis you can build upon. So at the end of the day you’re standing on the shoulders of giants. Right from these shoulders you have a perfect chance to innovate.