Within seconds, my voice left my home in Haifa, traveled through submarine fiber networks to Europe, was processed in a data center, possibly routed through additional vendor clouds across continents, and then made its way back, only to activate a switch sitting 10 inches from my face. The techie nerd in me gets excited every time this happens. But 鈥� I could have just raised my hand and pressed it.

We live in both incredible and absurd times. Our growing tendency to deploy global systems,听across multiple vendors, and continuous compute to solve problems that were already solved locally is something I feel we need to discuss.

To be clear, I am very much in favor of automation and agentic AI. I am educating myself with agentic AI courses to keep up with the times and use the latest capabilities. In many cases, they are transformative to businesses and consumers. Especially at scale, in repetitive processes, in data-heavy environments, or in cases where accessibility matters, AI agents do unlock real value.

But not every problem belongs in that category. And I feel an increasing number of AI-based applications and workflow automations tend to fall in the 鈥渟hower vent鈥� category.

You may think this isn鈥檛 an issue: What does it matter if we bring the tech revolution to solve ridiculous tasks, just because we can?

But there are drawbacks and risks to the automate-everything ethos.

Three risks of automating without discipline

Operational risk: more points of failure, less control: That simple command depends on multiple systems working in sync, your device, your network, Google鈥檚 infrastructure and potentially a third-party vendor cloud.

If any layer fails, the system fails. The same pattern is emerging in agentic AI workflows: multistep pipelines across LLMs, orchestration tools and external APIs. These add dependencies and complexities.

To give another example from my personal life: When my parents got their existing home, they built it as a 鈥渟mart home.鈥� It worked great, until a 鈥渟mart lightswitch鈥� malfunctioned and the smart home company asked for $1,500 to send a special 鈥渟mart home engineer鈥� to fix what would have been a $5 DIY. This is equivalent to hiring AI engineers and automation experts to support a workflow that could have been handled by a junior, nontechnical person in 10 minutes.

And that brings me to the next point.

Economic risk: hidden and compounding costs: Voice commands and AI workflows feel inexpensive at small scale, but they rely on paid infrastructure: compute, API calls, tokens, orchestration layers and vendor integrations.

In many cases, especially at scale, when implemented for those “ridiculous” tasks, the cost of automation can approach, or exceed, the value of the task being automated. We must ensure we invest in AI and automation where it makes economic sense.

Environmental and strategic risk: scaling inefficiency: Data centers create hundreds of millions of tons of CO鈧� emissions annually, estimated to grow to . AI is becoming a growing percentage of that. So these are megatons of CO鈧� emissions, and growing.

While each small agentic AI workflow can account for a few grams of CO鈧� emissions, at scale, these inefficiencies compound into real environmental impact. More importantly, this reflects a strategic issue: optimizing for the sake of it. This mindset can mean we often lose focus on solving meaningful problems.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

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One of their largest customers had asked for a specific new feature which would help their workflow, the kind of request that was clearly valuable to the customer but not necessarily important enough to jump it to the top of the roadmap.

As usually happens in enterprise software, the request needed to move through business and product discussions, design work, engineering prioritization, testing cycles and security reviews before anyone could even commit to a timeline.

The customer understood that. But after waiting for some time, it raised a different possibility: Rather than continue waiting for the vendor to ship the feature, it was considering using AI coding tools internally to build something themselves that would solve the problem well enough.

That single comment reflects a broader shift that SaaS companies are only beginning to fully absorb as their stock prices take a hit, precisely because of this sentiment.

For years, a feature request was a request to the vendor. It entered the backlog, competed with other priorities, and if the customer was important enough or the use case broad enough, eventually it would make its way into the product.

That logic is now starting to weaken. If customers can increasingly generate narrow workflows, lightweight internal tools or customized interfaces on their own, the role of the traditional feature begins to change. And once that happens, it is worth asking a deeper question: Will features even exist in the way the software industry has historically understood them?

Features were once the product

For decades, SaaS companies built value through predefined functionality. A roadmap was essentially a sequence of decisions about which features to build, which customer pain points to prioritize, and how quickly the product team could turn demand into software.

In many categories, feature depth and feature velocity became the core of competitive differentiation. The company that could ship faster, cover more use cases, and respond more effectively to customer requests often had the advantage.

That model made sense in a world where software creation was expensive, slow and highly constrained by engineering capacity. A feature had weight because it represented significant investment. It required planning, development, quality assurance, release management and support. Customers understood that process because there was no real alternative. If they needed something badly enough, they could ask for it, pay for customization, or wait.

AI-assisted development begins to change that equation. When internal teams can describe a workflow and generate a usable version of it in days rather than quarters, the meaning of a feature starts to erode 鈥斕齨ot because functionality is no longer important, but because it no longer has to arrive in the same packaged form.

In some cases, customers may not need the vendor to build every layer of functionality for them. They may only need enough access, flexibility and context to shape part of it themselves.

Functionality may become something dynamic

The real question may not be whether AI will help SaaS companies build features faster, although it clearly will. The more important question is whether the concept of a feature as a fixed unit of product development starts to fade.

For many years, teams gathered requests, translated them into product requirements, scheduled them into roadmaps, and released them as standardized functionality for a broad user base. That process may increasingly look inefficient in a world where software can be generated more dynamically.

In an AI-native environment, the customer may not ask for a feature in the traditional sense at all. They may simply describe the workflow they need, the output they want, the approvals required, the data sources involved, and the rules that should govern the process. The platform could then generate that capability inside the product environment rather than waiting for a formal release cycle. In that scenario, functionality becomes more fluid.

That would represent a meaningful shift in how enterprise software is defined. The feature would no longer be the product鈥檚 smallest strategic building block. Instead, the platform would provide an environment in which functionality can be created, modified and governed with greater flexibility.

This matters because it changes where value sits. If the workflow can be generated on demand, then the defensibility does not lie in the isolated feature itself. Rather, it lies in the system that makes that generation possible in a secure, reliable and scalable way.

The platform becomes the real moat

This is also why AI is unlikely to simply make serious SaaS platforms irrelevant. Even when a workflow can be generated quickly, it still needs to operate inside a much larger enterprise reality. It must connect to structured data, respect access controls, interact with existing systems, produce auditable outputs, comply with security policies, and function with a level of reliability that internal experiments rarely match on their own. These are not minor details. In many enterprise environments, they are the actual product.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

Related reading:

• IPOs Are Holding Up In 2026, But SaaS Debuts Aren鈥檛 Happening
• Why The Future Of SaaS Is Eat Or Be Eaten
• Rethinking Unit Economics: How AI Agents Are Rewriting The SaaS Playbook
• The Rise of VaaS: How AI Is Redefining SaaS Pricing Models

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That world is gone.

Over the past few quarters, markets have quietly flipped from 鈥渞eward any AI headline鈥� to 鈥渟how me the economics.鈥� Not because AI stopped mattering, but because it started costing real money. Annual AI-related capex is now pushing past $600 billion, and investors are no longer debating whether AI is strategic. They are debating whether companies are overfunding it relative to their ability to turn spend into cash.

That shift does not just affect public stocks. It changes how AI companies should be built, financed and exited.

The early signs are out

Look across , and even the – relationship, and you see the same pattern repeating. First come massive commitments, huge infrastructure plans to build capacity well ahead of proven demand. Then comes the uncomfortable question: Are we spending because this makes economic sense or because we fear not to?

Hyperscaler capex for the 鈥淏ig Five鈥� 鈥� , , , and Microsoft 鈥� is projected to reach around $600 billion in 2026, up roughly 36% year on year, with about 75% tied directly to AI infrastructure, which is also heavily funded by debt.

That begs the question: Will these investments be converted into durable cash flows?

Microsoft鈥檚 recent earnings . Capital expenditures jumped roughly two-thirds year on year, exceeding $37 billion in a single quarter, while Azure growth slowed and AI capacity constraints limited upside. The stock fell sharply, losing 21% over the past six months, wiping out hundreds of billions in market value.

Oracle faces a different version of the same issue. Demand for AI cloud infrastructure is real. Cloud revenue is growing around 50% year on year, and GPU-related revenue is surging. But Oracle plans more than $50 billion in capex for fiscal 2026 and expects to raise $45 billion to $50 billion through new debt and equity on top of an already leveraged balance sheet.

Even Nvidia and OpenAI are not immune. The widely publicized idea of a $100 billion Nvidia-backed infrastructure commitment has died down, with Nvidia that no firm commitment was ever made. At the same time, OpenAI has been actively diversifying suppliers, exploring , and others, to reduce over-concentration risk.

If the market is questioning AI overfunding at Microsoft, Oracle and the very center of the AI ecosystem, no one else gets a free pass.

What founders should take from this

For founders building AI companies with exits in mind, the implications are immediate.

• First, your product cannot be a capex sink. Acquirers want assets that make existing AI spend more productive. Lower cost per inference, better GPU utilization, faster deployment or higher revenue per dollar of compute will soon join the traditional SaaS-based unit economics.
• Second, flexibility matters. The Nvidia-OpenAI wobble is a warning. Multi-cloud, multi-model and multi-chip architectures reduce buyer risk and make deals easier to approve internally.
• Third, run your company as if public-market skeptics are already in the room. Clean unit economics after infrastructure costs, sustainable growth strategies and KPIs that matter also in the public markets, as these will be your future acquirers.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

The truth is far more foundational: AI is not a standalone sector. It is an infrastructure layer, just like the internet once was, and it’s being embedded across every industry, from healthcare to agriculture, from fintech to entertainment.

To frame AI as a trend misses the bigger picture. It is not stealing the spotlight from other sectors. It’s powering them.

AI is not a vertical, it’s horizontal infrastructure

Much like we no longer classify “internet companies” as a separate category, AI will soon follow suit. Every industry is already integrating AI into its core operations. So when we see that more than 50% of venture investments are going into AI, it is not a crowding-out effect. It is a signal that AI is a layer within adtech, cybersecurity, education, traditional manufacturing and more.

Calling AI a standalone “sector” oversimplifies its omnipresence.

Valuation discipline is needed, especially at early stage

The excitement has attracted significant capital, but not all startups justify their valuations. Some pre-seed companies with little more than a slide deck have raised at $100 million-plus valuations.

While these terms might look impressive on paper, they are often setting founders up for future funding challenges. As the market corrects and ties valuations more closely to actual commercial traction, companies with no product-market fit may struggle.

That said, some native AI players are building defensible businesses with strong early revenue and the potential to define new categories.

The next wave will address the challenges AI creates

The compute cost of running large-scale AI models is unsustainable. Hyperscalers are spending nearly $700 million per day to keep up with demand. Technologies that can reduce AI鈥檚 CapEx by an order of magnitude will unlock the next phase of profitability and scalability.

Meanwhile, digital trust is eroding as AI accelerates misinformation, deepfakes and identity confusion. Building a layer of digital integrity, particularly for agentic AI, will be essential. Identity verification, content provenance and transparent model disclosure are now mission-critical for the health of the internet.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

Related reading:

• Global Venture Funding In 2025 Surged As Startup Deals And Valuations Set All-Time Records
• The AI Value Chain Has Shifted. Here鈥檚 How Founders Can Still Build A Sustainable Business

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The warning signs were there. Now, years later, the consequences are materializing.

Many of those companies raised at sky-high valuations without ever achieving profitability. As cash reserves dry up, they are facing a harsh reality. Market multiples have contracted significantly, and those inflated valuations from 2021 are now a liability.

The consequences of inflated valuations

• Burning cash without a safety net: Companies that raised during the 2021 frenzy often expected follow-on funding at similar or higher valuations. But when that capital never came, they were left with aggressive burn rates and unsustainable cost structures. Many are now out of cash and scrambling to sell, often for a fraction of what they once claimed they were worth.
• Fire sales are replacing funding rounds: I now meet founders regularly who are exploring M&A not as a strategic exit, but as a last resort. These are not healthy companies looking to grow through partnerships. These are distressed startups trying to recoup whatever value remains. The market has corrected, but their cap tables haven鈥檛. The result is a mismatch between seller expectations and what buyers are willing to pay.
• Unit economics were ignored for growth: In the rush to grow fast and raise bigger rounds, many companies neglected the unit economics basics. Gross margin, CAC payback, dollar retention and profitability were sidelined in favor of valuations and top-line revenue. Now that the market is focused on sustainable growth, companies with weak unit economics are struggling to survive.

The AI wave is showing the same patterns

What worries me is that we are seeing the same dynamic play out today in the AI sector.

Early-stage companies are raising at valuations that assume future dominance, long before product-market fit or revenue. The technology is exciting and the potential is real, but history tells us that not all companies will emerge winners.

When the hype settles, those with sound business models and disciplined financials will remain standing. Others will be left dealing with down rounds, layoffs or worse.

What founders should focus on now

• Raise at a valuation that reflects your business, not the market trend: A modest, well-structured round sets you up for sustainable growth and realistic expectations in future financings. Chasing the highest number on your term sheet may feel good in the short term but often leads to long-term challenges.
• Plan for profitability, not perpetual fundraising: The best companies today are those that have built paths to breakeven. Founders should be laser-focused on extending runway, improving efficiency and demonstrating clear financial discipline.
• Avoid relying on momentum to carry you forward: Momentum helped companies raise easily in 2021. But when market sentiment shifts, only the fundamentals matter. Those who focus on building strong products with clear value and repeatable sales will be in the best position to raise, grow or exit at an attractive valuation.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

Related reading:

• Tech Revenue Multipliers Are Soaring. Should We Be Worried?
• Global Venture Funding And Unicorn Creation In 2021 Shattered All Records
• Don鈥檛 Raise VC Money Before You Understand Unit Economics

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Jack, who runs an AI competing company and is considering an exit, chooses the first route. He鈥檚 out skiing, unplugged, confident that nothing meaningful will happen until mid-January.

Mary, CEO of a competitor to Jack鈥檚 company, is also looking for a buyer, and is taking a different approach. Yes, her team is mostly out of office, year-end reporting is messy, and everyone is juggling personal commitments. But over a series of short check-ins with her banker, she realizes that this quieter season offers something rare: space. Space to think, prepare and get ahead while the market鈥檚 attention is elsewhere.

She doesn鈥檛 cancel her holidays. She simply doesn鈥檛 disappear.

While others wind down, Mary works through the less glamorous parts of preparing for a potential M&A process, the data cleanup, clarifying metrics, shaping the narrative, and starting to map the buyer landscape. Nothing moves quickly, but everything moves forward.

Her banker helps keep things structured, focusing on what鈥檚 realistically achievable rather than forcing deadlines into a holiday calendar. They go step by step, building the foundations that always take longer than founders expect.

When the new year arrives, buyers come back energized. Their inboxes are clear, their attention is reset, and they鈥檙e ready to look at new opportunities. Mary鈥檚 materials are not only ready, they鈥檝e been thoughtfully crafted without the noise of regular workweeks. She鈥檚 positioned to launch conversations immediately.

Jack returns from the mountains to a backlog of emails, an unorganized data trail, and the realization that Mary is already in motion. In a competitive environment, timing and readiness matter as much as performance. While he waited for the race to begin, Mary was already at full speed.

Entrepreneurs often face strategic balancing acts, navigating constraints, timing and judgment under imperfect conditions. December is no different. It doesn鈥檛 require nonstop work, just deliberate engagement.

Jack may have had the better ski trip, Mary may have the better shot at an acquisition.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

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When it came time to sell, one company received multiple bids within weeks. The other struggled to generate interest.

The difference wasn鈥檛 the product. It wasn鈥檛 even the financial profile. It was purely relationships.

In M&A, relationships often matter more than tech. Founders who build visibility, credibility and direct access to buyers from day one end up with faster processes.

Known CEOs generate stronger processes

When a CEO is already on the radar of potential buyers, either from speaking engagements, thought leadership or previous partnerships, there is less friction at every stage. Buyers feel like they already know the person behind the company. The initial outreach is warmer, conversations move faster, and internal buy-in is easier to secure.

What I see is that well-networked CEOs tend to attract interest earlier in a process. Buyers familiar with a founder鈥檚 reputation and strategic vision are more likely to prioritize the opportunity and engage with fewer reservations.

In contrast, companies led by technically strong but lesser-known founders often require a much longer ramp-up. More diligence, more explanation and more effort to convince buyers why this company and this team are worth betting on.

Buyer confidence is built over years, not weeks

Acquirers look at more than just the product and financials. They evaluate leadership, culture fit and long-term integration potential. When a CEO is already viewed as a credible, insightful voice in their domain, that evaluation process becomes easier.

Reputation and access also matter earlier in the funnel. When a CEO has built trust with VCs, industry executives or corp dev leaders over time, they gain strategic optionality. They can engage in early conversations that later mature into real offers.

Start focusing on networking and visibility

Not every founder is naturally inclined to public speaking or personal brand building. Many are product-driven and prefer to stay focused on building. That鈥檚 a legitimate strength, but it comes with tradeoffs.

Networking, visibility and building industry presence are not secondary tasks. For CEOs leading companies that plan to grow, fundraise or explore strategic exits, these are core responsibilities. Founders who invest time in building relationships with investors, potential customers, partners and buyers create long-term strategic value.

That visibility does not have to mean being constantly on stage or posting daily. But there needs to be a deliberate effort to build credibility and access across the ecosystem.

If the founder is not in a position to do that, and has no will to invest in visibility, bringing in a well-known operator is one option.

In the end, company value is shaped not just by tech, but by who is telling the story and who is listening.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

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The company had grown the top line, but not the margin. It was scaling itself out of business.

This kind of story is becoming more common as we move into the AI era. The old SaaS playbook of build a great app, charge monthly and let infrastructure fade into the background, doesn鈥檛 hold up when your core cost scales with usage.

AI has reshuffled the value chain, and for startups, this shift is existential.

The AI stack is deep and margin has moved

In traditional SaaS, most of the value was captured at the application layer. Today, AI companies operate in a much deeper stack:

1. Energy infrastructure: Data centers, cooling and power (see 鈥檚 $10 billion investment in data center energy in Virginia);
2. Chips and hardware: 鈥檚 H100s, TPUs, scarce and expensive;
3. Cloud platforms: Azure, , with priority GPU access;
4. Models: OpenAI, Anthropic and increasingly open-source players;
5. Vertical AI solutions: Can be used as low code/no code platforms to build specific AI applications; and
6. Applications: The user-facing product, where most AI startups still live.

But unlike the past, margins no longer concentrate at the top, close to the end user. They now often sit below the surface, especially in layers where scarcity exists such as hardware, compute and exclusive model access.

So what can startups do when they don鈥檛 own the infrastructure or the models?

Three moves founders can make to stay in the game

1. Own your data. It鈥檚 your new moat

You don鈥檛 need to train your own foundation model, but you do need to own the inputs that make your product valuable.

If you’re in a vertical such as healthcare, finance, real estate or legal, your advantage is proprietary, structured data. Fine-tune open models. Build lightweight adapters. Use your customer workflows to continuously collect differentiated data. The value is in the dataset.

2. Price for usage, not access

That founder’s $800,000 cloud bill happened because they were charging like a SaaS company but operating like a compute company.

In AI, usage drives cost. That means flat-rate subscriptions don鈥檛 work. Founders must embrace pricing models that align value delivered with cost incurred:

• Per-output or per-token billing;
• Compute-aware pricing tiers; and
• Charging for high-cost features such as image generation or live inference.

Track gross margin by feature, not just customer.

3. Avoid model lock-in. Design for flexibility

Tying your roadmap to one model provider like OpenAI or Anthropic is risky. Latency, pricing and policy changes can all blindside you.

Instead, build with model abstraction in mind. Route across providers, fine-tune open-source backups, and negotiate contracts with leverage. Flexibility is not just technical. It is a business hedge.

听 is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

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Fast-forward to today, that same company has rolled out its solution across dozens of U.S. cities and now employs hundreds of people. The very hardware once deemed 鈥渢oo heavy鈥� has become the immovable foundation of its market leadership.

And yet this isn鈥檛 an isolated story. Some of the most valuable companies in the world today, such as and , are fundamentally hardware-driven. Their sky-high valuations stem not just from software, but from controlling the infrastructure that enables others to build.

In the age of AI, when software can be built (and copied) at lightning speed, hardware companies offer something far more durable: presence, permanence and defensibility. Here鈥檚 why I believe it’s time for venture capitalists 鈥� and entrepreneurs 鈥� to rethink their stance on hardware.

Hardware is the new moat

Software is increasingly commoditized. No-code, AI coding assistants and open-source frameworks have narrowed the gap between vision and execution.

In contrast, physical hardware is much harder to replicate or replace once installed. When your device is literally bolted into a city鈥檚 infrastructure, the switching cost is not just technological, it鈥檚 political, logistical and financial. That鈥檚 a moat software alone rarely provides.

Software is still relevant, but it builds on hardware

The misconception is that hardware companies are 鈥渏ust hardware.鈥� In reality, the best ones are platforms. Once deployed, they can continuously upgrade their offering via software, new features, analytics, integrations and even AI layers.

That base unit of hardware becomes your permanent sales rep on the ground, enabling upsells and renewals without reselling the core product.

Bias against hardware is an outdated vestige

Many investors avoid hardware because of legacy scars: high burn, manufacturing delays, complex supply chains. But those assumptions don鈥檛 always hold today. Advances in prototyping, global contract manufacturing and recurring-revenue models have reshaped the economics. When properly structured, a hardware business can achieve healthy margins, strong retention and scalable growth.

I urge founders and VCs alike not to dismiss hardware out of habit, because the next generation of enduring tech giants may be building their moat from silicon, steel and infrastructure.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

Related 附近上门 query:

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So she stopped applying. Instead, Ella tried something else. She built a market trends dashboard using and , automated competitor tracking with , and launched a curated newsletter using . Within three months, she had 2,000 subscribers and inbound interest from multiple companies 鈥� not because of her r茅sum茅, but because of the value she had already created.

Ella鈥檚 story is becoming increasingly common. AI can easily handle many junior-level tasks, but replacing young talent with AI is a strategic risk.

Here鈥檚 why.

No juniors, no leaders

Skipping junior hires today means starving your leadership pipeline tomorrow. AI doesn鈥檛 absorb company culture, doesn鈥檛 sit in customer calls, and doesn鈥檛 develop the judgment that comes from lived experience.

Talented juniors grow into operators who understand your business inside out. Without them, you鈥檒l eventually need to hire from the outside, at a premium, and often without the same level of context or loyalty. Investing in junior talent is investing in your future bench.

Juniors are closer to your future customers

Younger professionals live in the behavior patterns, platforms and consumption habits that are shaping the next generation of buyers. They’re immersed in emerging communities, cultural trends and digital movements.

Senior leadership, no matter how experienced, rarely has the same intuitive pulse. Eliminating juniors increases the risk of losing touch with where your market is actually headed.

You lose the full potential of AI itself

Ironically, it’s often younger employees who are best positioned to push AI forward. They tend to experiment more, adopt faster and find creative use cases beyond the obvious. When you eliminate them, you don鈥檛 just replace task execution, you eliminate the very people who could help you get more from AI than you ever expected. Overreliance on automation limits your company鈥檚 ability to innovate with the tools themselves.

A message to the Ellas: Create value, don鈥檛 wait for permission

If you’re early in your career and being overlooked, don鈥檛 wait for a job title to start contributing. Use your skills to build something useful and visible. Whether it鈥檚 a side project, research product or content platform, showing traction is far more powerful than a polished CV. Companies notice momentum, not intent. When you prove value upfront, the right roles will find you, and on better terms.

It is important to note this is not an 鈥渁nti-AI鈥� article. On the contrary, I strongly recommend all companies should adopt AI 鈥� not as a replacement for people, but rather as a power multiplier for the people you employ and for those you will hire.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

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