PHINIA

Global Commodity Manager IT Software & Software Services

LBR Innovations, LLC

Advisor

• Strategic Investments in Tech and AI: Achieved a 22% ROI across 12+ companies through targeted investments in emerging tech, AI, and innovation-driven start-ups.
• Increased Portfolio Value through Market Forecasting: Conducted market analysis and trend forecasting, enhancing portfolio value by 25% with data-backed insights into high-growth sectors.
• Lifecycle Analysis for Enhanced Returns: Applied lifecycle analysis and reporting to optimize start-up investments, leading to a 25% increase in ROI for technology and digital media ventures.

Investor (Tech, AI, Innovation)

• Boosted Engagement through SEO and Optimization: Increased website traffic and social media engagement by 50% by optimizing 300+ web pages and 12 profiles, leveraging SEO and audience-focused content.
• Delivered High-Quality Content with High Satisfaction: Produced 35+ premium articles and blogs with a 96% client satisfaction rate, increasing repeat business by 15%.
• Refined Content Strategy with Data-Driven Insights: Analyzed performance metrics to optimize content strategies, enhancing user engagement and conversion rates while providing actionable client recommendations.

Digital Copywriter/Ghostwriter

Undisclosed

• Optimized Global IT Vendor Management: Spearheaded vendor management initiatives, achieving a 15% cost reduction through strategic contract consolidation and partnership alignment.
• Enhanced Software Procurement Efficiency: Overhauled procurement processes, aligning strategies with organizational goals to achieve a 20% faster procurement cycle.
• Managed SaaS Budgets and Forecasting: Led SaaS expenditure management with accurate forecasting and financial analysis, reducing overall spending by 10%.

Program Manager, Technology Sourcing

Out of Ink, Inc.

• Led strategic planning initiatives that drove the development of secure, high-performance Web3 components, optimizing team performance and resource allocation.
• Directed annual budgeting, reducing expenses by 10% through strategic cost management and optimized resource allocation.
• Conducted comprehensive program analyses, leading to a 12% overall efficiency and productivity increase.
• Managed developing and deploying innovative Web3 components, ensuring project milestones were met and quality standards upheld.
• Cultivated a collaborative team environment, promoting innovative solutions and continuous improvement.
• Identified key areas for operational improvements, streamlining processes, and leveraging data-driven insights to support strategic decision-making.

Co-Founder and Vice President

tribeOS

Coordinated $60 Million in LOIs: Orchestrated the successful coordination of Letters of Intent (LOIs) totaling $60 million at a major web3 event, significantly enhancing the company's growth trajectory and market presence.
Led Cross-Functional Agile Teams: Led multiple cross-functional teams using Agile and Scrum methodologies to develop cutting-edge advertising tools. This initiative resulted in a 20% increase in user engagement and a revenue increase exceeding 10%.
Developed and Executed Project Plans: Crafted detailed plans incorporating stringent timelines and milestones, reducing project turnaround time by 20%. Achieved a 95% on-time delivery rate, ensuring consistent project success and client satisfaction.
Enhanced Stakeholder Communication: Established robust communication channels with stakeholders, ensuring transparency and alignment throughout project lifecycles. Conducted regular updates and progress reports to maintain stakeholder engagement and trust.
Optimized Resource Allocation: Strategically managed resources to optimize project outcomes, ensuring efficient use of budget and personnel while maintaining high quality and performance standards.

Project Manager, PMO

Investor Relations Support

General Motors

Optimized Multi-Million Dollar Contracts: Successfully optimized contracts with SAS and ETL tools, resulting in a 20% reduction in purchasing costs. Utilized advanced strategic sourcing techniques and negotiation skills to secure highly favorable terms and conditions.
Executed 60+ Cross-Functional Projects: Spearheaded the execution of over 60 cross-functional projects, consistently meeting designated timelines and delivering profitable outcomes. Implemented effective project management methodologies to ensure seamless coordination and successful delivery.
Revitalized Ongoing Contracts: Revitalized existing contracts, achieving a 10%-15% reduction in supplier costs through targeted renegotiation and strategic supplier management. Enhanced supplier diversity, expanding the company’s vendor base and improving procurement processes.
Strategic Supplier Relationship Management: Fostered strong relationships with key suppliers, ensuring long-term collaboration and favorable contract terms. Regularly reviewed supplier performance to identify opportunities for further cost savings and process improvements.
Implemented Cost Optimization Strategies: Developed and implemented cost optimization strategies that resulted in significant savings and enhanced the overall efficiency of the purchasing process. Monitored market trends to make informed purchasing decisions.

Purchasing Project Manager

Achieved 95% User Adoption Rate: Delivered SAP training to 300-1000 users per plant, achieving a 95% adoption rate through tailored and comprehensive training programs.
Enhanced System Performance by 15%: Led SAP landscape management and upgrades, increasing system performance by 15% and decreasing downtime by 20% through strategic optimizations.
Boosted Process Efficiency by 30%: Developed GM SAP procedure documents that boosted process efficiency by 30% and reduced process errors by 50%.
Led Cross-Functional Training Sessions: Organized and conducted cross-functional training sessions and workshops, ensuring thorough understanding and effective use of SAP tools across departments.
Customized Training Materials: Created and tailored training materials, including detailed manuals and guides, to meet the organization's specific needs and enhance user engagement and retention.
Monitored and Evaluated Training Effectiveness: Implemented continuous assessment and feedback mechanisms to refine training programs, ensuring high user competence and satisfaction sustained.

Project Manager: SAP Training & Facilitator

Unicorn Exchange Co

Operations and Content Manager

Ally Financial Inc.

Information Security Analyst

Utilizing data-driven strategies, I specialize in empowering entrepreneurs and solopreneurs to build wealth and achieve financial freedom.

Brian Robertson, MBA

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

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

LUNARTECH, SeleneX, LaFamilia are hiring!

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.

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?

Whether you’re building a startup, running a nation, or evolving as a species, the mechanic is the same: survival belongs to systems that adapt faster than the pressure applied to them.

Most people believe different domains run on different principles. Startups are about innovation. Defense is about power. Biology is about survival. Politics is about ideology.

But underneath all of them, the mechanics are identical.

Every living system exists inside pressure.

In biology, that pressure is environment, predators, disease, resource scarcity. Species that fail to adapt disappear. There is no debate. No moral argument. Just selection.

In defense, pressure is constant—even in “peace.” Intelligence gaps, delayed responses, complacency. The moment vigilance drops, the balance shifts. Deterrence only works while it is credible. Lose that for a moment, and the entire equilibrium can flip.

In startups, pressure comes from burn rate, competition, shifting markets, technology cycles. You don’t lose in a dramatic explosion. You lose in micro-delays. A product update shipped too late. A hiring decision postponed. A competitor underestimated. Momentum is fragile. Once it slows, recovery costs exponentially more energy.

The mechanics are universal:

First, entropy is always working against you. No system stays stable on its own. If energy is not actively applied, decay begins. Code rots. Culture weakens. Security erodes. Cells age.

Second, feedback loops determine survival. The speed at which you sense reality and adjust to it is your real advantage. In biology, that’s evolution. In defense, it’s intelligence and rapid response. In startups, it’s data, iteration, customer feedback. Slow feedback equals blind spots. Blind spots equal collapse.

Third, leverage compounds—but so do mistakes. Small gains, repeated, create dominance. Small lapses, ignored, create failure. There is rarely a single catastrophic event. It’s accumulation.

Fourth, momentum is not permanent. Winning today does not protect tomorrow. In fact, success increases vulnerability because it creates comfort. Comfort slows adaptation. Slowed adaptation invites disruption.

This is why peace and prosperity require constant maintenance. Why companies that dominate markets fall. Why species that thrive for millions of years go extinct. Not because they were weak—but because they stopped adjusting while pressure continued.

There is no “arrival.” Only active equilibrium.

The world is not divided into separate arenas with separate rules. It is one continuous competitive landscape governed by energy, adaptation, and time.

The moment you assume you are safe, you are already behind.

And the systems that survive are not the strongest, the richest, or the smartest.

They are the ones that never stop responding.

The old model is broken. We're building a new one.

In todays world where many leaders of industries talk about AI and automation, they don't talk about the following, which are talents. Do you live to work for your goals, or are you alive and choose to work on your goals? Some people choose education and training as optional goods for their citizens. The academia and workforce get disconnected, and all of a sudden you see tech having way more power, and way more cutting edge tools than Academia. Academia gets lesser and lesser PhD students, and Tech gets more and more. Why is that? Well that is because Tech provides better oppotunities of learning. For example Tech invests hundreds of thousands into education of a single individual in the company, while student need debt to power their studies.

This leads us to the following issue, the growing disconnect between Universities and Tech companies. Universities output certain quality of students, and tech required certan quality of students, and they are growingly not matching one another. Because of this the unemployment of graduates rises, as well as social welfare system strain also rises. Social Welfare is very burdensome for countries, in fact some countries pay 25% of their budget to Social Welfare. This is a big problem.

So basically what this means is the students that university output are not technically proficient or professional. They don't meet the industry standards. Therefore Tech companies have to invest more and more in order to get the people aligned and actually start producing good quality output/work. But many tech companies choose another route, AI automation and Engineering. You can get right now 10 AI Engineer work on the same project. Because of this there is a growing problem with employment. People choose no longer go to universities, simply because it doesn't guarantee them career growth. Tech companies choose not to hire but to automate and engineer, because that is what they need to do to survive as a company.

And so the country experiences talent capital decline (people don't study anymore), brain drain (smart people can't find their place because it gets dominated by other countries, and so they find somewhere else their place, world is multipolar), Debt to cover Social welfare (25% sometimes to just that), as well as growing reliance and outsourcing. And when you start outsourcing your brains and everything, and stop building, you might as well as change the flag of your country and start speaking in another language.

Therefore we have introduced LUNARTECH Fellow program to help those that are in need of guidance, mentorship and getting real experience, and work on real projects with real engineers, in the real world.

In today’s world, industry leaders constantly speak about AI and automation. But they rarely talk about something even more important: talent.

Do you live to work for your goals, or are you alive — and consciously choose to work toward them?

In some countries, education and training are treated as optional goods rather than national priorities. As a result, academia and the workforce have grown increasingly disconnected. What is taught in universities can lag 5–10 years behind what the average technology professional experiences in real-world roles.

At the same time, universities are seeing fewer PhD candidates, while the tech industry continues to grow rapidly. Why?

Because tech companies often provide better learning environments. Many invest hundreds of thousands of dollars into developing a single employee, while students accumulate debt just to access education.

This has created a widening gap between universities and technology companies. Universities produce graduates with a certain level of preparation. Industry demands a different level of technical proficiency and professional readiness. Increasingly, those two standards no longer align.

The consequences are serious.

Graduate underemployment rises. Strain on social welfare systems increases. In some countries, up to 25% of the national budget is allocated to social welfare. That is not sustainable long-term.

When graduates are not technically prepared or professionally aligned with industry standards, companies must invest heavily to bridge the gap before meaningful productivity can begin.

Some tech companies respond by increasing internal training. Others choose a different path: automation and AI engineering. Instead of hiring broadly, they build systems. Instead of scaling teams, they scale technology.

Today, ten highly capable AI engineers can deliver what once required much larger teams. As automation expands, traditional entry-level roles shrink. For many young people, university no longer guarantees career growth — and companies no longer guarantee employment.

This creates a dangerous cycle:
Decline in national talent capital
Brain drain, as skilled individuals relocate to countries with stronger ecosystems
Rising public debt to fund social welfare systems
Increasing outsourcing of critical intellectual capacity

When a country stops building its own talent pipeline and outsources its knowledge base, it risks losing more than economic competitiveness. It risks losing sovereignty over its future.

That is why we introduced the LUNARTECH Fellow Program.

The program is designed for those who seek guidance, mentorship, and real-world experience — working on real projects with real engineers, solving real problems. It bridges the gap between academia and industry, helping individuals develop both technical depth and professional readiness.

The future will not belong to those who merely talk about AI.

It will belong to those who build intellectual capital.

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.

Armenia’s reported acquisition of multiple advanced VTOL unmanned systems of U.S. origin represents a meaningful step forward in modernizing the country’s defense capabilities.

Platforms such as the V-BAT, developed by Shield AI, are among the most innovative unmanned aerial systems available today. Their vertical takeoff and landing capability means they require minimal space to operate — no runway, no large infrastructure footprint — making them especially well-suited for Armenia’s terrain.

The decision by the United States to export powerful autonomous technology to Armenia is a significant and unexpected development. This marks a major milestone, particularly as it follows the recent provision of other exclusive high-performance assets, such as advanced GPUs.

These developments represent a clear victory for Armenia as it seeks to solidify its reputation as the 'Silicon Mountains.'

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The Battle

Brian Robertson, MBA

Vahe Aslanyan

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taplio

The Battle

Brian Robertson, MBA

Vahe Aslanyan

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Engagements

Likes

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

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