Follow the Money:

How finding your actual client requires investigative work

By: Tina Simpson, JD, MSPH, Co-Founder, Line Axia

This is article #3 in our Field Guide for EU Founders series. Check out the first two here: #1 Strategy without Context, and #2 How Context Defines Opportunities 

The United States healthcare system is, first and foremost (and despite significant public funding), a “free” market system.

To illustrate that, lets look at the numbers:

  • Roughly half of Americans get their health coverage through employer-sponsored private insurance.
  • Another twenty percent are covered by Medicare—increasingly administered through private Medicare Advantage plans.
  • Twenty percent receive coverage through Medicaid, the vast majority delivered through privately managed Medicaid MCOs.
  • The remainder are covered through individual market plans or are uninsured.

 

Even programs that are publicly funded flow primarily through private market mechanisms. Medicare Advantage plans manage 54% of Medicare beneficiaries. Medicaid MCOs serve 72% of Medicaid enrollees. Public dollars, private delivery.

This means that to understand how the US healthcare market functions, how purchasing decisions get made, how value is defined, how solutions get adopted—you need to understand it through a payer-driven lens.

In most European systems, a single-national or regional payer sets the rules. Providers navigate one set of regulations, one reimbursement structure, one definition of value.

The US operates differently: dozens of payers, each with their own contracts, metrics, and incentives… and this is true for the small family doctor operating independently to the specialist operating in a health system. This is what we mean by “payer-driven”—not that someone pays for care (that’s universal), but that the fragmentation of who pays fundamentally shapes how the market functions. Different payer types and contracts have different rules, risks, and rewards.

For example, that includes different:

  • Decision-making processes and purchasing authority
  • Reimbursement models and payment mechanisms
  • Regulatory oversight and compliance requirements
  • Quality metrics and performance incentives
  • Definitions of value and return on investment

A U.S. healthcare provider doesn’t manage “patients” in an undifferentiated sense. They manage multiple distinct populations, defined by payer contracts, with varying rules, metrics, and financial incentives.

A primary care practice will simultaneously manage patients covered by three different commercial insurance carriers, traditional Medicare, two different Medicare Advantage plans, their state’s Medicaid program, and various individual market plans. Each represents a different “mini-business” with different contractual obligations, documentation requirements, quality measures, and reimbursement structures.

This means that when evaluating a digital health solution, U.S. healthtech buyers ask a fundamentally different question than their European counterparts. Not simply: “Does this improve care quality or operational efficiency?” but rather: “Does this improve care quality or operational efficiency in a way that aligns with the specific contractual obligations, financial incentives, and quality metrics we are managing across our payer mix?”

A solution that delivers tremendous value for providers managing Medicare Advantage populations may be irrelevant to providers primarily serving commercially insured patients under fee-for-service contracts.

The same clinical problem can represent entirely different business problems, and advantages, depending on the payer context.

But, Operations are Provider-Driven

Following the money tells you where financial pressure and purchasing power live. But it doesn’t tell you the whole story.

Healthcare is ultimately delivered by providers, physicians, nurses, administrators, care coordinators, and the practices, hospitals, and health systems they work within. Clinicians operate within specific organizational structures and clinical workflows. Understanding how care is organized, who controls operational decisions, and what constraints providers face is equally critical.

This creates a fundamental tension in the US market.

The market is structurally payer-driven in terms of money flows and purchasing authority. But it is operationally provider-driven in terms of care delivery and solution adoption.

Financial incentives may flow from payer contracts, but the day-to-day reality of healthcare delivery is controlled by providers managing clinical workflows, patient relationships, and operational constraints.

This means your economic buyer (the person with budget authority and decision-making power) may not be the same person who will use your solution day-to-day. And the person who uses it may not be the person whose operational reality determines whether it gets adopted into actual workflows. (And we return to the complexity created by fragmentation).

This split is where many solutions, especially those built outside the US context, fail.

 

Case Study: DocuAide Enters the US Market

Consider a hypothetical example: DocuAide, a clinical documentation tool developed in Germany.

DocuAide uses ambient AI to capture patient encounters and generate clinical notes, reducing documentation time by roughly 40%. In Germany, the value proposition is straightforward: physicians spend less time on paperwork, more time with patients. Practice efficiency improves. Physicians are happier. The ROI is clear.

An American practice administrator hearing the same pitch asks different questions:

“Does it improve my HCC coding accuracy for my Medicare Advantage contracts?”

“Will it reduce claim denials from Aetna?”

“Can it capture the quality measures I need to report for my MSSP ACO?”

“Does it integrate with my EHR’s revenue cycle module?”

Same product. Same core functionality. Completely different evaluation criteria.

Why the complexity?

Because, again, in the US, that practice doesn’t manage “patients”; it manages business units and contracts. To collect and defend claims paid to a provider for Medicare Advantage patient, the provider needs documentation that captures HCC codes for risk adjustment. That Aetna patient needs prior authorization paperwork. That Medicaid patient’s visit must meet state-specific quality metrics. The same building, same doctors, treating the same conditions, but operating under different requirements.

This workflow problem is shaped by payer requirements.

DocuAide’s founder, Stephanie, might focus on “reducing physician burnout from documentation burden.” This is a real problem. But in the US, physician burnout isn’t the (prioritized) purchasable problem, at least not in most contexts.

The purchasable problem is: “Our Medicare Advantage contract requires accurate HCC coding for risk adjustment, and our physicians are missing diagnoses that cost us $200 per member per month.”

Or: “We’re losing $50,000 a month to claim denials because documentation doesn’t support medical necessity for the commercial payers.”

Or: “We can’t take on more value-based contracts because our current documentation doesn’t capture the quality measures required for performance bonuses.”

The value proposition, therefore, must be framed by payer contract requirements

In addition to this, American clinicians have learned to be skeptical. They’ve lived through waves of EHR implementations that promised to reduce their workload but were actually built to serve revenue cycle requirements. The result is deep skepticism toward “solutions” that don’t clearly address their specific workflow pain points, while also delivering on the financial and compliance requirements that drive institutional purchasing decisions.

The value proposition, therefore, must align with the provider’s operational realities.

DocuAide needs to ask:

  • Which payer types does the target practice serve? (Commercial insurance? Medicare? Medicaid? A mix?)
  • What reimbursement model governs each contract? (Fee-for-service? Capitation? Value-based arrangements?)
  • What specific quality metrics or documentation requirements are contractually mandated? (These vary by payer and by contract)
  • How does documentation quality affect the practice’s financial performance under each contract?

The same practice managing commercially insured patients under fee-for-service contracts has different documentation priorities, perhaps focused more on coding specificity to maximize reimbursement per visit, or on supporting prior authorization requirements for certain procedures or medications.

DocuAide’s leadership might assume that if clinicians love DocuAide, adoption will follow. But in the US, clinicians rarely control purchasing decisions.

Budget authority lives with:

  • Practice administrators or CFOs (in independent practices)
  • Health system IT departments and finance teams (in hospital-owned practices)
  • Revenue cycle management leadership
  • Population health management teams (for practices taking on risk-based contracts)

These decision-makers evaluate solutions based on measurable ROI: Will this tool increase reimbursement? Reduce claim denials? Improve performance on value-based contracts? Reduce compliance risk? Enable the practice to take on more patients without adding staff?

The decision for adoption, therefore, is not about the actual effectiveness of the product—it’s about measurable financial impact within specific payer contexts.

What This Means for DocuAide’s Market Entry

Before DocuAide can develop a go-to-market strategy, it needs to:

  1. Define the target payer segment(s): Which types of payer contracts create the most urgent need for improved documentation? Where does DocuAide’s functionality align with contractual requirements and financial incentives?
  2. Identify the economic buyer: Who controls budget for this category of problem? What metrics do they use to evaluate ROI?
  3. Map the decision-making unit: Beyond the economic buyer, who influences the purchase decision? (Clinicians, IT, compliance, revenue cycle—each will have evaluation criteria)
  4. Reframe the value proposition: How does DocuAide support the practice’s ability to succeed under specific payer contracts? What measurable financial or operational outcomes can be demonstrated?
  5. Understand workflow integration requirements: Different payer contracts often require different documentation elements, coding specificity, and quality measure reporting. Can DocuAide adapt to these varying requirements within a single practice?

Without this foundational understanding, DocuAide risks positioning a solution to a problem that, while real, will have no adoption success in a US market.

The Takeaway

The U.S. healthcare market is not a single system with a single definition of value. It is a collection of thousands of overlapping markets, each organized around different payer types, each with different incentives, metrics, and purchasing dynamics.

For EU founders, this requires a fundamental shift in approach.

It is not enough that your solution “does a thing”—even if that thing demonstrably improves clinical outcomes or operational efficiency.

You must understand how your solution fits within the specific financial and operational context of a defined market segment, organized by payer type.

This means being able to answer:

  • Which specific payer contracts does your solution support?
  • How does it increase revenue or reduce costs within those contracts?
  • How does it fit within existing clinical and operational workflows shaped by those payer requirements?
  • Who has budget authority to purchase solutions in this category, and what metrics do they use to evaluate ROI?

The complexity and fragmentation of the U.S. market can feel overwhelming. But this fragmentation also creates real opportunity. Different payer segments have different unmet needs, different levels of competitive intensity, and different willingness to adopt innovation.

The US market rewards this level   of focus—and punishes companies that try to sell  everything to everyone. Expert guidance is a necessity, not a luxury.

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If this article resonated with you—or if you found yourself thinking “wait, that’s exactly the conversation we need to have internally”—let’s talk. Line Axia works with European digital health companies navigating U.S. market entry, helping founders recognize blind spots before they become expensive mistakes.

As always, this post was written by a human being! Not AI. ChatGPT was used for final grammar edits and spellcheck.

 

The Deep Tech Paradox

Why the most important innovations are the hardest to fund

 

By: Olivia Arechiga, Co-Founder, Line Axia

 

AI Disclamer – As always, this blog was written by a human, me! ChatGPT helped me edit.

 

“DeepTech” has long been a favorite buzzword in venture capital circles, promising the next frontier of innovation, from quantum computing to advanced materials and synthetic biology.

Yet, when you talk to investors, many will tell you the same thing: they love the idea!… but it is too risky to invest.

@Tina and I saw this first hand during our time at the Sifted Summit in London this past October.

Fundamentally, DeepTech sits at a strange intersection.

It represents both bold and undeniably innovative visions for the future, and at the same time requires immense amounts of capital, over long periods of time. And often times, it ends up being not viable. It’s hugely risky, in other words.

Understanding, and bravely working within that quagmire, is a requisite if you want to build, invest in, or lead a DeepTech company that actually survives the gap between science and market.

 

First, Let’s Define DeepTech

I asked this question to a small group of EU DeepTech founders during a roundtable discussion at the London Sifted Summit: What is the actual definition of “DeepTech”? Is there one? Does it vary by industry or country?

No one had an answer. In fact, this question was met with concrete silence. No one had any definition of their own to provide. I jumped in and suggested it was something like – “You know it when you see it?”

Some may disagree, and no doubt this is a broad definition, but for the sake of brevity, we’ll define it as: technology rooted in scientific or engineering breakthroughs.

The Singapore Global Centre wrote a good definition: “

Deep tech refers to the cutting-edge and often disruptive technologies that are built on profound scientific discoveries, engineering innovations, or advancements in research areas that have the potential to radically transform industries, economies, and lives.”

Unlike SaaS or consumer tech, DeepTech companies aren’t selling an efficiency or an AI chat-bot; they’re really selling possibilities beyond our current reality.

Whether or not you can articulate this possibility with concrete requirements, steps, and most importantly, a timeline, will determine whether your DeepTech becomes a darling of daring investors, or if it dies in an under-funded lab.

 

Why Investors Want DeepTech:

  • Massive Potential Upside
    DeepTech companies are often trying to solve big problems, like climate change and healthcare, which means the upside is outsized. If it works, it can reshape entire sectors. It can literally, change the world.
  • Defensibility and IP
    The moat is in the science. DeepTech ventures will build patent portfolios or proprietary engineering processes that are years ahead of actual replication or deployment.
  • Portfolio Differentiation
    VC’s interested in this type of risk want their investment to be high impact and high return– which is typically what you get when you invest in DeepTech and it takes off.
  • Impact and Narrative
    DeepTech is attractive when it’s an impact initiative – helping humanity and solving big problems makes a VC look good. Good headlines bring more opportunity for the VC.

Conversely…

Why Investors Don’t Want DeepTech:

  • Technical Risk
    The science can fail. And often does, sometimes spectacularly. (Think the Theranos story).
  • Time-to-Market
    While a SaaS startup can iterate and pivot in months, DeepTech ventures can take 10+ years (or longer) to reach a minimum product or a marketable result.
  • Capital, Capital, & More Capital
    Prototyping, manufacturing, data analysis, and creating regulatory pathways require incredible amounts of capital. The same components that make it defensible, also makes it incredibly expensive.
  • No Clear Exit Sign
    Many DeepTech ventures have uncertain (or non-existent) IPO/capital return paths. The “exit math” doesn’t traditionally fit standard VC time or investment return horizons.
  • Operational Complexity
    DeepTech teams need to align scientists, engineers, regulators, and business operators which demands an extremely high level of governance, expertise and oversight that most early-stage companies don’t need.

Here’s a quick real-world comparison of funding a non-DeepTech initiative by VC’s, compared to a DeepTech:

In June 2019, Commonwealth Fusion Systems (CFS) closed a $115M Series A to commercialize fusion energy. That number is already a tell: in DeepTech, “Series A” is often not “fuel to scale”  (like it is in software, by comparison), it’s fuel to survive the middle: pilots, first-of-a-kind engineering, and a regulatory path that sometimes is being built at the same time.

CFS itself notes “ARC” (its first grid-scale fusion power plant) as arriving in the early 2030s. In other words, even after a nine-figure “A,” you’re still staring down a decade-plus gap between promise and market reality.

Now, put that next to a typical software timeline.

Notion was founded in 2013 and first released to the public in 2016 (roughly a three-year path to a shippable product). And when Notion raised $10M in 2019, it was widely described as an “angel round”  i.e., capital coming in when the product already existed and the business case was legible.

This is absolutely not an apples to apples comparison, but that is the point.

In software, a “Series A” often comes after the market has already confirmed demand. In DeepTech, a “Series A” can show up when you’ve proven something in a lab, but the hardest part is still ahead: building the thing in the real world, under real constraints.

Even the “normal” baseline makes CFS stand out: PitchBook’s 1Q 2019 Venture Monitor put the median early-stage VC financing at $8.2M. CFS wasn’t just raising “more.” It was raising a different category of money for a different category of timeline; and then being judged by investors who are structurally optimized not to thrive through that ugly middle bit.

The paradox then, is pretty obvious.

The same traits that make DeepTech valuable, make it nearly uninvestable.

It’s desired because it’s visionary, defensible, and impactful.
It’s avoided because it’s slow, uncertain, and illiquid.

Many investors want to be associated with DeepTech, but not live through the difficult middle.

I’m sure we’ve all heard the (slightly inappropriate) analogy – “spare me the labor pains, just bring me the baby”.

 

Where DeepTech Investment Actually Breaks Down

The failure point is in translation – between the research and business. This was obvious in the convo at the Sifted Summit with DeepTech founders; my question should have been answerable, if only to be a plug for the founders own DeepTech projects.

A team can build groundbreaking, life-changing tech, but will fail to:

  • Define measurable milestones (this is much harder than it sounds)
    • How do you define milestones for something that doesn’t exist yet, and you don’t know if, when, or how it will exist?
  • Build governance and timeline structures that can withstand funding cycles
    • Again, building governance for something that has yet to be governed or regulated – where do you start?
  • Translate scientific proof into commercial readiness
    • How to make a technological advancement / scientific breakthrough, marketable? Results is the obvious answer, but what if results are decades away?

 

Conclusion

DeepTech is, across the board, worth investing in. The ambition though, requires a longer fuse. We’re talking about true world transformation in some cases – and the patience for this demands more bravery, grit, and discipline from founders and VC’s alike.

DeepTech founders however, need to learn to sell, plain and simple. Marketing without a market plan, without concrete metrics for investor return, pivot points, and governance parameters are all big red flags with VC’s. Most venture investors are optimized for software-style risk/reward: low capital requirements, fast iteration, and relatively short time-to-market.

DeepTech needs to do a better job at making risk more quantifiable, and make the technology more analogous commercially.

Firstly, if every R&D milestone is not directly tied to a business KPI (e.g. cost per unit, performance metric), you’re missing an opportunity as a DeepTech founder.

Secondly (and this one is not going to be very popular), but move away from the story-book “making a better world”. Don’t abandon it outright- but don’t rely on this narrative. Build investor decks that translate science into economics and strategic moat – not a story about saving the world.

As much as “saving the world” speaks to me personally, it’s not going to be the line that moves the investor from a maybe to a yes.

Keeping business mechanisms at the forefront of DeepTech may seem a bit icky, but it’s the only way DeepTech makes it out of the vision stage, and into the hands of those who really need it; which is all of us on this planet who need big breakthroughs in health, science, climate and energy, now more than ever.

 

How Context Defines Opportunity:

Fragmentation and the US Healthcare Market

#2 In Our Field Guide for EU Digital Health Founders
By Tina Simpson, JD, MSPH

 

Before the break for the holidays last month, we opened this series with a reflection on how many EU founders misread US market dynamics, misunderstanding not only demand and opportunity, but the structural forces that shape adoption, scale, and success.

They rely, understandably, on the mental models and assumptions of their home countries. Many EU and international digital health founders approach the US as if it were a single healthcare system: large, complex, and imperfect, but ultimately coherent.

This assumption is understandable.

There is one FDA. National programs like Medicare and Medicaid exist. Major hospital brands operate across multiple states. The infrastructure suggests centralization. This leads founders to make several miscalculations and mistaken assumptions.

For example, they assume:

  • the only (or best) path into the US market is as a clinical FDA-regulated intervention.
  • there is a meaningful nexus between regulatory review and approval and market adoption.
  • regulatory approval is “the hard part” and underestimate the commercial and operational challenges of bringing an innovation into a clinical practice.

But as we discussed previously, the U.S. healthcare “system” is better understood as as an ecosystem than as a centrally designed system. It’s Jurassic Park, not We Bought a Zoo. Each requires different skills, strategies, and tools.

 

I use the term ‘ecosystem’ intentionally; and I want to pause and ensure that this Jurrasic-tinged theme hits home before we proceed further. Classifying it as an ecosystem captures the unexpected diversity and adaptation to local conditions that emerges in the absence of centralized authority and intentional design. In the absence of that centralized authority and vision, there is no external force smoothing out variation, aligning priorities, or reallocating resources toward a shared objective. Variation is not mitigated, but amplified.  

Consequently, in in the US ecosystem, local conditions exert disproportionate influence over what gets adopted, funded, scaled or abandoned. What thrives is not what is universally optimal, but what is well-adapted to its specific environment; or, more accurately, what is perceived as the most urgent issues for local decision makers. What succeeds in California may fail in Texas. Resources flow differently. Competitive pressures differ. Regulatory and political constraints diverge.

This is the crux of why the distinction between a system and an ecosystem matters.  Because there is no centralized organizing force fragmentation and localization are not temporary features to be engineered away. They are defining characteristics of the U.S. healthcare market, and they are the first things that a founder must wrestle with when considering market entry.

What follows in this article (the second in our Field Guide) is an exploration of the practical consequences of that reality.

If the U.S. healthcare market is an ecosystem rather than a system, how does that shape the structure of the market itself? How does fragmentation manifest in practice? And why does this variation, while often experienced as a barrier, also create opportunity for founders who understand how to navigate it?

In this installment, we continue to challenge prevailing mental models by examining how:

  • The U.S. healthcare market is not a monolith, but a network of overlapping regional markets and systems;
  • Fragmentation is a structural feature of the environment, not a bug; and
  • This fragmentation creates both real barriers and strategic openings for the savvy navigator.

Let’s get started.

The Illusion of (Immediate) Scale

For many EU founders, the immediate appeal of the U.S. market is its scale: one regulatory checkpoint theoretically provides access to over 330 million people. This vision of access to a single massive market is tantalizing. It is also misleading.

This is because the U.S. healthcare market is not a monolith. I want to pause here a moment, because it can be tempting to glibly pass over that statement. I mean, of course the U.S. healthcare market isn’t a monolith (who said it was?)– the US itself isn’t a monolith. But what I want you to appreciate is the diversity and fragmentation that exists because there isn’t a centralized system setting the agenda, and allocating resources. Once again, the US healthcare market is an ecosystem – not a system.  It is a patchwork of competing actors, rules, and incentives, knitted together by shared interests, history, and federal funding. Fragmentation is a profound, pervasive and protected feature of this environment.

It is not simply and accidental or bothersome byproduct. Therefore, the more accurate, and more actionable, way to think about the United States is not as one market, but as a loose confederation of (at least) fifty distinct regional markets, each with its own stakeholders, infrastructure, regulatory environment, and healthcare needs.

Medicaid: A Case Study in Fragmentation and Diversity

To illustrate just how fundamental fragmentation is in the US healthcare “system”, it is useful to start with Medicaid.

The choice of Medicaid (as opposed to private insurance – which actually dominates the US landscape) as a case study is intentional. Of all the U.S. health programs, Medicaid most closely resembles the centralized, government-administered health systems familiar to European founders. It was created by federal legislation, is publicly funded, targets a defined population, and is overseen by a federal agency. On paper, it looks like the kind of national program that would impose uniformity across the system.

It does not.

Instead, Medicaid is fifty different programs under one statute. And, as commonly cited by Medicaid operators, and regulators: “If you know one Medicaid program, you know one Medicaid program.

Let’s back up: Medicaid is a public program created by federal legislation in 1965, funded by government (both federal and state contributions), and designed to provide comprehensive coverage to a specific population (in this case, low-income Americans). It’s administered by a federal agency (the Centers for Medicare & Medicaid Services) responsible for establishing baseline program requirements and conducting oversight of the states’ administration and performance. On paper, it looks like a national program with consistent standards and centralized administration. In practice, it demonstrates exactly why thinking of the “the US Medicaid Market” as “one market” is fundamentally misleading.

As a jointly administered (and funded) federal-state partnership, states have primary authority for the design and administration of their individual state Medicaid programs. The result is that there are more than fifty different Medicaid programs (one for each state, plus territories and the District of Columbia). These programs differ significantly across multiple dimensions:

Eligibility criteria: Who qualifies for coverage varies significantly by state.

Some states have expanded Medicaid under the Affordable Care Act to cover adults earning up to 138% of the federal poverty level; others restrict eligibility restricted to only low-income children, pregnant women, and the disabled.

Covered Services: The types of medical services covered beyond federal minimums vary substantially.

Some states cover dental and vision for adults, others don’t. Behavioral health benefits, transportation services, and coverage for emerging technologies like telehealth or remote patient monitoring, differ wildly.

Delivery models: Most states rely heavily on managed care organizations (private insurers contracted to manage Medicaid populations), but some states directly administer the programs within a more fee-for-service infrastructure.

The number of managed care plans, their market share, and their sophistication varies dramatically. California has a dozen major Medicaid managed care plans with sophisticated value-based care capabilities; Wyoming has a predominantly fee-for-service model with limited managed care penetration.

Reimbursement rates: What providers are paid for the same service varies across states, affecting which providers participate in Medicaid and their capacity (or willingness) adopt new technologies or care models.

Tailored Innovation: States rely on federal waivers to test new payment models, delivery approaches, or coverage expansions.

Some states are actively experimenting with value-based care, social determinants of health interventions, or technology-enabled care delivery. Others maintain more traditional fee-for-service approaches with limited innovation initiatives.

This means that a digital health solution serving Medicaid beneficiaries in California may be irrelevant or even incompatible with the Medicaid program in Texas or Florida – even though the patients may share the exact same need.

Beyond Medicaid

The state-level variation extends beyond Medicaid:

Commercial insurance is regulated at the state level. State insurance departments approve plan designs, set rate review processes, and establish consumer protection requirements.

What constitutes an allowable benefit design, how plans can be marketed, and what consumer protections exist vary by state.

Provider licensing and scope of practice regulations vary by state. What services nurse practitioners can provide independently, whether physicians can practice telemedicine across state lines without additional licensing, what mental health professionals can prescribe, all of this varies.

A digital health solution that relies on a particular care delivery model (nurse practitioners providing primary care, or licensed clinical social workers managing behavioral health) may not be viable in all states without substantial modifications to workflows.

Telehealth policies vary by state. Reimbursement parity (whether insurers must pay the same for telehealth as in-person visits), what modalities are reimbursable (live video, store-and-forward, remote patient monitoring), and what settings qualify all vary.

A telehealth-enabled solution may have strong reimbursement support in one state and face significant barriers (or indifference) in another.

Even Medicare (a wholly federal program with nationally standardized eligibility, benefits, and payment rules) exhibits meaningful regional variation, with variation in implementation, coverage interpretation, and market dynamics.

What this Means When Developing a Market Entry Strategy

For EU founders, this reframing from “the U.S. market” to “U.S. markets” has several critical implications.

Let’s start with the good news: by recognizing and respecting market fragmentation and diversity, market entry becomes more manageable. Instead of needing a strategy to penetrate a 330-million-person market, you are forced to focus on specific geographic markets where your solution has the strongest product-market fit, where you have relationships or local knowledge, or where regulatory and market conditions are most favorable.

You don’t need to “enter the U.S. market.” You need to succeed in small subsections: Ohio, or the Mid-Atlantic region, or among specific safety-net providers in major metropolitan areas.

This is a fundamentally different strategic position, and it requires different analysis. It means asking:

  • Which states have the regulatory environment that supports (or at least doesn’t inhibit) your solution?
  • Which have the payer mix that aligns with your value proposition?
  • Which have provider networks or health systems (or lack thereof) that present natural entry points?

Variation creates opportunity—if you choose strategically. Different states are at different stages of healthcare transformation, have different budget constraints, and different appetites for innovation. A state struggling with a particular challenge—rural access, maternal health outcomes, chronic disease management, opioid epidemic response—may be actively seeking solutions and willing to pilot new approaches.

Massachusetts (Boston-area, most notably), for example, has been a consistent leader in healthcare innovation, with a sophisticated payer landscape, strong academic medical centers, and a history of piloting new payment and delivery models. The same goes for Washington State, which has the longest history experimenting in population health and managed care models. Both states are actively testing solutions that better integrate behavioral health, as well as integrating social determinants of health.

On the other end of the spectrum, Mississippi has a less sophisticated payer landscape and agenda, but as it faces profound rural access challenges, bu is likely to be more  receptive to solutions that address fundamental access gaps or enable specialists to operate remotely from hub facilities.

Understanding which states have the political will, regulatory flexibility, and budget capacity to support your type of solution becomes the key part of market selection.

This isn’t about finding the “best” state—it’s about finding the right state for your specific solution at your specific stage of development. Are you targeting early adopters with sophisticated infrastructure, or are you solving fundamental access problems in under-served markets? Different states represent different strategic opportunities.

Proof points are portable, but expansion requires adaptation. Success in one state creates a case study and proof of concept that can be leveraged elsewhere.

Demonstrating impact with California Medicaid creates credibility when approaching New York or Illinois. But expansion to other states is genuine expansion and not scale – it requires understanding local market dynamics, building new relationships with different payers and providers, and often adapting the product or business model to local regulatory and operational requirements.

In short: this is not “land and expand” in the traditional agile-software sense, where success with one customer creates a reference and the product scales horizontally with minimal adaptation. This is market-by-market expansion, each requiring its own go-to-market strategy, partnership development, stakeholder engagement, and often product adaptation. Your success in one market proves your capability and creates a foundation, but it doesn’t automatically unlock neighboring markets.

Resource allocation decisions become clearer. When you think of the US as one market, the resource requirements feel overwhelming: you need a national sales team, relationships with national payers, presence across the country. However, when you think of the US as fifty markets, you can make deliberate choices about where to focus limited resources and develop the relationships (and generate proof of impact) where it matters most.

You might choose to focus on three target states in your first 18 months, building deep expertise in those markets, establishing strong customer relationships and proof points, and achieving product-market fit before expanding. This approach is both more reflective of the realities and demands of the US system, while also being a much more realistic and capital-efficient path than trying to achieve national scale from day one.

So how do you choose which markets to target? Consider analyzing several dimensions:

  • Regulatory environment: Which states have the licensing, scope of practice, telehealth, and insurance regulations that enable (or at least don’t prohibit) your solution? Some states are regulatory leaders that welcome innovation; others maintain more restrictive approaches.
  • Payer landscape: Which states have the payer mix (Medicaid expansion status, managed care penetration, commercial market structure, Medicare Advantage presence) that aligns with your value proposition? If your solution is designed for value-based care arrangements, you need markets with sophisticated managed care infrastructure.
  • Provider infrastructure: Where are your target customers (health systems, independent practices, FQHCs, specialty providers) concentrated? What is their level of technological sophistication? What is their capacity to adopt new solutions?
  • Market need: Which states are struggling with the specific problem you solve? Where is the pain point most acute? Where does your solution address a recognized state priority (rural access, maternal health, chronic disease, behavioral health)?
  • Competitive dynamics: How saturated is the market for your category of solution? Are there established competitors, or is this relatively white space? Where do you have differentiation?
  • Your capabilities: Where do you have existing relationships, local knowledge, or operational presence? Which markets can you realistically serve given your current team, capital, and go-to-market capacity?

The goal isn’t to find the “best” state in absolute terms, but to identify the first beachhead.

You want a market where you can gain traction, prove impact, build relationships, and establish proof points that can be leveraged for expansion. Understanding that the U.S. represents fifty+ distinct markets rather than one monolithic system is the first critical reframing for EU founders. It makes market entry more manageable, turns fragmentation from an obstacle into a strategic opportunity, and enables more focused and efficient resource allocation.

This reframing shifts the question from “How do we enter the US market?” to “Which US markets should we enter, and in what sequence?” It means being deliberate about market selection, realistic about the resources required for expansion, and strategic about building proof points that can be leveraged over time.

Knowing you’re targeting “California” or “the Mid-Atlantic region” is only the beginning.

To actually succeed in those markets, you need to understand how they’re organized operationally which means understanding the payer-driven dynamics that govern how care is purchased, delivered, and valued. Different payer types have different incentives, purchasing processes, quality metrics, and definitions of value. And any successful digital tool needs to adapt to those dynamics. That’s what we’ll explore in the next article.

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About the Author Tina Simpson is a healthcare strategist and co-founder of Line Axia, a consultancy that helps European digital health companies navigate U.S. market entry. Having worked on both sides of the Atlantic, she specializes in translating across healthcare ecosystems, 90s adventure films, and regulatory jargon.

…

 

If this article resonated with you—or if you found yourself thinking “wait, that’s exactly the conversation we need to have internally”—let’s talk. Line Axia works with European digital health companies navigating U.S. market entry, helping founders recognize blind spots before they become expensive mistakes.

As always, this post was written by a human being! Not AI. ChatGPT was used for final grammar edits and spellcheck.

#1 Strategy Without Context: How EU HealthTech Founders Misread the U.S. Market

Earlier this year, I met a friend for lunch in London. He had recently exited a leadership role at a successful digital health startup, one that had built a loyal user base, secured NHS contracting, and by all European measures, succeeded. But the company had plateaued. It wasn’t going to expand beyond its domestic market, and for a venture-backed startup, that was a problem.

Over fancy fish and chips, we exchanged notes. I was curious about his perspective; I wanted to understand the European founder experience, particularly when it came to evaluating U.S. market entry. He shared his reflections on building in Europe; I offered some (admittedly unsolicited) armchair analysis of how his former company might have approached the U.S. market differently.

Two important things from that conversation have stayed with me:

First: The US is on every market expansion roadmap – but most maps lack a key or legend.

For EU and UK digital health founders, U.S. market entry isn’t just an option; it’s baked into the funding trajectory and expectations from the start. It might not be explicitly stated in pre-seed rounds, but it’s absolutely part of an investor’s calculus. To reach the scale and returns that venture funders expect, the U.S. remains the holy grail: a massive market with one centralized regulatory system and a robust appetite for technical solutions to address systemic inefficiencies.

Nothing surprising there.  Here’s the kicker: while U.S. market entry is an expected milestone, the actual mechanics of that market aren’t well understood, even by the most sophisticated and experienced founders.

My lunch companion perfectly illustrates this phenomenon. During our conversation, he reflected on his one lingering regret: they should have pursued the U.S. market. They hesitated, he explained, because the regulatory pathway was too demanding and uncertain. They weren’t eligible for 510(k) clearance, and the full FDA De Novo process meant indeterminate cost and timeline risk. So, they deferred, missing the momentum from their last funding round. They should have entered the U.S., he said, and the mistake was one of timing.

I wasn’t convinced. I was familiar with his application, a digital therapeutic for mental health management. It had clear potential as a patient engagement and self-management tool, which meant natural appeal to U.S. payers and providers managing risk-based or capitated populations. I asked whether they had considered piloting the intervention in the U.S. as a supportive tool, positioning it (at least initially) outside the clinical intervention framework. This approach,  often overlooked by European founders, would have enabled his team to build an evidence base (not clinical trial evidence, but ROI evidence), develop relationships with payers and providers, and establish U.S. market traction without waiting for FDA clearance.

He didn’t hesitate: “No. Ours is a clinical intervention. We needed FDA clearance, and that would have taken two years.”

His forceful and unequivocal response was a bit of a conversation stopper: it also, however reflected a crucial misunderstanding of the US healthcare market. He assumed that how his product worked in the EU and its business model would be the same in the US.

Specifically, in Europe, his product’s background as a clinically validated intervention to treat a given condition was the company’s greatest asset. Its designation as a clinical intervention was core to the company’s identity and its business model. But that rigid adherence to a clinical-only identity became a limitation in the U.S. market. It blinded his team to a critical opportunity: the ability to pivot and adapt the application to serve the pain points of their (actual) US clients- healthcare payers.  By framing their solution so rigidly, they locked themselves into requiring FDA clearance, which meant they never entered the market at all.

Why Are European Founders so disposed to make this mistake?

This isn’t an isolated case, but a pattern I see among European founders. Not because they lack sophistication, but because their experience and success in home systems have trained them to think in ways that don’t translate to the U.S. context.

When European founders look at the U.S. market, they assume it works like their home markets, just faster and more expensive. They apply a European lens to an American ecosystem, and that lens fundamentally distorts what they see.

Let me explain: In Europe, the go-to-market sequence is orderly, hierarchical, and technocratic:

Basically, it works like this:

Regulation → Reimbursement → Adoption

Certification by a Health Technology Assessment (HTA) body or notified body (which grants CE marking, the European equivalent of FDA clearance) unlocks the pathway to reimbursement. Clinical evidence and comparative effectiveness analyses drive evaluation. National contracting and scale follow. The process is long, but it’s predictable, repeatable, and institutional.

This model creates rational assumptions that founders internalize and carry forward:

  1. Regulatory approval (and reimbursement approval) is the primary barrier to market entry and adoption
  2. Clinical evidence and rigorous academic validation are the currency of legitimacy and market success

These assumptions make perfect sense in Europe. They are, in fact, correct for European markets. But when founders apply this same framework to the U.S. market, they fundamentally misread the landscape.

The core misunderstanding is this: the U.S. does not have a healthcare system; it’s a healthcare ecosystem. It’s a fragmented, overlapping network of stakeholders, payers, providers, and value chains—each with different incentives, priorities, risk tolerances, and decision-making processes. There is no central gatekeeper. There is no single payer and that means there is no single pathway to adoption. The difference between a centralized system (with all its challenges and deficits) and an ecosystem built around private market principles is the difference between We Bought a Zoo and Jurassic Park.  (Welcome to Jurassic Park, indeed) There is no unified “market entry” in the way European founders conceptualize and experience it, in their home-European markets.

The complexity of the US healthcare landscape and complexity deserves its own deep dive, which we’ll cover in a future article. For now, suffice it to say: it’s a lot.

Regulation matters enormously in the U.S., but it is not what drives adoption; that intangible “traction” that founders and funders obsess over. This is surprising to most Europeans, because, unlike in European systems, regulators in the U.S. are not the payers or clients. The FDA’s role is to ensure the safety, integrity, and effectiveness of products; but it has no role in reimbursement, utilization, or market success.

This presents a necessary, fundamental shift in the “go-to-market” mind map for European founders. European founders tend to prioritize regulatory approval as the critical milestone, investing enormous resources and time into achieving it, only to discover that FDA clearance doesn’t open doors the way CE marking (certification that a medical device conforms to EU safety and performance requirements) does in Europe. By the time founders realize adoption requires an entirely different playbook (one focused on demonstrating ROI, building payer partnerships, integrating into provider workflows, and navigating a byzantine reimbursement landscape) they’ve burned resources and momentum.

The challenge, and the mistake my friend and his team likely made when evaluating whether to enter the US market, is recognizing these blind spots upfront. Instead of doubling down on strategies tailored to the European model, successful entrants identify where U.S. market dynamics differ and adapt their approach early. This means rethinking not just when to pursue regulatory approval, but whether it’s necessary at all for initial market entry.

Inverting the Sequence: How the US Market Actually Works

For many non-medical devices (specifically solutions that support clinical decision-making or workflows, facilitate patient management and engagement), the sequencing in the US often looks like this:

Adoption → Evidence → Reimbursement → (Maybe) Regulation

Here’s a critical distinction that European founders often miss: not all digital health tools require FDA authorization. Indeed the U.S. regulatory framework was never designed to treat every digital health tool as a medical device. The FDA regulates “Software as a Medical Device” (SaMD), when the software performs a clinical function: diagnosing, treating, or making autonomous clinical decisions. Many tools that support clinicians, help patients manage chronic conditions, facilitate workflow, or promote general wellness either fall outside the device definition entirely or sit within the FDA’s doctrine of enforcement discretion polices. In practice, this means a wide range of adjunctive tools can be deployed without FDA authorization under the agency’s policy of “enforcement discretion”, because clinical judgment, not the software, remains the locus of control.

This discretionary space is not an accident; it reflects a broader U.S. tradition of light-touch professional regulation, where physicians retain autonomy and are expected to interpret and contextualize supportive tools. The result is a large market of solutions that augment care rather than replace it. European founders, coming from systems where regulatory designation tightly maps to reimbursement and adoption, often misinterpret this as a loophole. It isn’t. It is a feature of the U.S. system: a system that relies more heavily on clinician judgment, tort liability, and market forces than on centralized gate-keeping.

Of course, as software becomes more central to care delivery and AI systems assume more determinative roles, the line between supportive workflows and the autonomous provider will continue to blur. The scope of FDA regulated products will continue to expand, but it is important to keep in mind that, even with expanded regulatory reach, not all regulatory process and pathways are the same or will involve the same rigor as the archetype De Novo review (tailored to pharmaceuticals and traditional medical devices). The agency is developing a more structured, software-specific regulatory framework. This evolving framework reflects intentional, risk-based philosophy that allows low-risk and adjunctive tools to evolve quickly without unnecessary burden. This more nuanced regulatory architecture seeks to adapt to modern care workflows while preserving core free-market principles: promoting speed, innovation, and real-world use. This evolving but still flexible space remains a viable and often faster pathway into the U.S. market.

Why this is consistently missed by EU Founders:

This is a hard concept for EU founders to grasp, let alone spend capital pursuing. You send your product to market… without regulatory approval. Develop partnerships, generate evidence of impact (whether on operational processes, patient outcomes, or cost savings), develop and execute business plans and generate revenue without any sign-off from an administrative body. This is the inverse of the European system(s). It feels risky, unstructured, and perhaps even illegitimate (it’s not). Instead it reflects the diversity of the US ecosystem – a market characterized by decentralization, free-market principles, and where money (and evidence of ROI) talks.

This brings us to a universal truth in business: know your customer. You won’t get far whether selling donuts or digital health unless you tailor your offering to your actual clients and their context and priorities. In the U.S., the State is often not the client (even though they’re often the ones picking up the check in the end). If you don’t prioritize developing traction and credibility with the actual prospective clients and payers, FDA approval by itself gets you nowhere commercially.

My friend’s company had a solution that could have provided immediate value to U.S. payers managing chronic disease populations. By piloting it as a patient engagement tool (as support to a patient’s care team, outside the direct clinical intervention framework) they could have:

  • Built relationships with U.S. payers (who are desperate for tools that improve outcomes and reduce costs)
  • Generated evidence of impact in the U.S. healthcare context
  • Established a revenue base to fund the regulatory approval process (if needed)
  • Learned what U.S. stakeholders actually need and prioritize (which might have been different from initial assumptions) and adapted their offering to meet these needs
  • Positioned the company for acquisition or partnership

Instead, by defining their solution as exclusively clinical, they made FDA clearance a prerequisite, which meant they never entered the market at all. To return to the Jurassic Park analogy, when you are navigating an ecosystem predicated on free market principles, sometimes your best move is to think different and come from the side.

Addressing Blind Spots and the Genesis of the Series

Our conversation also raised another challenge, one that compounds these blind spots: the pressure on founders to always appear certain.

Founders are conditioned to project confidence, expertise, and conviction. Admitting uncertainty, especially about something as fundamental as “how does this market actually work?” feels like a weakness. In pitch meetings, investor updates, and competitive conversations, there’s enormous pressure to have all the answers.

This creates a trap.

Founders can’t admit when they’re operating from flawed assumptions because doing so would undermine confidence in their ability to execute. So, there is a tendency to double down on the mental models they know, even when those models don’t fit the new context. And even when founders hire experts (regulatory attorneys, market consultants) those professionals tend to focus on specific technical questions rather than challenging foundational assumptions at the risk of stepping on toes.

This is particularly acute when it comes to the U.S. healthcare market because it’s so genuinely confusing. Even Americans who work in healthcare struggle to explain it coherently. How do you ask “stupid” foundational questions, like “Why doesn’t the U.S. have universal healthcare? How does the absence of a national health system translate to different market priorities? Who actually decides what gets reimbursed? Why do payers and providers seem to have conflicting incentives?” when you’re supposed to be the expert positioning your company for U.S. expansion?

The answer to identifying and resolving cultural blind spots is to ask these questions, to be vulnerable, to admit what you don’t understand. But the founder experience rarely creates space for that kind of vulnerability. That’s why I’m writing this series.

After my lunch with my friend, I realized that what European founders need isn’t just a guide to U.S. regulatory pathways or reimbursement codes or an explanation on how to approach hospitals and health systems. Those resources exist.

What’s missing is an accessible explainer of the foundational mental models and cultural assumptions that shape the U.S. healthcare market – and how it applies to (and sometimes directly challenges) the EU founder. This series is designed to be that resource: a space to understand not just what the U.S. market requires, but why it works the way it does. Consider this series a safe space for “stupid questions;” or, to continue the Jurassic Park theme, your pocket Jeff Goldblum, a companion who can interpret the tremors, identify the pivots and when, frankly you “must go faster.”

We’ll cover:

  • Why the U.S. isn’t “one market” and what that means for your strategy
  • How U.S. stakeholders define and measure “value” (and a glossary to translate industrial jargon)
  • Understanding US value-based care and other transformation models (and the role of digital solutions).
  • Why European traction and clinical evidence don’t automatically translate to U.S. regulatory approval or market success
  • An overview of the FDA approval process, and the evolving approach to regulating Software as a Medical Device.

The goal is not to overwhelm you with complexity (as always, our objective is to studiously untangle complexity), but to help you see the U.S. market clearly, and therefore to move forward confidently, hopefully bridging the best of two worlds.

In the next installment of our series, we’ll dig into what “the U.S. is not one market” actually means and how this impacts your market strategy.

…

About the Author Tina Simpson is a healthcare strategist and co-founder of Line Axia, a consultancy that helps European digital health companies navigate U.S. market entry. Having worked on both sides of the Atlantic, she specializes in translating across healthcare ecosystems, 90s adventure films, and regulatory jargon.

…

 

If this article resonated with you—or if you found yourself thinking “wait, that’s exactly the conversation we need to have internally”—let’s talk. Line Axia works with European digital health companies navigating U.S. market entry, helping founders recognize blind spots before they become expensive mistakes.

As always, this post was written by a human being! Not AI. ChatGPT was used for final grammar edits and spellcheck.

Is Lithuania the Next Data-Center Boom in Europe?

Short answer, Yes. Long answer, also Yes – but with an important asterisk.

Google Earth

By: Olivia Aréchiga Co-Founder, Line Axia

(AI Disclaimer – as always, this post was written by a human, me! I used ChatGPT-5 to confirm and discuss several sources.)

 

Who remembers that great viral video from February this past year – Baltic leaders flipping the grid switch, ending reliance on Russion/Belarus energy, to join the European energy grid?

Lithuania and its neighbors, Estonia and Latvia, are now completely independent of the Russian and Belarusian electricity grid; a gigantic and critical step in their geo-political stability, and economic development interests.

This is one of the key reasons investors should be looking more closely at this region for data center (DC) development. Lithuania, uniquely, offers several advantages over its other Baltic neighbors.

First, let’s discuss why we’re looking at DC development specifically, and not, for example, manufacturing. Both could be lucrative and beneficial for the Lithuanian economy. But DC is the golden egg.

Why?

 

AI.

 

Data center development brings large upfront capital (hundreds of millions to billions) with long asset-lives. They act as an anchor for the digital economy, and as a lynchpin for AI.

It’s important to understand, AI development ultimately depends on three interlocked resources: compute, power, and storage. Compute is what turns data into intelligence (chips, GPUs). Storage is what holds the massive training data and physical servers. And power is what keeps it all running.

But traditional DC’s used for colocation and redundant on-ramps are not going to cut it. AI requires “hyperscale” DC campuses. A hyperscale data center is not just big; it’s uniquely architected for scale and uniformity.

It’s the physical backbone for companies running enormous workloads such as AI model training and cloud services.

Simply training a single large-scale model can consume millions of kilowatt-hours, and that electricity draw keeps going once the model is live. That’s why data centers are no longer just digital infrastructure; they’ve become energy infrastructure.

AI workloads outgrew traditional data centers a long time ago. Hyperscale campuses are now the only environments that can reliably deliver the power, cooling, and interconnect required for modern model training and large-scale inference.

And it seems Lithuania, with its large, grid-adjacent land, renewable energy expansion, and fast-track permits, is actively positioning themselves to attract new hyper-scale builds.

 

Power & Sustainability

 

Lithuania offers a grid that’s both reliable and increasingly green. According to their own national investment-agency site, Lithuania aims to have 100% renewable energy infrastructure by 2030. This timeline is far more aggressive than most other EU countries.

By comparison Lithuania’s neighbor, Latvia, hopes that by 2030, they’ll be able to “source 57% of total energy from renewable sources, with an ultimate goal of climate neutrality by 2050…”.

Cooling is a giant electricity cost line-item for data-center OPEX. Lithuania’s climate is relatively cool for most of the year, enabling efficient and “free-cooling” strategies. That means lower power usage effectiveness (PUE), fewer external heat-risks, and better margin potential for operators.

For data centers, electricity is easily the largest cost and one of the biggest risks. Lithuania’s combination of cost-control, credibility and sustainability is a serious draw.

 

Connectivity & Geography

 

Lithuania sits on multiple international fiber corridors, including the Baltic Highway and NordBalt  subsea cable to Sweden and Germany.

This provides Lithuania a low-latency link to Western Europe, while still providing geographic and political diversification from saturated markets like the Netherlands and Ireland (currently two of Europe’s most congested DC hubs).

Lithuania has lower population density than other EU countries. Lithuania’s density is ~46 people/km² over 62,674 km² of land, well below many Western EU markets, so it is ostensibly easier to find parcels with distance from housing/residential areas, while still tying into the electric and fiber grids**.

The number one lesson of real-estate (“Location, Location, Location”) also governs data-center siting, but with more exacting technical requirements. The ideal site is a kind of “Goldilocks zone”, ideally not (too) densely populated, but not so remote that grid connection and fiber access become costly or wasteful.

Several big industrial parks, “FEZ” zones (Free Economic Zones), are reportedly energy-anchored sites designed for big-footprint industry. The government is actively advertising “Data Center” ready sites, like its Kruonis area.

Established Data Center markets like Ireland and the Netherlands, have fewer large, “shovel ready” industrial parks with heavy-duty grid access and comfortable residential buffer. Their combination of grid connection limits + hyperscale policy constraints + urban density makes it hard to stand up very large, quick-to-build plots regardless of whether a parcel exists on paper. These constraints are why developers often look to peripheral regions or other countries for the big footprints hyperscale builds require.

** Authors note – Building Data Centers that tie into populated or residential areas has become more and more controversial  – and for good reason. They raise electricity costs for homes & businesses nearby, and can have a substantial negative effect on the environment, including water scarcity, ecological disruption, and water contamination. Hyperscale DC’s use an unprecedented amount of energy; renewable energy is the only feasible way to keep up.**

 

Business Environment & Incentives

 

The Lithuanian government is actively treating data-center development as a strategic sector. And so far, it doesn’t seem like this is just a marketing head-fake.

Earlier this year, Lithuania’s Economy & Innovation Ministry launched an “Investment Highway”. This legislation cuts red tape for major projects, framed as enabling “up to ten times faster movement from investor decision to start of construction.” It includes the “Green Corridor” initiative, aimed at streamlining procedures for large scale, complex projects, and attractive tax benefits.

How effective this program will be remains to be seen, but the legislation itself seems to offer concrete pathways and clear communication routes directly into government channels.

An even stronger signal of Lithuania’s strategic commitment to this broader initiative is how rapidly it transposed and implemented the EU’s NIS2 cybersecurity directive. Neighboring Estonia and Latvia missed the EU’s implementation deadline. This is not a minor detail: it signals not only a level of cybersecurity readiness, but also regulatory predictability and administrative capacity, qualities that materially de-risk long-horizon infrastructure investment.

In preparing this article, I reached out to Milda Venckutė, Investment Advisor, and Elijus Čivili, the General Manager for Invest Lithuania, the country’s official investment promotion agency, for comment.  Mr. Čivili’s response reinforced the degree to which data infrastructure (at the scale needed for AI transformation) is now regarded as a matter of national systems planning, rather than ad-hoc speculative development by any single sector.

“This fits our pattern” he explained, pointing to Lithuania’s increased prominence as a fintech hub in the wake of Brexit, and its recent expansion of defense manufacturing facilities in cooperation with Rheinmetall.

“Both times, we saw the opportunity and moved fast with flexible regulation and clear investment strategies. Now with our new Investment Highway initiative cutting pre-construction development timelines by half and our government treating data centers as a strategic priority, we are applying the same approach to become a solution for AI data centers.”

Centralized government strategy and coordination are now used to accelerate hyperscale development, underscoring data-center capacity is being treated as core national infrastructure, not simply a development or tax-arbitrage play.

Lithuania’s DC market is currently pretty small. And yet, it has all the markings of a fertile environment for development.

As any investor will say, “timing is key”. Being early in a high-growth region can lead to land-locked advantage, first-mover positioning, and cost arbitrage advantages relative to Europe’s current saturated hubs.

 

The Other Side of the Coin – Local Competition & Geography

 

Lithuania isn’t alone. The Baltic and Nordic regions are courting the same investors with similar pitches: green energy, cool climate, low cost. The differentiator may come down to execution, who actually delivers power, permits, and uptime faster.

Lithuania has made more concrete moves to convince the world they are the ones in the region who can do this.

Estonia is positioning itself as a leader in digital public services, and Latvia seems “less vocal” on fast-track builds. For big infrastructure (like data centers), Lithuania’s recent reforms make it the most overtly pro-build of the three.

 

What about Lithuania’s “Other” Neighbors?

 

We have yet to talk about the big elephant in the room – Russia (Kaliningrad) and Belarus. These neighbors provide real concern about Lithuania’s geopolitical position.

As a NATO and EU member, this should quell some investors’ fears about Lithuania’s geopolitical instability. And in being completely energy independent, they’ve effectively eliminated Moscow’s leverage over electricity flows.

Yet, risks such as Russian DDoS and cyber-attack campaigns (like the 2022 Killnet) are not one-off concerns, and must be assumed in long-term resiliency planning. The NIS2 framework is important in this regard, as Lithuania seems more than aware of these threats, and more importantly, prepared.

There is also a concern with infrastructure security, specifically undersea cables.  In response, NATO earlier this year formed “Baltic Sentry”, a new military program to “strengthen the protection of critical infrastructure”.

Yet, one cannot ignore the outright animosity in the region, and Russia’s coercive maneuvers, hostile positioning and rhetoric requires its Baltic neighbors be on a heightened security posture at all times. There is an objective need for more advanced incident protocols in the region, to manage real geographic risks that other EU countries don’t share.

Thus, while Lithuania’s NATO/EU status and 2025 grid synchronization have reduced structural dependence on Russia/Belarus, hybrid threats (cyber, undersea) remain a real, ongoing planning assumption. These risks are manageable, but they must be engineered in from day one.

Additionally, while Lithuania’s renewable goals are bold, the grid is still developing. Integration of variable wind and solar sources can cause load-balancing challenges, and large-scale data centers require stable, uninterruptable and redundant power.

Neighboring countries like Sweden and Finland already have excess generation capacity and more experience supporting hyperscale loads needed by DC’s.

Lithuania is still building its data center brand. The ecosystem of specialized contractors, suppliers, and technical workforce isn’t as deep as in Frankfurt or Dublin. For early entrants, that means potentially higher build costs, longer commissioning times, and steeper learning curves.

 

The Bottom Line

 

Lithuania’s fundamentals are solid: sustainable energy, efficient climate, strong connectivity, and a government that actually wants and is working to develop this business.

But it’s not a turnkey market – it’s a build-and-shape market.

For operators and investors willing to engage early, Lithuania offers first-mover advantage and cost-efficient positioning in the EU’s underdeveloped East-Nordic corridor.

It’s not at a “Frankfurt-level” readiness, but that’s a good thing. AI is not going to slow down (at least in terms of infrastructure), and DC’s are the requisite commodity for that development.

For many data-center developers and AI/capacity providers, the factors mentioned add up to a compelling site thesis: efficient cooling, renewable energy alignment, strong connectivity, and a business environment ready to compete.

If Lithuania continues its current trajectory, there is no doubt of it being a competitive and viable market. It’s a matter now of which investors are willing to take the risk now, and are patient enough for the inevitable payoff.

 

AI Disruption

How Disruptive is AI?
Examining the Narrative Behind the Hype


By: Olivia Arechiga, Co-Founder – Line Axia

(AI acknowledgement: this post was written by a human, me! I used ChatGPT-4 to fact check a few items, confirm sources, and to answer a few specific questions).

“People worry that computers will get too smart and take over the world. But the real problem is that they’re too stupid and they’ve already taken over the world.”—

Pedro Domingos, computer science professor, University of Washington. 2015.

Artificial Intelligence (AI) has been hailed as a force of radical disruption.

We’ve all heard the fear-mongering and the siren songs: AI will take over the world, AI will replace humans, AI will solve & cause immense problems. All major tech CEO’s have something profound to say on AI, and all seem to think they are right. From healthcare and finance, to logistics, media, and legal services, nearly every industry is bracing for an up-ending transformation.

Just look at your LinkedIn feeds – it seems there every third post is about how AI solved X problem, or spouting their opinion on AI, hoping you’ll think of them as an expert on the subject.

But as organizations rush to integrate large language models, predictive analytics, and workflow automation tools, it’s worth asking a more fundamental question: Is AI truly “disruptive”, and “revolutionary”, or is it just the next logical phase in digital evolution?

This post takes a measured look at what disruption really means in practice, how AI is unfolding in real organizations, and why leaders should temper both fear and hype with critical analysis.

First off, let’s define “Disruption”

For the sake of this post, we’re sticking with the definition used by Clayton Christensen, in his 1997 book “The Innovator’s Dilemma”.

In it, Christensen describes a specific and unique kind of market shift; the theory of disruptive innovation. Disruptive innovation in this context, is innovation that creates a new value network, often entering at the bottom of an existing market, and eventually displacing established market-leading firms, products, and alliances.

In this sense, disruption is not about radical new technologies per se, but about business model displacement and transformation.

Through this lens, AI’s disruptive potential depends less on how technically advanced it is, and more on how it restructures value chains and redefines who captures the margin. So far then, it seems many AI use cases are powerful, but largely adjunctive, and not truly destabilizing.

“Take a look at software developers. It’s not hard to see AI can help software engineers, and help optimize their workflows, but not to the extent that it can replace them. Illustrating this point much better than me, is GitClear’s founder warning that AI tools are generating “AI-induced tech debt,” with code churn increasing, and hastily added code, creating substantial maintainability challenges.

Emily Bender (noted AI skeptic and professor at the University of Washington for Computational Linguistics Laboratory) wrote a piece in the Financial Times, June 2025, in which she called LLMs “stochastic parrots” and “plagiarism machines” lacking real understanding; the boom being built on misconceptions that mask their limits and costs.”

But we know AI has had a direct effect on jobs. So who Is Actually Being Displaced? And is this a permanent change?

As with any “disruptive” technology or change, the process by which it integrates itself into society is messy in its regulatory and societal integration. The market effect therefore is inconsistent and often painful. The key is distilling what is real and lasting change, and what is the overreaction of tech leaders trying to temporarily improve their bottom line and stay ahead of the perceived curve.

The more significant disruptions we have seen are at the service layer, not the strategic core. A few examples:

  • Freelancers in design, content writing, and translation are already seeing pricing pressures as AI models produce similar content and products at no or very low cost.
  • Customer support outsourcing is being re-evaluated in light of 24/7 AI agents.
    • More on this later, but more businesses are looking at replacing customer support with AI agents and AI bot support.

In these sectors, AI may indeed be commodifying a sector of specialized labor. But in regulated, high-stakes, or relational contexts like healthcare, law, consulting, public sector- AI tools remain assistants, not substitutes.

Additionally, the displacement effect, where it exists, is fragmented and uneven.

It seems then, what we’re talking about here is more evolutionary than revolutionary.

  • Routine cognitive tasks (data entry, document summarization) are vulnerable.
  • Manual and low-wage jobs in unpredictable environments (e.g., skilled care work, construction) remain largely resistant.
  • Highly skilled knowledge work (e.g., law, medicine) may become augmented, not displaced.

Why “Disruption” Isn’t Inevitable

First off, AI is not a monolith.

It encompasses an array of technologies: machine learning, natural language processing, computer vision, recommendation algorithms, generative models- each with different maturity levels and use cases. Generative AI (like what you use Claude or Chat-GPT5 to write your emails or create an image), is all the rage at the moment.

But most “AI” embedded in industry today is still narrow and purpose-built– automating specific tasks (e.g., fraud detection, predictive maintenance, customer support chat-bots), not reinventing entire workflows or job categories.

It is, plainly, falling short of its promises.

Gartner’s “trough of disillusionment” comes to mind.

https://www.economist.com/business/2025/05/21/welcome-to-the-ai-trough-of-disillusionment

There are several high-profile examples of AI pilots programs stalling in what Gartner calls its “trough of disillusionment.” Are we currently sitting at the “Peak of Inflated Expectations?”, or somewhere near there?

Think back to “Humane”, the company who launched a buzzy wearable AI device- the AI Pin- as a high-profile proof-of-concept. By mid-2024, operations were halted. The product didn’t transform user interaction and failed to live up to expectations, illustrating a classic “nice demo, but no mass-market product” scenario.

A recent article by Live Mint, highlighted 42% of companies are abandoning most GenAI pilot projects, citing frustrated Chief Executives who feel the money has been spent without delivery.

And a 2024 report from MIT Sloan found that only 23% of firms using AI reported significant impact on core business models.

The questions now are, are these just the messy, contradictory market convulsions we tend to see with disruptive technologies? Where hype and marketing don’t align with operability, scale, and function? Is AI ever going to actually, substantially, and permanently disrupt our economies and workflow?

Labor Market Shifts Will Be Gradual- And Messy

The claim that AI will replace vast swaths of the workforce is not baseless, but it is definitely oversimplified.

Technological capability alone does not guarantee success. Just because you successfully integrated an AI chatbot tool into your stack to replace 75% of your call-center agents, does not mean that it will produce a net-positive for your business. We’ve seen big changes and hasty-layoffs now, as businesses scurry to reduce their bottom line and integrate fancy and exciting AI tools, but the reality of AI is that it is still pretty bad at most tasks. (Who here has had to work with an AI customer service agent? I have, and it’s been veritably terrible each and every time). Those that integrate AI tools too early or hastily, get their feedback quickly in the market response by the consumer.

We don’t need to look too far for a few notable examples:

  • Klarna – After replacing 800 customer service staff with AI chatbots, Klarna found that customer satisfaction dropped “significantly”. Klarna then reversed course, rehiring human agents to ensure customers “always have the option to speak with a real person”.
  • IBM – Replaced many HR roles with an AI bot (“AskHR”), but found the system failed to replicate human empathy and judgment. IBM then rehired staff to fill those gaps in their HR division.
  • Duolingo -Declared it would become “AI‑first” in a near-viral message that was all over social media. They laid off 10% of its workforce. Its intention was to use AI to replace contractors for translation, amongst other tasks. Within a week, it reversed course: CEO Luis von Ahn clarified they’d continue normal hiring, using AI as a tool to “assist rather than replace” employees.

But on the other hand, we see things like Amazon’s CEO Andrew Jassy announcing “he expects the rise of generative AI to “reduce” Amazon’s corporate workforce over the coming years”.

So what are we to think?

Well, tech giants have everything to gain in perpetuating the idea that AI is, and will continue to, disrupt our modern workforce as we know it.

Narrative = Valuation: Market history shows us publicly traded tech companies benefit when they’re seen as innovators. Framing AI as a transformative force boosts investor enthusiasm, driving up stock prices.

Capital Inflows: VC and institutional funding favor “AI-forward” firms. AWS, for example,  can undoubtedly attract more clients and partners by emphasizing AI as the future.

Market Perception: If Amazon for example doesn’t talk about AI, it may look like it’s falling behind peers like Google, Microsoft, or Meta. Public statements maintain a perception of technological leadership – no matter how realistic, effective, or devastating those plans might turn out to be.

“Inevitable Future” Mentality: If workers, governments, and competitors believe that AI adoption is inevitable, it becomes a self-fulfilling prophecy. Dissenters look regressive or inefficient. Companies are afraid to be left behind. Talking about AI begets talking about AI.

Social License to Automate: Calling AI a “revolution” provides a certain level of moral and economic cover for hard-to-defend decisions… like replacing human roles.

In reality though, there are still enormous, dynamic hurdles AI needs to clear- before it can even be considered an actual revolution.

  • In healthcare, AI diagnostic tools face strict regulatory review, data residency laws, and major ethical scrutiny.
  • In legal practice, issues of privilege, authorship, and jurisdiction make full automation of legal reasoning unlikely in the near term.
  • In finance, auditability and explainability remain major barriers to black-box AI adoption.
  • Government regulation and compliance: the EU AI-Act has been touted as the most aggressive and far reaching regulation surrounding AI to date. Yet it’s hitting big roadblocks, with integration and adherence planning at the near foregone conclusion that it will be delayed.

What we’re actually seeing instead is selective adoption– AI being used tactically, not structurally, in environments where trust and accountability still matter more than speed or cost.

What Leaders Should Actually Focus On

Rather than asking whether AI will disrupt their industry, organizations might get farther by asking:

  • Where does AI offer immediate value without compromising risk posture?
  • Which workflows can be streamlined without undermining human oversight?
  • How should governance evolve to integrate AI responsibly and strategically?
  • What talent and skills will be needed to translate AI outputs into real-world impact?

The true differentiator won’t be which company has the most aggressive and highest number of AI tools, but which ones can integrate them sustainably, and  meaningfully within their operational model and stakeholder context.

And it’s important to remember, disruption is not uniform. It is filtered through economics, regulation, unionization, ethical boundaries, and social context.

Final Thoughts

AI is significant. But disruption is not a foregone destiny. The most likely scenario is not an AI revolution, but an AI diffusion, a gradual, uneven, messy integration into the workflows of institutions that are already adapting to other transformations: remote work, data localization, ESG reporting, and platform consolidation.

For leaders, the real work lies not in reacting to headlines (or even stock prices), but in building the operational discipline to assess, adopt, and govern AI technologies in proportion to their actual impact.

Hypnotized by Complexity

Hypnotized by Complexity:
How Excess Systems and Processes Cloud Judgment

 

By: Olivia Arechiga, Co-Founder Line Axia Consulting

AI disclaimer: As always, a human being wrote this article (me!) I used AI tools however to fact-check and proofread.

 

“We struggle with the complexities and avoid the simplicities” – Norman Vincent Peale

In the never-ending pursuit of growth, compliance, and innovation, organizations naturally accumulate “layers” of systems, and frameworks –  in the name of efficiency, compliance, or even “the next best thing”.

Over time, these layers create a dense operating environment in which complexity is normalized – and even virtuous.

This phenomenon, which we refer to as being “hypnotized by complexity,” is not just an operational challenge; it is a strategic liability.

This post explores the roots of organizational complexity, how it impairs decision-making, and why leaders must remain vigilant in disentangling the useful, from the unnecessary and distracting.

 

Understanding the Nature of Complexity

Complexity in business is by no means negative. In highly regulated sectors such as healthcare, financial services, or digital infrastructure, a certain degree of sophistication is simply non-negotiable.

However, the distinction between essential complexity and accidental or outdated complexity is often blurred (…or ignored).

Essential complexity refers to the inherent intricacy of the issues one is seeking to address.

Accidental complexity arises not from the inherent demands of a system, but from historical decisions, poor integration, siloed teams, or an over-reliance on one-size-fits-all tools.

This complexity then goes unchallenged because it is embedded in bad institutional habits or justified by legacy compliance concerns. It becomes justified by how the work is managed. And because it becomes familiar, it often goes unchallenged.

For lack of a better phrase, it’s a rock you don’t have time (and don’t want) to look under.

 

The Psychological Dimension: Decision Fatigue and Overload

Leaders navigating complex environments are bombarded by an overwhelming number of variables, including metrics, vendor options, approval gates, governance frameworks, and ever-changing compliance requirements. This creates decision fatigue, where the quality of judgment actually deteriorates over time due to cognitive overload.

A study published in Harvard Business Review outlines how even experienced decision-makers begin to rely more on heuristics and defaults under pressure, potentially undermining the quality of their choices (Harvard Business Review – “Beware of the Busy Manager”).

Complexity of this kind, often fosters a sort of procedural inertia– where decision-makers defer action, or over-consult stakeholders- not because it adds value, but because the system implicitly demands it. 

Over time, the result is a system that can (seemingly) only survive in its current complex, over-architectured state. You were, in effect, hypnotized into thinking this is required. 

 

Common Drivers of Accidental Complexity

  1. Legacy Infrastructure and Policy
    Outdated tools, processes, or compliance interpretations are rarely (or not totally) decommissioned. Instead, new layers are added on top of the old, creating internal friction, excessive budgets, and a lack of clear understanding of what is needed.
  2. Functional Silos
    Highly specialized departments often build their own systems, taxonomies, and workflows, which complicates cross-functional integration.
  3. Over-responsiveness to Risk
    In regulated or litigation-sensitive industries, it’s common for compliance functions to actually over-correct, resulting in burdensome procedures that can outpace actual legal or regulatory requirements.
  4. Tool Sprawl
    The proliferation of platforms, especially in IT and healthtech, often leads to overlapping capabilities and poor system visibility, rather than better outcomes.

Organizational Consequences

The net effect of unmanaged accidental complexity is not simply inefficiency- it is strategic drift.

Decision-making slows. Coordination erodes. Stakeholders lose a shared understanding of priorities. This environment then rewards “fire-fighting” rather than long-term thinking. And that reward system makes it difficult to understand and see that you actually are in a fire-fighting system, not a strategic and sustainable future-proofed path.

Moving Toward Deliberate Simplicity

Reducing complexity is not about oversimplification or ignoring legitimate risk.

It is about applying design thinking, analytical rigor, and cross-functional cooperation to distinguish between what is essential and what is merely habitual.

A few guiding questions for teams:

  • What decisions consistently take longer than they should, and why?
  • Are there systems or processes no one fully understands, yet still rely on?
  • Do our compliance mechanisms reflect today’s requirements, or yesterday’s fears?
  • Where are there repeated failures of coordination or accountability?
  • When was the last time there was a full audit of all IT tools and services?

Clarity, in this context, is not a communications goal, it is an organizational discipline.

Final Thoughts

Every organization carries complexity. The challenge is to recognize when that complexity has ceased to serve the mission, and instead obscures it.

In a marketplace where every product promises to solve your current pain point, it is easy to stay “hypnotized” by complexity.  Getting out of this cycle and perspective is both difficult and seemingly counterintuitive at times. “Consciously decoupling” essential complexity from incidental complexity, needs an expert outsider viewpoint and voice.

This is Line Axia’s mission and role: to break the trance or procedural inertia associated with overburdened systems and help businesses reclaim clarity – ensuring they invest in what truly adds value, and shedding what does not.