Paarko Global

Paarko Global is a holding company I co-founded, overseeing multiple digital businesses and brands. Starting as a two-person team operating from a small apartment, we’ve achieved remarkable growth and success, building a diverse portfolio of digital ventures.

2014 – 2015: Foundations in Affiliate Marketing
- Launched our journey in direct-response affiliate marketing, promoting offers from platforms like ClickBank, SoftwareHealth, A4D, and MaxBounty.
- Built high-converting sales funnels using tools like Instapage and managed email marketing campaigns via Mailchimp and GetResponse.
- Drove traffic using Bing Ads (now Microsoft Ads), pop ads, and platforms like PopAds.net, RevContent, and 50OnRed.
- Tracked ad performance using tools like Prosper202, ClickMagick, and Voluum.

Gained expertise in marketing strategy development, PPC management, funnel strategy, copywriting, and performance tracking.

Achievements:
Recognized as a Top Affiliate on ClickBank in 2014 and 2015.

2016 – 2019: Transition to Product Creation and E-Commerce
- Shifted from affiliate marketing to creating and selling our own physical and digital products.
- Launched a dropshipping business, sourcing products from China and targeting the US market.
- Generated 8-figures in revenue by building and optimizing sales funnels, mastering product research, competitor analysis, direct-response copywriting, and conversion rate optimization.
- Scaled campaigns using Facebook Ads and Google Ads (GDN, YouTube Ads, Search), spending over $50K/day on ads at peak.

Utilized advanced tools like FB Business Manager, Google Tag Manager, Hotjar, Shopify, ClickFunnels, WordPress, and Klaviyo.

Achievements:
2x ClickFunnels’ 2CommaClub Award winner (2017, 2019).

Co-Founder & Partner

OneLittleWeb

After a tragic incident forced a shift from product-based businesses, I founded OneLittleWeb, a generic SEO agency that evolved into a renowned Digital PR and Link-Building agency.

Key Responsibilities:

Led client acquisition, business development, and high-ticket client relationship management.

Conducted sales calls, closing ~$500K in SEO services personally.

Scaled the team from 6 to 100+ employees, overseeing recruitment, HR, and team management.

Managed crisis situations and provided leadership during rapid growth phases.

Achievements:

Generated 8-figures in service revenue, serving 1,200+ clients with a 4.8/5 service rating.

Secured 2,000+ organic features for clients in top-tier publications like Forbes, Business Insider, NY Post, BuzzFeed, and IMF.

As of 2024, exclusively serving SaaS startups, helping $100M+ companies secure high-quality backlinks.

Key Skills & Expertise:
Digital Marketing: PPC, Funnel Strategy, Copywriting, Conversion Rate Optimization.

E-Commerce: Product Research, Dropshipping, Facebook & Google Ads.

Agency Leadership: Team Building, Recruitment, Crisis Management, High-Ticket Sales.

SEO & Digital PR: Link-Building, Outreach, Client Acquisition, Campaign Management.

Co-Founder

Our 𝙎𝙩𝙖𝙩-𝙇𝙚𝙙 𝘾𝙊𝙉𝙏𝙀𝙉𝙏 attracts media giants, earns backlinks & boosts PR📈 | 🏆 2x 2CommaClub Awardee | 💰 8-Figure in Sales

Tuhin Parvez

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

Here’s where most people are going wrong.

They believe that to succeed in AI, you must train your own models. They think you have to spend billions — or even hundreds of billions — on GPU power, and that this is the only way to create value. In their minds, using AI is less important than building the model itself. They assume that the only way to survive in the AI business is to create your own foundational model.

But that’s not true.

Take cloud companies as an example. They have some of the brightest minds in the world working for them. They have massive funding. They also have access to infrastructure like AWS from Amazon — one of the dominant players in cloud computing since 2006. The amount of knowledge, intellectual property, and talent within AWS is enormous. That level of scale and capability is extremely difficult to replicate.

So when people believe that if they don’t build their own model, what they’re developing won’t matter — that’s exactly where they’re wrong.
Training a model is one thing. Deploying it effectively is something entirely different.

Many highly educated people — even PhDs — struggle with deployment. They may understand theory deeply but lack experience with real-world systems, production environments, or scalable solutions.

Another key issue is the hype.

The hype makes people believe AI is replacing everything — that software engineers are no longer important, DevOps is no longer important, AI engineers are no longer important, and that human thinking itself is becoming irrelevant. 

This leads to all kinds of unrealistic ideas.

In reality, AI is good at specific tasks. But being a strong software engineer means being good at multiple things — systems thinking, architecture, product design, deployment, debugging, performance optimization, and more. That’s where many people struggle. That’s where “riding the dragon” becomes difficult.

You don’t have to be world-class at just one thing. And you don’t have to be great at two things either. But once you master one skill, start mastering a few additional tools. When you build a toolkit of five, six, or even eight strong capabilities, you become extremely powerful in your niche.

There aren’t many people who truly understand software engineering at a high level. There aren’t many who deeply understand marketing. There aren’t many who are genuinely strong at AI engineering — real, production-level AI engineering.

And there are very few people who can combine all of that:
Build full systems
Ship working applications
Solve real pain points
And solve them properly and sustainably
That’s where the opportunity lies.

There are multiple paths to creating value in the AI era. You don’t need to build the next foundational model. You can create immense value by integrating tools, designing workflows, building systems, and solving real problems.

There is still enormous room to build.

Over the years, I’ve had experiences—particularly within accelerators and structured ecosystems—where this value is quietly minimized or left unspoken. These environments are frequently shaped by broader institutional, geopolitical, or organizational constraints. 

As a result, communication can be cautious, curated, and sometimes filtered. Censorship, in itself, is not inherently wrong; there are cases where sensitive data must be handled carefully. However, when most information is treated as something to suppress rather than contextualize, an important truth gets lost.

What is often not stated clearly enough is this: a founder’s capabilities are a real and material asset to the company.

If you are a technical founder, that is not a footnote—it is substantial value. If you have demonstrated the ability to push limits responsibly, solve hard problems, and operate at the edge of what systems allow, that matters. This does not mean recklessly challenging authority or structure. Most of the time, restraint is the right choice. But there are moments where certain solutions demand that boundaries be tested carefully and deliberately. When those moments are navigated successfully, they build credibility.

That credibility compounds. It becomes reputation. People begin to recognize you as someone who can systematically solve problems, handle complexity, and deliver under pressure. Over time, this reputation follows you—from one company to the next, from one venture to the next. And when you start a new company, that accumulated experience should be reflected appropriately.

Founder value does not come from loud promises or polished narratives. Scientists and technologists create value by building things that work. Progress does not come from saying the right words at the right time—it comes from execution. Those who consistently deliver solutions, who understand consequences, and who act with discipline are rare, and they are valuable.

This is where many founders struggle—not because they lack ability, but because they lack frameworks. Many do not fully understand how companies are evaluated, how terms are structured, or how to advocate for themselves responsibly during fundraising. Yet one element should never be overlooked: the founder themselves is a core component of the company’s valuation.

Founders and co-founders are the ones who carry the company through its earliest and most fragile stages—often through multiple funding rounds, long before stability or liquidity exists. Any fair evaluation process must account for this. Ignoring founder value does not make a system disciplined; it makes it incomplete.

At the same time, respect must flow in both directions. Respect for institutions, regulators, and leadership—what one might call “the guy upstairs”—is essential. But self-respect matters too. Valuation, when done properly, is a mutually agreed method that balances opportunity, risk, fundamentals, and human capital.

As a software engineer, being taken seriously requires more than writing correct or performant code. It requires mastery of fundamentals across the entire product surface, and UI/UX is a core part of those fundamentals. Just as a data scientist is expected to understand statistics, data distributions, and modeling tradeoffs, or a security expert is expected to understand threat models and attack surfaces, a competent software engineer must understand how users interact with systems.

The user interface is not cosmetic. It is the primary contract between your system and its users. Regardless of how robust your backend architecture is—whether it uses microservices, distributed systems, or highly optimized algorithms—none of that value matters if users cannot understand, navigate, or effectively operate the product. UI is the execution layer where all technical decisions become visible.

From a technical standpoint, good UI/UX involves far more than visual styling. It includes:
Information architecture: how data and features are structured, grouped, and prioritized.
Interaction design: how state changes, transitions, and user actions are communicated and handled.
Feedback systems: loading states, error messages, confirmations, and affordances that clearly indicate system status.
Accessibility: proper contrast ratios, keyboard navigation, screen-reader compatibility, and semantic markup.
Performance perception: minimizing layout shifts, reducing time-to-interactive, and using skeleton states or optimistic updates to improve perceived speed.
Consistency and predictability: reusable components, design systems, and standardized interaction patterns that reduce cognitive load.

Many developers treat UI as an afterthought—something to “slap together” once the real work is done. This mindset ignores decades of human-computer interaction research and thousands of hours of design iteration that inform modern UX principles. It also creates technical debt. Poor UI decisions often lead to brittle front-end code, unclear state management, confusing data flows, and ultimately higher maintenance costs.

In practice, a well-designed UI directly improves system reliability and user behavior. Clear interfaces reduce invalid input, prevent error states, guide users toward intended workflows, and expose edge cases early. This leads to fewer support requests, more accurate data, and better overall system health.

There is also a professional dimension to UI. Your product is a representation of your engineering judgment. Just as developers care about clean APIs, readable code, and scalable architecture, the UI communicates whether the engineer understands quality, precision, and user-centered thinking.

For these reasons, UI and UX should be treated as first-class engineering concerns. They are not secondary, optional, or purely aesthetic.

When people say a country is “no longer a superpower,” the claim is structural, not emotional. Superpower status in 2026 is not about symbolism. It is about whether a state can consistently convert resources into durable influence across tech, energy, military, economic, technological, financial, and alliance systems at the same time.

Military power alone is not enough. Nuclear weapons deter, but they do not create innovation ecosystems or anchor global supply chains. The countries shaping this era control high-value production layers such as semiconductors, AI infrastructure, and advanced manufacturing. When you lead at the technological frontier, influence compounds. When you fall behind, gaps widen gradually.

Alliances follow the same logic. They are not one-sided arrangements or emotional loyalty pacts; they are long-term risk-sharing agreements. If you want to be a good partner, you must understand it is a two-way street. And for a two-way street to work, you must be competent across multiple fields. Partners need predictability, delivery, institutional stability, and reassurance that cooperation will not expose them to arbitrary risks.

But none of this is random. When a country’s power weakens, it is often because key instruments inside the system are missing. One of the most important instruments is venture capital. Venture capital is, in many ways, the manufacturer of technology companies. Startups power modern technological leadership, and many of today’s dominant firms began as venture-backed startups. Venture capital builds them. Private equity later scales and optimizes them. But first, you need the manufacturer.

If a nation cannot consistently manufacture successful startups at scale, its tech ecosystem will not thrive. Relying purely on large, legacy enterprises or centralized industrial models is rarely enough in a fast-moving innovation economy. The size and strength of a country’s venture capital ecosystem often correlate with its technological and geopolitical power. The United States has the deepest venture capital market in the world, and that depth fuels its startup engine. China has built a powerful venture ecosystem of its own, reinforcing its rise. Regions that have talent but lack risk capital, speed, and policy alignment tend to underperform relative to their potential.

Superpower status is therefore not permanent. It depends on institutional stability, innovation capacity, alliance management, and the ability to finance risk at scale. 

If leaders mismanage (fumble the bag) these foundations, startups relocate, capital migrates, and long-term influence erodes. In the end, superpower status is less about how loudly a state speaks and more about what it can reliably build, fund, scale, and persuade others to build alongside it.

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 we speak openly about AI and its challenges, it can sometimes be misunderstood as resistance to progress. It isn’t. It’s commitment.

We believe deeply in AI — in its power to expand human potential, unlock creativity, and drive extraordinary productivity. And precisely because we believe in it, we are focused on making sure we can truly deliver on its promise.

Real ambition requires real foundations.

Short-term excitement is easy. Headlines are easy. Rapid expansion without reflection is easy. But building systems that transform industries, empower nations, and endure for decades? That requires discipline, integrity, and long-term thinking.

Our approach is simple: if we are going to promise transformative AI, then we must build it in a way that actually works — at scale, sustainably, and responsibly.

There are real structural challenges to address:

Energy capacity and sustainability
Data quality and availability
Intellectual capital and research depth
GPU and semiconductor manufacturing constraints
Infrastructure and edge computing readiness

These are not obstacles. They are the engineering realities of building something meaningful. Facing them directly is not pessimism — it is leadership.

AI is meant to increase productivity and human capability. But acceleration without stability is fragile. We cannot sacrifice long-term strength for short-term optics. Sustainable progress is not slower progress — it is smarter progress.

We are not pulling back from AI. We are investing in its foundations: robust data ecosystems, powerful tools, resilient infrastructure, and diversified energy strategies that ensure reliable scaling. Growth powered by thoughtful systems lasts. Growth powered by shortcuts does not.

Only through sustainable practices can we accomplish what we have set out to accomplish. That is how ambition becomes achievement. That is how vision becomes reality.

We want AI to succeed — not just in theory, not just in the next funding cycle, but in the real world, at global scale, for generations to come.

And the surest way to get there is to build it right.

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

We were discussing real software engineering projects—projects that require an enormous amount of work. AI is great, and we all appreciate its capabilities. However, a critical point is often missed: AI amplifies what already exists. It cannot amplify nothing. If there is no core knowledge, no foundation, AI simply will not work effectively.

AI multiplies your baseline. If you multiply zero by anything, the result is still zero. If you multiply two, you get more. If you multiply one hundred, you get a lot more. The same principle applies to software development. I have seen many people try to build software using AI, but they go nowhere because they lack core knowledge in product development, UI design, and engineering. On the other hand, those who succeed with AI are the ones who already have strong fundamentals. AI enhances their abilities—it does not replace them.

Take a fintech application as an example. First, you must be financially literate. Second, you often need appropriate certifications and regulatory knowledge. Third, you need a deep understanding of the market—something that requires a completely different level of thinking, vision, and education. The amount of real-world knowledge required about finance, markets, regulations, and business is massive. Simply having AI beside you does not magically provide this expertise.

Beyond that, you need access to high-quality datasets. Where do those datasets come from? You need a team. You need legal expertise, especially in regulated industries like finance. You cannot build a serious banking or financial application alone, and you certainly cannot do it without proper legal, compliance, and operational support. At some point, you also need to raise capital.

AI is an incredibly powerful tool, but to produce real, meaningful work, you still need a capable and informed human brain. You need deep domain expertise. You still need DevOps skills, security knowledge, and solid software engineering fundamentals. You still need to recruit people and build teams.
When you put all of this together, AI becomes what it is meant to be: a teammate, not a replacement. Creating a high-quality fintech or financial application involves so much complexity that AI alone cannot solve it.

The idea that the software market is “over” or no longer valuable is simply incorrect. Yes, you can pay $20 or $50 for a subscription, or you can spend $50,000 building software yourself. But in practice, you quickly realize you cannot build everything from scratch, and you end up purchasing licenses, services, and infrastructure anyway.

The claim that AI has eliminated software scarcity does not hold up. There is an abundance of bad software, yes. AI absolutely helps with coding. But it does not replace an entire software startup team. Building something meaningful, reliable, and scalable still requires deep expertise, experience, and investment.

People confuse money with wealth. They treat money as the core, when in reality it’s just a tool. Money doesn’t create value on its own—it only facilitates exchange. The real core of wealth is the thing being exchanged.

Many people misunderstand what it really means to be "rich." We often view money as the ultimate goal or the central hub of wealth, but we forget that money is merely a tool used to facilitate trade. It is a medium of exchange, not the source of value itself.

The true "core" of wealth is the asset—the good being traded or the data behind it. Consider a laptop: the physical materials are secondary to the "data"—the design and intellectual property—that makes it work. If you have the design, you can replicate the machine. If you only have money, but lack the IP, you have nothing.

We see this on a global scale. Nations like Russia or Venezuela may have vast resources or capital, but if they are cut off from trade, that money is useless. Money cannot buy what is not for sale. For example, you can print an infinite amount of currency, but you cannot simply "buy" hypersonic missile technology if you lack the specific talent and data required to build it.

Ultimately, data and skill cast a shadow over currency. Money is great, but it is just a vehicle. Never forget that true power lies in the system, product, the knowledge, and the invaluable assets that money sometimes cannot buy.

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.

The core issue with AI engineering—and with modern software more broadly—is that while AI is impressive and fast, it is still not as powerful as it is often assumed to be.

AI excels at generating software, and because there is now so much software being produced, the perceived value of software has dropped. However, this perception is misleading. High-quality software still requires highly skilled engineers. It requires substantial infrastructure, deep knowledge of system design, and expertise in areas such as DevOps, security, scalability, and operations. These are not trivial concerns, and they cannot be solved by code generation alone.

AI can certainly help write simple code or support small, weekend-level projects. But it cannot realistically compete with large-scale systems—projects that involve multiple layers of complexity, massive datasets, extensive codebases, and years of accumulated engineering decisions. Competing with such systems requires enormous investment, often in the range of millions if not billions of dollars, just to reach a comparable level of reliability and performance. Large team of actual good engineers, be it Data Engineer, Software Engineer, AI Engineer, Machine Learning engineer, Hardware engineers

Because of this, venture capital firms are no longer funding many of these ideas as aggressively. The narrative that software companies are no longer needed, or that software scarcity no longer exists, is simply not true. While basic software may be easier to produce, there is still a significant scarcity of meaningful software—software that delivers real value, enables complex automation, and solves hard, real-world problems at scale.

In short, AI is a powerful in the domains that it has been enabled to do so, but it does not eliminate the need for strong engineering, deep expertise, or substantial investment.

The recent actions taken by French authorities against X (formerly Twitter) and the scrutiny on Google represent a fundamental "reality check" for Big Tech. To understand why, we can look at the original intent of large-scale innovation through the lens of Jaegers vs. Kaijus.

In theory, Jaegers—and by extension, massive tech platforms—were designed by the world’s most brilliant minds to solve "Kaiju-sized" problems: global connectivity, the democratization of information, and human progress. They were meant to be shields for humanity.

Instead, we are seeing these tools repurposed. They aren't being used to battle global crises; they are being used to:

Hammer down competition: Algorithms are increasingly designed to be "anti-combative," systematically suppressing any person, platform, or idea deemed a threat to the corporate ego.

Enforce corporate agendas: Rather than serving the public good, these "Jaegers" are now being used to extract maximum value while offering diminishing returns to the actual users.

A significant part of the frustration stems from how capital is being manipulated. We are witnessing a shift where:

Venture Capital is being swallowed: High-potential startups and genuine research are being starved of funding.

"Black Hole" projects: Billions in taxpayer money and private investment are being funneled into hype-driven projects that serve as tax write-offs or power grabs rather than functional technology.

Economic Sabotage: By concentrating power and sabotaging rivals, these titans ensure that no "good" startup can breathe, effectively creating a stagnation in real-world progress.

The raids on X’s headquarters aren't just about administrative paperwork; they are a symbolic and legal reminder that no one is above the global ecosystem.

The Core Issue: The world is becoming "fed up" with a lopsided deal where tech giants demand everything (data, attention, subsidies) and give back nothing but hype and algorithmic suppression.

Elon Musk, despite his wealth and influence, is ultimately "just another player" in a global theater. If a leader chooses to systematically ignore the law and treat the world as their personal fiefdom, the world will eventually push back. The raids serve as a mirror.

The "raiding of the HQs" is a symptom of a larger rejection of value-extraction culture. If Big Tech continues to use its power to bankrupt taxpayers and stifle innovation rather than solving problems, the legal and social backlash will only intensify.

AI is going to replace some people, but not too many... AI is impressive, but we are amazing as well.

Running large AI models isn’t cheap. I’ve seen cases where teams burned through roughly $50,000 in AI credits in a single month just to build or qualify a product. And that’s not an outlier. Many companies run multiple models continuously, which turns into a permanent cloud bill. In practice, that bill can range anywhere from $50,000 to $2 million per month—just for infrastructure.

What’s often overlooked is that, for the same money, these companies could have hired experienced software engineers and built something more sustainable. Instead of paying people, they’re paying cloud providers. At the end of the day, this is still software development. AI is a tool, not magic—and tools don’t eliminate costs, they just move them around.

The best part is to have both of them, a powerful AI Model and an amazing team connecting with it.

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.

LUNARTECH, SeleneX, LaFamilia are hiring!

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.

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Vahe Aslanyan

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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);}Vahe Aslanyan's LinkedIn Performance

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