Find me on https://lovable.dev/@lazarjovanovic and learn Lovable on https://february.lovable.app/

Lazar Jovanovic

Professional Vibe Coder at Lovable | L5: Diamond Certified

Next stop - Lovabletown! ❤️ 

Dream it -> Ship it

Am I still crazy for saying no one will need to code 6 months from now? 
Am I still crazy for saying that PMs and designers do not need to learn how to code to build production grade software? 

Am I crazy? Or am I just trying to shake you from your roots? Make you do something about it? 

Not freak out by any means - but by all means take action and start doing something. 

It's entirely your choice and within your reach, please please please listen to Lenny Rachitsky and Boris Cherny dive deeper into this topic. 

Don't listen to me. Watch me as an example it is possible, and do something! 

https://lnkd.in/eK5jAdwq

Open video: Head of Claude Code: What happens after coding is solved | Boris Cherny on youtube.com

Head of Claude Code: What happens after coding is solved | Boris Cherny

Trying to come up with a good caption for this one.

This lesson is probably going to be very polarizing so I need your opinions on this in the comments. 

My POV:

The real leverage isn't in building more apps. 
It's in building fewer apps that can become many.

I used to think every project had to be unique. 
A new idea, a new market, a new everything. 
That is backwards.

Most beginners build apps. 
Builders build templates.

I don't mean a generic boilerplate. 

I mean a repeatable solution to a repeatable problem. 
Because most people in the same niche need the same things.

Take fitness creators. 
They all need workout delivery, progress tracking, and community features. When you build it, the pitch isn't "I have an idea for an app." 
The pitch is "I already built this. It just needs to look like you."

Or look at podcasters. 
They have audience and trust, but they lack products. 
You build a companion app once with searchable content and AI summaries. You use the same structure and simply swap the identity for the next creator.

The math changes everything:

Build one solid template in two weeks. 
Customize it for a client in two days. 
At $2,000 per branded app, five clients is $10,000 from that same initial week of work.

To find your own template, look for these four things:
1. A niche where people share the same needs.
2. A stable structure where only branding and content change.
3. A market where people are already spending money.
4. A clear way to reach that audience.

Your best work isn't just for one client. 
It is proof that you can deliver something premium, and that proof can be replicated.

Find a repeatable need, build once, and customize many times.

Catch Day 26 and the full lesson at https://lnkd.in/eWZShRpu and tell me if you agree about this being the easiest way to start monetizing your Lovable builds?

I think most people are still using AI at the surface level.

They use it to generate something faster. 
A document. 
Some code. 
A summary.
A deck. 

And that’s fine. 
It already feels magical.

But the real shift for me wasn’t speed. It was structure. I stopped thinking of AI as a tool that produces output and started treating it like a thinking partner.

Now I use it to pressure-test ideas before they become expensive.
1. To argue with me.
2. To ask me the uncomfortable questions.
3. To simulate edge cases I wouldn’t have thought about on my own.

The biggest unlock isn’t “write code for me.”
It’s shortening the feedback loop between idea and clarity.
That’s the part that changes how you work.

Vibe coding is just problem-solving under new constraints.

People hear the term and think shortcuts or laziness. 
In reality, it is the natural next step in the history of software development.

In Day 27 of 28 Days of Lovable, Marija Milosevic Lovable Ambassador and a former software engineer with 10 years of experience writing Java joins us to reframe how we look at AI-powered building.

If you zoom out, the pattern is logical:
- We moved from binary to functions.
- We moved from machine commands to readable languages like Python.
- We moved from manual coding to visual low-code builders.
- Now, we describe functionality in natural language and let the system generate the structure.

Every time we build on top of a new layer, we give up a bit of control in exchange for leverage. 

That has always been the deal. 
But faster execution does not mean less thinking. 
If anything, it means your thinking matters more. 

Thinking, judgment, and taste are what keep you calibrated when the tools move fast.

Over the last 27 days, the goal hasn't been to master a tool, but to master a loop:
1. Identify a problem.
2. Understand the constraints.
3. Design a system to solve it.
4. Execute as cheaply as possible.
5. Review honestly.
6. Decide deliberately.

When you look at an app like Uber, it feels like magic. 
But when you break it down into driver location, your location, distance, and a recalculation loop, it stops being mysterious. 

It becomes a system. 
And once you see the system, it is buildable.

Good builders don't wait for perfect tools. 
They see limitations early and design around them. 
That is what transfers. 
That skill doesn't expire when the tools change.

Catch Day 27 and the full lesson at https://lnkd.in/eWZShRpu

Community spotlight time! 

I asked -> you shared -> now it's my turn :) 

I cannot be more grateful for all of you that kept sending me Lovable app links for the past week. 

I hate singling anything out - but these are the 3 that popped for me. 

Keep sending stuff that you build and I'll do my best to feature you. 

https://meucv.online/ by Diego Ruzzinenti - simple (love that) application, the reason why I really loved it is because he took advantage of everything that AI development can offer. Check out the animation of how the app works on the page. That is a game changer. You no longer need to record videos of your demos or get people onto the waitlist. Just show them. 

https://lnkd.in/e8disuhJ by Erik Willemse - very cool for getting a bit of perspective in life, in case you ever asked yourself the question I've been asking myself a pretty decent chunk of my time on earth, which is, "Would my life be any different if I was born elsewhere?" 

https://lnkd.in/ezm3xJ88 Michael Tippett - Most people come to me, and when I ask them why they haven't used Lovable, they tell me they don't know what to build. I think this is one of the best apps to solve for the "cold start problem" that I've seen recently. 

Keep shipping, keep sharing, and I'll keep giving you the spotlight. 

Happy Friday!

Tomorrow, to end the lesson portion of 28 Days of Lovable - I have another surprise guest to announce! 

She's a Lovable Ambassador, a wearer of all hats one could put on their head and the best person I could think of explaining what I believe vibe coding is really about - problem/constraint solving! 

Stay tuned for an amazing 15 minute session from Marija Milosevic! I was on the edge of my seat for this one, and am convinced you'll be as well 🙂 

(and you'll hear a few amazing few quotes too)

Everyone talks about how AI makes you faster.
That’s true.
But something else happened that I didn’t expect.

It made me more exposed.

When you can ship in days instead of months, there’s nowhere to hide.

If the idea is weak, it shows immediately.
If your taste is average, it shows immediately.
If you don’t understand the user, it shows immediately.

Before, you could blame timelines.
Or dependencies.
Or engineering bandwidth.
Now the bottleneck is you.

AI removes friction.
And friction used to hide mediocrity.

That part is uncomfortable.
But it’s also the opportunity.
Just depends on where you stand. 

We're all producing faster. The question is what - magic, or garbage.

Most projects don't die because the idea is bad. 
They die because the structure collapses.

On Day 22 of 28 Days of Lovable, we are discussing the uncomfortable truth: sometimes a project isn't worth fixing. 

When fixes start breaking other fixes, you are no longer progressing. 
You are fighting entropy. 
And entropy always wins.

Starting over is not quitting. 
It is optimizing for leverage.

If you find yourself re-explaining the entire system to the AI just to make one change, you are past the point of efficient repair. 

The professional move is to extract what worked, identify what caused the breakdown, and move the good components into a fresh build.

You do not owe a project completion.
You owe yourself systems that can evolve.

Watch the full lesson on structural failure and how to restart intentionally in this video

For a long time, building something felt like training for a marathon.

You would prepare for weeks.
Write specs.
Align stakeholders.
Wait for engineering bandwidth.

By the time something shipped, so much time had passed that it almost felt detached from the original idea.
Now it feels more like going to the gym.
You have an idea in the morning.
By the evening, there’s a working prototype.
And that changes something fundamental.

When building is slow, you can hide inside planning.
You can say, “We’re still refining the strategy.”
You can live in Notion and feel productive.

When building is fast, that shield disappears.
If the idea is weak, you find out quickly.
If users don’t care, you know within days.
If the execution is sloppy, it’s visible immediately.

AI didn’t just make me faster.
It removed the space where I used to overthink instead of finish.

And finishing and shipping something real, however imperfect - is becoming the actual edge.
Not intelligence.
 Not credentials.
Just the discipline to turn thoughts into things.

Don't watch this video at all! 

Day 21 is about building, and this post is for my own consistency purposes - your only purpose for the day should be to create! 

And then visit https://lnkd.in/esnAfSXh to submit what you created as I plan to share it tomorrow. Stop reading! Go ship!

Finally on Android!! 

Shane Spencer let's get to 1M faaaast 😎 Bobby Siegfried can't be the only one!

I was literally thinking this morning - if we're all solving for software, how will the hardware keep up? 

Perhaps this is the answer!?

This result is just mind-numbing. When you can generate 1M toks/min (which corresponds to say 3-4k pages of a book, or a pretty large codebase) - what new AI applications become possible? :)

Ljubisa Bajic a fellow Serbian and former co-founder of Tenstorrent, is building a new AI ASIC at Talaas.

When we spoke last February, he told me this was exactly the direction he was heading in - and now a year later, he’s shipped it.

The ultimate chip specialization: burn the weights of the transformer directly into the chip. Why even deal with software (at least at that level of the abstraction)?

When you're providing the intelligence infrastracture at planetary scale (and given how much the LLM architecture has converged)  this can be a very viable economic model. You tape out a chip that can support 3-6 months of inference - and then you ditch it after that.

Keep in mind that these numbers are for an 8B heavily quantized model (so the quality is somewhat lower) - but then again this is just an "MVP". :) We'll soon be able to do this at a much larger scale.

Check out their blog post here: https://lnkd.in/gEXdXBKs

You can apply to try it out.

Most challenges reward outcomes. This one rewards process.

Today is Day 28 of 28 Days of Lovable, and we are closing the program by opening the 7 Day Buildathon.

Starting now, you have one full week to build and ship a project using Lovable. 

But I am not looking for the prettiest UI or the biggest scope. 
I am looking for clarity of thinking and constraint awareness.

I am judging five specific pillars of execution:

1. Deliberate Starting Point
Show why you chose your entry point. 
Did it come from a real problem, a remix, or a template? 
Day 1 through Day 5 were all about this.

2. Intentional Documentation
You must demonstrate context preservation. 
I want to see your Forward docs (what you planned) and your Backward docs (what you actually built and the decisions you made along the way).

3. Agent-Oriented Execution
Show how you designed for execution, not just prompting. 
This means structured task breakdowns and phased builds with verification steps.

4. Systems Over Memory
Demonstrate how you avoided relying on one-off prompts. 
Show the system you used to keep the project aligned as it grew in complexity.

5. Reflection
This is critical for builder maturity. 
What broke? What limitation did you hit? How did you work around it? 

A month ago, you might have hesitated to start or feared breaking things. 
If you have done the work, you are not that person anymore. 
You have practiced the loop: identify a problem, design a system, execute cheaply, review honestly, and decide deliberately.

That loop is yours now. 
It does not depend on any single tool. 
The thinking transfers.

Submissions close next Friday. Winners will be announced next Saturday.

Catch Day 28 and get the full Buildathon briefing at february.lovable.app

P.S. I am proud of you. And proud of myself for showing up every morning in February. It's now the best month of the year for me ❤️

In the ever-changing world of tech I've carved a niche for myself as a committed Software Developer. My love of the subject has not just resulted in me accumulating readers of over one million on a variety of blogs as well as to launch 2023's most sought after Data Science Bootcamp. My academic background is solidified by an Bachelors in Computer Science which has been crucial in my work.

My method of developing websites is multi-faceted, making sure there's the seamless integration of aesthetics for the front end and robust back-end functions. This balanced approach has enabled me to design user experience that stands out connecting with users and meeting the business goals.

Collaboration is at the core of any successful project. I've had the honor in leading, and working with some of the most skilled teams in the business. Together, we've tackled problems with aplomb, streamlined workflows and incorporated technology into our decisions that are in perfect alignment with the overall goals of our projects.

Performance is not a matter of negotiation. Each component I'm involved with undergoes rigorous optimization procedures to ensure efficacy, scalability and reactivity. Each of the projects I've completed has served as a learning experience and has provided me with valuable lessons and enhancing my method further.

The concept of AI has always fascinated me. I've been pursuing AI's capabilities to improve the offerings of our products. The aim? Improved user satisfaction. The process of infusing AI into the process of optimizing products has been thrilling and I'm always watch for the next major breakthrough in this field.

Some of the milestones I treasure in my heart are co-founding the world's most prestigious Data Science Bootcamp in 2023 getting an appearance in the UBC Dean's List in 2022 and training more than 1000 developers since the year 2015. My contributions and work have also been acknowledged by industry leaders, including appearances in respected publications like Forbes.

In the words of industry experts I've had the pleasure to collaborate with, my commitment to digital excellence is evident. As Entrepreneur puts it, my approach to digital solutions is both innovative and grounded, striking the right balance between vision and execution.

My professional path is a combination of technical proficiency, a team spirit, and an unstoppable determination to achieve excellence. I invite you to join me to collaborate and take part in this thrilling digital journey.

Vahe Aslanyan

Most people give far too much weight to short-term thinking.

And because of that, they often end up experiencing the exact consequences of their short-sightedness: lost opportunities, stalled growth, and unrealized potential.

One clear example? Learning.

Learning new tools.
Learning new skills.
Learning how to operate in a new field.

People try once.
They fail once.
And they stop.

But failure rarely comes out of nowhere. It usually comes from lack of information, lack of structure, or lack of repetition. Yet instead of adjusting and continuing, many give too much power to the discomfort of the short term.

We are living in an era of noise, hype, and instant gratification. Quick wins are glorified. Long-term investment is underrated.

But everything meaningful operates on foundational principles.

If you consistently take the right steps, structured around solid principles, the cumulative effect will lead to progress. That’s not motivational talk — it’s mathematics. Repeated good inputs compound into good outputs. Repeated bad inputs compound into losses.

The key is balance.

If you focus only on the long term without short-term execution, you drift.
If you focus only on short-term wins, you collapse strategically.

Sustainable success comes from balancing both.

Take artificial intelligence as an example.

Many people say they want to learn AI tools. They try one tool for two or three hours. It feels confusing. They give up.

But today, access to information has never been greater. Access to powerful assistance has never been greater. The leverage available to individuals has never been greater.

Information is power.
Applied information is transformation.

If you fail for three hours, five hours — that is not a signal to quit. It is a signal to refine your learning process.

Learn how to learn.

Short-term discomfort is the price of long-term capability.

And long-term capability is what wins.

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Founder of LunarTech, SeleneX, Nomad and Midnight

Success in leadership rarely comes from reacting emotionally to problems. It comes from building the right pipelines, the right systems, and the right habits to respond intelligently.

A problem appears. One person freezes. Another person studies it, breaks it down, looks at what others have done, finds the right tools, builds the missing pillars, and moves forward.

That is often the difference.

Most problems are not impossible. Most are simply unanswered.
Not because the solution does not exist.
Not because the talent is not there.

But because too many people stop too early.
They do not search deeply enough.
They do not ask for help.
They do not upgrade their tools.
They do not upskill.
They do not build the infrastructure needed to solve the problem properly.

In business, and frankly in almost any field, progress comes from a more disciplined approach:

First, understand the problem clearly.
Then analyze it without ego and without drama.
Then identify the tools, knowledge, people, and systems required to solve it.
Then start building.

Pillar by pillar.
Layer by layer.
Step by step.
That is how the house begins to stand.

The truth is, even the strongest team cannot operate well without the right instruments. In the modern era, this includes data, computation, workflows, feedback loops, and a mindset of continuous improvement. If you want modern results, you need modern methods.

Take something practical: building a drone.

If someone does not know how to solder, do they stop there? Or do they look for tutorials, learn from others, practice, fail, adjust, and practice again?
That is how capability is built.

Not through wishful thinking.
Not through waiting.
Through repetition, analysis, and iteration.

This principle applies almost everywhere:
When you do not know, learn.
When you fail, analyze.
When something is missing, build it.

When the path is unclear, create the structure that makes clarity possible.
A lot of people want outcomes.

Fewer are willing to build the foundations that make those outcomes possible.
But in the end, foundations are everything.

#Leadership #Execution #ProblemSolving #Innovation #GrowthMindset 
#DataDriven #SystemsThinking #BuildInPublic

No matter what you do for some people, they will always ask for more.

It doesn’t matter if you build their daughter a hotel, or create the perfect venue for their “historic” meetings with fellow prime ministers. It doesn’t matter if you build the most beautiful buildings in the most beautiful places. 

For some personalities, none of it is ever enough—because the goal was never appreciation. The goal was extraction.
And as time goes on, the demands don’t just increase—they get more illogical.

These are often the same people who try to “bend gravity” by lying. But you can’t interpret your way out of gravity. You can’t manipulate reality forever. Eventually, the mental energy required to maintain the lie becomes exhausting—and you start seeing it in their faces.

Meanwhile, the other side—the side dealing in reality—keeps gaining an “infinite” set of skills: negotiating, building, solving, adapting, creating real partnerships.

You can see the same pattern at scale between nations, too. If a country repeatedly changes standards, rewrites rules, and behaves inconsistently, partners start treating it as unreliable.

For example, if the United States constantly introduces new standards and shifts the playing field, Europe can begin to view the U.S. as an untrustworthy partner—and respond by taking tighter control of its own position and reducing dependency.

The principle is the same everywhere:

When you become difficult to deal with, people move on.

They choose partners who get the job done—because the unreliable partner delivers more problems than results.

And in the end, the maintenance cost of the lie becomes too heavy to carry.

You see it in national debt. You see it in low revenue versus high obligations. You see it in infrastructure failures, system breakdowns, and compounding inefficiencies.

At first, it looks manageable.

But that’s the trap: it’s not the end—it’s the beginning.

When you hold yourself for a fool, you gently open the door for fragmentation. 

It begins softly: you step back from what feels heavy, create a quiet distance, and soften reality with gentle thoughts—“perhaps it was never truly mine” or “this doesn’t really affect me.” Over time that distance grows, one small compromise leads to another, and wholeness quietly begins to ease apart.

This gentle distancing, whether in people or nations, often signals a quiet admission that something has been lost. Losing half one’s real estate remains a real change, even if we reassure ourselves it was never ours to keep. There is no need for celebration or blame—only a calm recognition that land, resources, and possibilities have slipped from the whole. From there, manufacturing may slow, true innovation grows rare, and new technologies sometimes quietly drain a country’s strength. 

The same fragmentation appears most clearly in how leaders relate to their groups. Strong leaders stay whole under pressure. They remain connected, flexible, and open to many paths, giving themselves time to adapt without breaking. 

Yet when a leader begins to draw smaller circles—thinking “these are not my people,” “this group is not mine,” “that part no longer belongs”—the center softens. Starting to lose groups or allowing internal division through such quiet disowning is not clarity or protection; it simply invites fragmentation. This truth holds only for the leader of the group. It does not apply to those outside or unconnected, who carry no responsibility for the whole.

Downplaying our own strength can invite the same gentle fracture. It is like a diamond passing through iron as smoothly as a Stanley knife through paper. By softening our sense of power, we make what was solid feel more fragile than intended.

In the end, behavior that leads to the loss of half one’s real estate—or half one’s sovereignty, productivity, or future—does not reflect enduring strength. Strong leaders remain whole.

🚨 $0.30.

That’s the price of a full AI employee that just crushed GPT-5.2 on real computer tasks.

Anthropic just dropped Sonnet 4.6 — and it’s legitimately scary how good it is.

Benchmarks:

Sonnet 4.6 → 72.5% on real computer tasks
Opus 4.6 → 72.7%
GPT-5.2 → 38.2%

Mid-tier model. Opus-level performance. $0.30 pricing.

Tasks you can now automate for under $1:

Expense reports → $0.20–0.30
Email triage → $0.15–0.40
Competitor monitoring → $0.40–0.60
Data entry → $0.20–0.50
Meeting prep → $0.30–0.50

We are just getting started.

Claude Code: The Complete Handbook — a three-part enterprise whitepaper covering AI-assisted development from first session to full agent orchestration. Most developers are using AI coding tools at 10% of their capability.

Not because the tools are limited. Because the inputs are. This handbook exists to help you become a capable participant in that transition.

Read more: https://lnkd.in/eVvj6fqU

LUNARTECH, SeleneX, LaFamilia are hiring!

Data science and data analysis aren’t overrated — they’re just misunderstood. Every now and then you’ll hear someone say, “data science is dead” or “analysis doesn’t matter anymore,” and it usually comes from the same mindset that treats math like it’s only “1 + 1” and treats data like it’s just a messy spreadsheet you pass around.

In reality, data scientists remain in high demand for a simple reason: most people still don’t know how to handle data properly. Clean, reliable data isn’t a “nice to have.” It’s the foundation of good decisions, scalable products, and trustworthy AI. When data is collected and structured well, everything moves faster — teams stop arguing about what’s true, leaders can trust the numbers, and organizations stop bleeding time on confusion and rework.

A strong data scientist brings order to chaos. They make data cleaner, more organized, reproducible, and usable across teams. That discipline shows up in their working style, and senior stakeholders recognize it immediately because it reduces risk and increases execution speed. That’s why good data people keep getting hired — and why they will continue to get hired.

What’s actually happening is that the field is evolving. The bar is rising. Data analysts and data scientists need more skills than before, especially around experimentation, automation, and production-level thinking. But that doesn’t make the role less valuable — it makes the best people in the role even more valuable.

So don’t let the loud noise distort your view. Data science is still one of the most powerful careers for one core reason: it builds a mindset. It trains you to think in fundamentals, to reason clearly with evidence, and to build AI and decision systems on solid ground. That’s not going away — it’s becoming more important.

In the relentless world of technology, the hardest lessons give birth to epic songs.

We’ve learned that firsthand. What started as setbacks forced us to rethink everything. We realized our earlier solutions simply weren’t powerful enough — not in an era where only truly formidable, multi-layered technologies can thrive and deliver on our responsibilities with real impact.

So we went back to the fundamentals.
We studied ourselves.
We studied our users.
We studied the cutting edge of technology.
And we leveled up our skills like never before.

This year, we’re incredibly proud to announce multiple of those new technologies, designed to provide thrust for breakthroughs beyond boundaries.

These aren’t incremental upgrades.

They’re game-changers that will level the playing field, push boundaries to entirely new heights, and let us build things that, just a short while ago, felt straight-up unimaginable.

The power these tools unlock is beyond what most people can picture right now.

Get ready.

The technologies we’ve been working will be dropping this year — and it’s going to be spectacular.

When people analyze the strength of a country, they often look at GDP, military size, or stock market performance. But the real mechanics of a strong system sit deeper—in how capital is structured and how fairly it circulates.

A resilient economic system is not built on one pillar. It is built on an interlocking capital stack: banks, venture capital, private equity, public markets, and the broader investor base. Each plays a different role. Banks provide stability and credit. Venture capital manufactures new companies by funding risk at the earliest stages. Private equity scales, restructures, and optimizes businesses that have already proven something. Public markets distribute ownership and liquidity. Together, they form a capital ecosystem.

Venture capital is the manufacturing layer of innovation. It funds uncertainty. It absorbs early failure. It turns raw ideas into companies. Private equity is the optimization layer. It professionalizes, scales, and restructures. Banks provide leverage and liquidity across the system. Remove one layer—or weaken it significantly—and the entire structure becomes distorted.

You cannot sustainably have a powerful private equity sector without a healthy venture capital pipeline feeding it. You cannot have vibrant startups if early-stage risk capital is missing. And you cannot expect banks alone to manufacture innovation; banks are designed to manage risk, not create it. When a system leans heavily on banking but lacks strong venture capital, you often see fewer breakthrough startups and more concentration around established players.

Strong systems balance these forces. Weak systems tend to have distortions: overreliance on debt, underdeveloped risk capital, or capital captured by narrow interests. When balance erodes, fairness erodes with it. And fairness is not a moral slogan here—it is a structural requirement. If capital allocation becomes predictably unfair or extractive, trust declines, incentives warp, and long-term strength deteriorates. No nation can maintain durable economic power through sustained imbalance or short-term greed. Eventually, misallocation compounds and the system weakens from within.

Look at the most resilient economies and you’ll usually find depth across all layers: a strong venture ecosystem, robust private equity, deep banking infrastructure, and diversified capital sources. That diversity allows value to be created, scaled, financed, and recycled. It creates upward mobility for ideas and capital alike. A distorted capital stack creates fragility.

Sustainable strength comes from balance. And balance requires resisting the temptation to let short-term greed consume the broader architecture. When the capital ecosystem is aligned—fair, competitive, and multi-layered—the system compounds. When it is distorted, it eventually cracks.

Google has long been regarded as one of the world’s most formidable companies. Lately, though, it feels like that reputation is being tested—especially in AI—because the scale of investment and the clarity of results don’t always seem to match.

Here’s what’s striking: the publicly reported capex numbers tied to AI infrastructure (data centers, compute, networking) have become enormous:

2024 capex: ~$52.5B
2025 capex: ~$91.4B
2026 capex (forecast): $175B–$185B

When a company is allocating capital at that magnitude, the expectation is straightforward: breakthrough capability, consistent execution, and clear leadership. If the perceived output feels uneven compared to peers, the disappointment isn’t “hate”—it’s simply the gap between what should be possible and what’s actually shipping.

And this isn’t a “Google-only” pattern. We’re seeing similar whiplash elsewhere.

Take xAI: it entered the public imagination as an “all-in” frontier AI company. Yet recent reporting suggests xAI (alongside SpaceX) is competing in a Pentagon contest focused on voice-controlled autonomous drone swarming software. (Really?)

Even if you’re supportive of defense innovation, it raises a fair question: why divert attention from the core AI roadmap when the product and research race is moving this fast?

It’s a bit like owning a Ferrari and choosing to use it for errands around the block—technically valid, strategically confusing.

What's next in this circus? US using Defense act to order Anduril to ditch drones for the ultimate stealth tech: AI condoms that deploy faster than a Falcon 9, shielding us from humanity's worst?

Armenia is living through a moment that would have sounded impossible in earlier chapters of its history.

At no point in time has there been such a high concentration of technology startups operating under the sovereign ruling power of the Armenian government. For centuries, the systems Armenians built were repeatedly tested—not because of a lack of talent or ambition, but because geography and geopolitics made stability fragile. A small nation positioned at a strategic crossroads was constantly surrounded, contested, and too often treated with hostility by far larger empires. Even when Armenian ingenuity thrived, the environment rarely allowed long, uninterrupted stretches of institutional strength and economic focus.

But something has shifted.

Today, Armenia isn’t just producing talent—it’s retaining it, attracting it, and compounding it. A dense network of founders, engineers, operators, diaspora builders, investors, and technical communities is forming critical mass. When that happens, progress stops being a collection of isolated success stories and starts becoming an ecosystem: startups mentoring startups, second-time founders reinvesting, talent moving between teams, and global standards becoming local habits.

This is what “technological power” looks like in a modern state: not simply infrastructure, not only a handful of companies, but a concentration of capability—people who can build, ship, scale, secure, and sell technology to the world.

And the most important part is this: the moment is happening while Armenia is fully sovereign. The center of gravity is local. The decisions, the institutions, and the long-term vision can be Armenian-led—designed for resilience, for competitiveness, and for continuity.

History shaped Armenia through pressure. The present is shaping Armenia through momentum.

The concentration of technological power has never been greater. The question now is how boldly we choose to build on it—through education, policy, capital, global partnerships, and a culture that celebrates builders.

This is not just a wave. It can be an era.

Unlike many beautiful things in life, long-term thinking gives you exponential returns. Short-term thinking gives you very little.

Many people assume that when a new technology is released, the adoption curve is equal for everyone — that all countries, companies, and individuals benefit at roughly the same rate.

In reality, that’s not how it works.

What truly determines impact is not the technology itself, but the **system behind it** — the infrastructure, the scale, and the ability to operationalize it.

Take China as an example. With around 1.4–1.5 billion people and massive investment in robotics and automation, they have built systems that manufacture at extraordinary speed and scale. Their shipbuilding capacity is among the fastest in the world. When they decide to build something, they build it — quickly and at scale.

Even in AI, despite chip restrictions, they continue training powerful models. Why? Because systems matter more than isolated constraints.

Now look at AI adoption in the United States. Adoption is also strong. But here’s the key point:

If a country with 200+ million people adopts AI at even a small percentage, that can result in millions of deployed AI agents across the economy.

Now compare that to a country with 3 million people. Even if adoption rates are similar percentage-wise, the absolute number of deployed systems will be dramatically lower.

Scale compounds.

So when a technology is open-sourced, it does not mean everyone benefits equally.

The ones who benefit the most are those who already have:

* Systems
* Infrastructure
* Talent pipelines
* Capital
* Operational discipline

Open access does not create equal outcomes. It amplifies existing capability.

And this brings us to something important:

There is a fundamental difference between system builders and schemers.

A system builder:

* Thinks long term
* Invests in infrastructure
* Builds durable mechanisms
* Plays compounding games

A schemer:

* Thinks short term
* Looks for arbitrage
* Exploits temporary gaps
* Plays extraction games

They are fundamentally different.

A system builder never becomes a schemer.
A schemer never becomes a system builder.

Their goals, incentives, and time horizons are completely different.

And in the age of AI, systems win.

There’s this popular idea that success comes from choosing one path and committing to it. But when you look at what actually works in the real world, the pattern is different: hybrids win.

Hybrid cars outperform single-approach engines across use cases. Hybrid UAVs extend range and resilience. And the same logic shows up in how the strongest modern tech companies operate.

Big tech doesn’t grow purely by “deciding” and executing one internal roadmap. They run a hybrid capital strategy:

Venture-style behavior: placing many bets, exploring new products, spinning up new teams, testing emerging tech

Private equity–style behavior: consolidating, integrating, optimizing, cutting waste, scaling what works

That’s why they can accelerate so aggressively. They don’t just build—they acquire. Over time, that becomes a compounding machine: distribution + product + talent + IP + market access, repeated again and again.

And there’s a third force in the background that makes the whole thing even more powerful: banks and credit markets. With financing available at the right scale, the system becomes a three-part engine:

Venture (discovery and options)

Private equity (execution and efficiency)

Banks (leverage and velocity)

That “triquetra” is what lets a single entity behave like an ecosystem—constantly absorbing new capability, strengthening the core, and repeating the cycle. And when you do that well, the outcome is exactly what you see in the markets: not a fragile one-time spike, but a long compounding curve.

You can only tell whether something truly makes sense—and you can only make a good decision—if you have enough data.

At some point, you have to step outside your own perspective, suspend judgment, and genuinely consider another person’s viewpoint. Then you reconnect that viewpoint to the facts. When perspective and evidence align, you can finally tell whether you’re looking at a hallucination—a persuasive but false narrative—or something grounded in how the world actually works.

That’s why data matters. And that’s why data engineering, data analysis, and data science aren’t buzzwords—they’re the practical machinery that turns messy reality into decisions you can defend.

Data is captured reality: events, behaviors, measurements, outcomes. But raw data is rarely decision-ready. It’s often incomplete, noisy, delayed, biased by how it was collected, or missing context. A metric without a clear definition, timeframe, or denominator can create confidence where none is deserved. That’s how people end up “connecting dots” that were never connected in the first place.

Data engineering is what makes data trustworthy and usable. It ensures the same entity means the same thing everywhere, the same metric is computed the same way every time, and the pipeline is stable enough that you’re not rebuilding the truth each week. Without solid engineering, analysis becomes a fragile story built on inconsistent inputs.

Data analysis is where reliable data becomes understanding. It forces you to define the question precisely, choose the right comparisons, and separate signal from coincidence. It helps you see what changed, what stayed stable, and what evidence would actually change your mind.

Data science goes one step further: it makes prediction and testing systematic. It helps you estimate impact, quantify uncertainty, and decide based on expected outcomes rather than anecdotes. Instead of “I feel like this will work,” you move toward “If we do X, we expect Y, and here’s how we’ll measure it.”

When these layers work together, your thinking changes. You stop arguing opinions and start arguing definitions. You stop chasing narratives and start testing claims. You become harder to manipulate, because your confidence is tied to evidence, not emotion.

Think of the world as a library. Some people walk through it once and call it boring. Others learn how to use it—how to extract signal from noise—and they upgrade their ability to decide. Most bad decisions aren’t caused by lack of intelligence; they’re caused by missing data, weak structure, or refusing to do the work.

So the real question is simple: if the tools are everywhere, why do so many people still avoid the library?

People don’t realize this: once a technology reaches a critical core, the game changes.

At that point, it’s no longer about whether development can continue — it’s about how it will continue. The capabilities, tooling, talent, and infrastructure are already in place. As long as people can operate logically, progress doesn’t “stop.” It reroutes, adapts, and often accelerates.

And here’s the part that gets misunderstood:

If your intention (explicit or implicit) is to “box in” AI through pressure-heavy regulation, you might be doing the opposite of what you think you’re doing.
Pressure doesn’t always weaken a system.

Sometimes it compresses it into something denser.

If you build a rigid block around a force that’s trying to expand, you don’t eliminate the force — you increase internal strength. And once it breaks through, it doesn’t come back gently. It comes back with momentum.

That’s why the real conversation shouldn’t be “regulate or don’t regulate.”
It should be:

Are we correctly modeling the action → consequence chain?

Because a lot of policy debates assume a simple relationship:

add rules → slow progress → reduce risk

But once the critical core exists, the relationship often looks more like:
add pressure → concentrate capability → increase asymmetry → escalate outcomes

So yes — governance matters.

But governance built on the wrong assumptions can create the exact future it was trying to prevent.

Actions and consequences are real.

The dangerous part is thinking they work the way we wish they did.

Startups do not grow in isolation. They grow inside systems, and the quality of that system determines the quality of outcomes.

A startup ecosystem is, at its core, an economic structure. When the right inputs are present, activity compounds. When they are missing, growth slows, capital becomes less efficient, talent becomes underutilized, and promising companies struggle to scale.

That is why ecosystem-building should be treated not as a symbolic exercise, but as a matter of financial and institutional design.

If a country wants its startup sector to mature, it needs more than ambitious founders. It needs the mechanics that allow innovation to move from idea to company, from company to revenue, and from revenue to scale. That includes aligned incentives, access to early-stage capital, strong technological capacity, and institutions that reduce friction rather than add to it.

And this is exactly why it is important to recognize that results are already beginning to show.

We are already seeing momentum. We are seeing stronger startup formation, more serious engagement around innovation, increasing attention to technology, and clearer signs that the ecosystem is gaining pace. These developments do not happen randomly. They are the result of people doing the work, building the foundations, and making the right decisions over time.

That deserves recognition.

The people who are helping shape the ecosystem, whether through policy, infrastructure, education, investment, or direct founder support, should be given proper credit. When positive results begin to emerge, it is important not only to analyze them, but also to acknowledge those who helped make them possible. Strong ecosystems are reinforced not only through capital and incentives, but also through credibility, consistency, and recognition of effective contribution.

At the same time, the fundamentals remain unchanged.

Without venture capital, Armenia’s startup ecosystem will continue to face limitations in how far and how quickly it can scale. Without strong technology infrastructure, commercialization pathways, and a sustained focus on reducing friction, momentum will be harder to convert into durable economic value.

So the path forward is clear:
recognize what is working,
give credit to the people and institutions producing results,
continue improving the financial mechanics,
and keep reducing the structural barriers that slow growth.

Because ecosystems scale when good decisions are repeated, when contributors are recognized, and when capital, talent, and infrastructure are aligned around long-term value creation.

Armenia is already showing promising signs. The task now is to build on that momentum intelligently, reward what works, and continue strengthening the system so that progress becomes compounding rather than temporary.

We love the narrative of the visionary founder scaling a company purely on gut instinct. But the reality of business growth isn't a flash of genius—it's data. It’s the ability to run rigorous experiments and operate like a scientist.

This is why data scientists remain your most powerful asset. However, they fall into two distinct camps:

The Task Executors: They pull the requested numbers, deliver the report, and stop there.
The System Thinkers: They see the entire machine. They don't just look at isolated metrics; they map the interconnected nodes to understand how the business actually functions.

To truly scale, you have to abandon rigid, static formulas—the kind of narrow algorithmic thinking that forces you to miss the forest for the trees. Instead, true system builders operate on a dynamic, continuous loop: Action → Result → Prior Data.

When you stop fixating on a single isolated event or a one-off anomaly, you finally see the entire concept. This holistic view is a massive competitive advantage. It ensures you don't waste precious time and resources over-indexing on a specific "node" that ultimately has zero impact on the broader machine.

Too often, founders filter out this broader data to react emotionally to a single event. Growth accelerates when you replace emotional reactions with numerical realities. You have to start asking system-level questions: If I tweak this specific variable, what is the exact downstream effect on the rest of the ecosystem?

It’s fine to have zero systems on day one. Scrappiness gets you off the ground. But the startups that actually survive are the ones that evolve into system builders.

Relying on individual heroics or isolated events will eventually cause you to stagnate. Building systems is what allows you to scale.

This is especially critical as AI disrupts traditional, siloed expertise. If your business relies solely on the isolated "genius" of a few individuals, you are vulnerable. System builders win because they engineer repeatable processes that don't rely on a single point of failure. They scale faster, build bigger, and ultimately have the freedom to do more.

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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);}Lazar Jovanovic's LinkedIn Performance

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