Regulatory Trends 2026: AI Act, DSA, and DMA Impact on Startups | Ultimate Guide For Startups

TL;DR: Regulatory Trends 2026: AI Act, DSA, and DMA Impact on Startups

Table of Contents

Regulatory Trends 2026: AI Act, DSA, and DMA Impact on Startups means you need to build compliance into your product early, so you can ship faster in Europe, pass investor and buyer checks, and avoid costly rework.

• The AI Act, DSA, and DMA affect real startup decisions like AI feature design, user content moderation, app store reliance, search visibility, marketplace duties, and human review for risky flows.
• The startups most exposed include HR tech, fintech, health, edtech, marketplaces, creator platforms, e-commerce, and B2B SaaS tools with AI features or strong platform dependence.
• The article’s main benefit for you is a lean 12-week founder system: map your AI and content flows, classify risk, assign one owner, keep a live system register, add plain-language notices, set up appeals, and track channel dependence.
• The biggest founder mistakes are shipping AI before classification, assuming DSA only applies to giant platforms, trusting DMA to fix platform power, and treating documentation like investor theater instead of daily operations.

The article also shows that trust, traceability, and clear records can help you close enterprise deals faster and answer due diligence with less stress. If you want more founder-focused context, read AI compliance trends or AI product launch rules. Read the full guide and use the week-by-week checklist to audit your startup now.

Check out startup news that you might like:

Intercom News | June, 2026 (STARTUP EDITION)

When your startup finally decodes the AI Act, DSA, and DMA… and realizes the real product is compliance with a side of panic. Unsplash

Regulatory Trends 2026: AI Act, DSA, and DMA Impact on Startups is no longer a policy topic for lawyers alone. It is now a founder issue that affects product design, hiring, fundraising, distribution, customer support, platform dependence, and even whether your startup can ship features in Europe without triggering legal friction.

If you are building with AI, selling through app stores, relying on search or marketplaces, or moderating user content, this guide matters. For startups, the EU AI Act, the Digital Services Act, and the Digital Markets Act form a practical rulebook for how software is built, marketed, ranked, audited, and challenged. Unlike vague “be careful” advice, these rules attach duties to concrete business actions.

My view, as Violetta Bonenkamp, is simple. Founders lose time when compliance sits outside the workflow. I have spent years building deeptech and startup education products across Europe, and I keep seeing the same pattern: teams treat rules like an end-stage legal clean-up, then panic when a partner, regulator, or investor asks for evidence. That is a bad way to build. Protection and compliance should be invisible inside the product and process, not added in a late-night sprint before fundraising.

Why this topic matters for startups: the companies that map these rules early will move faster in 2026, not slower. They will close enterprise deals faster, answer due diligence faster, and reduce platform dependency earlier. If you operate in Europe or sell into Europe, waiting is expensive.

Key takeaway

How the AI Act, DSA, and DMA shape product and go-to-market choices for startups
Which startup models face the highest exposure in 2026
How to set up a simple founder-led compliance system without bloating the team
Common mistakes that burn cash, slow sales, or scare investors
What different startup stages should do first

Why do the AI Act, DSA, and DMA matter so much for startups in 2026?

The short answer is market access. The AI Act touches AI systems and, in some cases, general-purpose AI duties. The DSA covers online intermediaries, marketplaces, and platforms that host or distribute content. The DMA targets gatekeepers, which are the largest digital platforms, but startups still feel it because their access to app stores, ranking systems, data, and distribution channels changes under DMA pressure.

Here is why. Most startups do not fail because a regulator knocks on the door on day one. They fail because they build hidden dependency into the business. They rely on one traffic source, one platform, one moderation shortcut, one unchecked AI feature, or one legal assumption. Then a customer asks for proof, a platform changes terms, or an investor spots a hole.

Research and market signals already point in that direction. Diligent’s 2026 compliance hiring findings reported by Law.com show that 64% of surveyed senior legal and company-secretary professionals see AI governance and oversight as one of the top skills needed in the next three years, and 59% rank technology and data literacy near the top as well. That means buyers, boards, and investors are preparing for scrutiny even if many founders are still pretending this is optional.

Also, policy is moving on both sides of the Atlantic. Debate in the US about a single national AI approach, covered in Bloomberg Law’s discussion of a national AI framework, shows that startups cannot assume regulatory fragmentation will disappear soon. If anything, founders need a practical internal system that can adapt across markets.

What challenge do startups face?

Most founders face five problems at once:

Limited legal budget and no in-house counsel
Fast product cycles that outpace documentation
AI feature pressure from competitors and investors
Platform dependence on search, app stores, social media, and marketplaces
Fundraising scrutiny during diligence

This hits even harder in Europe, where cross-border growth quickly raises employment, tax, company structure, and product-law questions. If you are still deciding entity structure while expanding, review this company formation guide early. Legal structure and product duties are not separate worlds. They collide the moment you hire, invoice across borders, or sign enterprise contracts.

How do these rules help, not just constrain?

Good founders should stop viewing these rules as pure drag. The better frame is this: they push markets toward traceability, explanation, appeals, disclosure, and fairer platform conduct. Startups that can show these things cleanly often beat sloppier competitors in enterprise sales and public-sector deals.

Limited resources: a simple evidence trail reduces repeated fire drills
Rapid growth: clearer internal rules cut product rework later
Competitive edge: trust becomes easier to prove, not just claim
Better decisions: you see which features create legal exposure before launch

That is very close to how I approach IP and engineering tooling in deeptech. Engineers should not need to become lawyers to do the right thing. Founders should not need a 200-page memo to know whether a feature needs human review, logging, notice, or an appeal path.

What are the fundamentals founders must understand first?

AI Act: what is it in startup terms?

The EU AI Act is a risk-based law for artificial intelligence systems. In startup language, it asks: what does your AI do, where is it used, who can be harmed, what evidence do you keep, and what obligations follow from that use case?

Why it matters for startups: not every AI feature is treated the same. A text helper for marketing copy is not the same as AI used in hiring, education, lending, insurance, biometric use, or safety-related products. Founders need classification discipline before launch, not after.

Real-world startup example: if your HR tool ranks candidates or your fintech flags people for risk scoring, you may enter a much stricter category than a simple summarization feature. If you are in fintech, this European fintech guide helps frame how regulation and market opportunity interact across the region.

Related terms: prohibited AI practices, high-risk AI systems, general-purpose AI, provider, deployer, conformity assessment, technical documentation, human oversight, data governance.

Sector watchers are already warning that governance expectations are rising. Hinshaw’s note on rising AI governance expectations focuses on insurers, but the lesson travels well for startups: the market is moving toward documented controls, not founder intuition.

DSA: what is it in startup terms?

The Digital Services Act governs many online intermediary services. In startup terms, if users can upload, list, share, sell, review, or distribute something through your product, DSA logic may touch your business.

Why it matters for startups: many founders think “we are too small” or “we are just a tool.” That can be dangerously lazy. If your product hosts user content, seller listings, community comments, reviews, or app-like distribution, you likely need clear notice-and-action processes, terms transparency, complaint handling, and traceability in some cases.

Real-world startup example: a niche B2B marketplace for industrial components may still need trader information checks and illegal-content response processes. A creator platform may need better content moderation logic and user notices.

Related terms: intermediary service, hosting service, online platform, notice-and-action, illegal content, trader traceability, recommender systems, transparency reporting.

DMA: what is it in startup terms?

The Digital Markets Act targets very large platform gatekeepers. Startups are usually not direct addressees, but they are deeply affected by it. DMA is about fairer competition in digital markets where a few huge platforms control access to users, data, rankings, app distribution, and defaults.

Why it matters for startups: if your growth depends on a gatekeeper, DMA may shape how discoverability, self-preferencing, interoperability, app store rules, or business-user access play out. You should treat DMA as a market-structure rule with startup consequences.

Real-world startup example: a SaaS company that acquires users through search, mobile app stores, or platform ads can benefit if gatekeeper conduct becomes easier to challenge. At the same time, the startup must prepare for volatility while platforms change flows and interfaces to meet new duties.

Related terms: gatekeeper, self-preferencing, interoperability, app store terms, platform access, business-user data, ranking fairness.

Why should founders think about all three together?

Because most products are not “just AI” or “just a marketplace” or “just a SaaS app.” They mix AI outputs, user content, platform distribution, and business-user dependence. A startup can trigger AI Act questions in the product layer, DSA duties in the content layer, and DMA exposure in the channel layer all at once.

That combined view is where many teams fail. They assign the issue to one lawyer, or worse, one ops person, and then act surprised when trust, ranking, moderation, and model behavior show up in the same sales call.

Which startup types face the biggest impact in 2026?

HR tech and recruiting startups using screening, ranking, or profiling
Fintech and insurtech startups using scoring, fraud detection, or decision support
Health and edtech startups where AI outputs may shape outcomes for people
Marketplaces with seller listings, reviews, recommendations, and traceability duties
Creator and community platforms that host user-generated content
B2B SaaS tools adding AI copilots without documenting model behavior and oversight
E-commerce startups dependent on large platforms, app stores, or ranking systems
Deeptech founders handling technical files, logs, and evidence for industrial or safety-linked use cases

And yes, bootstrapped teams are exposed too. In some ways they are more exposed, because they often ship faster, document less, and rely more heavily on single channels. That is why founder discipline matters more than legal team size.

A quick founder test

Does your AI rank, score, classify, predict, or recommend for people?
Can users post, upload, list, review, comment, or transact through your product?
Do you rely on a giant platform for traffic, distribution, or billing?
Can a customer ask why the system produced a result?
Can a person appeal, opt out, or contact a human?
Can you prove what model, dataset, prompt pattern, or policy was active at the time?

If you answered “no idea” to more than two of these, you do not have a policy problem. You have an operating-system problem.

How can startups handle the AI Act, DSA, and DMA step by step?

Let’s break it down. This is a founder-friendly twelve-week system. It is lean enough for a small team and serious enough to survive investor questions.

Phase 1: assessment and planning, weeks 1 to 2

Step 1.1: audit what you actually are

List every AI feature, model, automated workflow, and recommendation system
List every place users can upload, post, sell, review, or message
List every external platform you depend on for distribution, billing, identity, or ranking
Map where personal data, logs, prompts, outputs, and moderation actions are stored
Mark customer segments such as consumers, workers, students, patients, or sellers

Do not let product teams hide behind vague labels like “assistant” or “smart automation.” Define the actual action. Does it summarize? Score? Flag? Match? Filter? Refuse? Recommend? That difference matters.

Step 1.2: classify your risk and duties

Tag each AI feature by likely risk level and use context
Tag each content or marketplace function by DSA relevance
Tag each business dependency by DMA relevance
Rank the top five exposures by likely revenue impact, not abstract fear

Focus on product-revenue links. Founders often spend energy on a rare edge case while ignoring the one enterprise feature that needs explanation, logging, and human review.

Step 1.3: pick an internal owner

Assign one founder or product lead as the rule owner
Assign one technical person to evidence and logging
Assign one operations or support person to notices, appeals, and reports
Set a weekly 30-minute review

Small teams do not need a giant department. They do need one accountable human. If no one owns the issue, it becomes everyone’s last-minute panic.

Phase 2: build the foundation, weeks 3 to 6

Step 2.1: create a simple system register

Create one shared document or database with these fields:

Feature name
Business purpose
User group affected
Model or rules involved
Input data types
Output type
Human review step
Logging status
User notice shown
Appeal or complaint path
Owner
Last review date

This single register solves more confusion than many expensive memos. It also helps when you prepare fundraising materials. If you have not built a clean evidence room yet, this due diligence checklist will save you later pain.

Step 2.2: write product-facing notices in plain language

Tell users when AI is involved
Tell users what the system is meant to do
Tell users its limits in human words
Tell users when a human can review or override
Tell users how to complain or appeal

My linguistics background makes me unforgiving here. Legal clarity is not just legal. It is language design. If your notice reads like an insurance graveyard, users will not understand it and support tickets will explode.

Step 2.3: set up evidence and review flows

Log model versions, prompt structures, and policy changes
Save examples of flagged failures and fixes
Create a human-review queue for sensitive cases
Document moderation and takedown decisions
Track seller verification or trader information where relevant

If your startup runs remote across Europe, assign these responsibilities clearly and in writing. A vague Slack culture is not enough. This remote work policy template can help formalize owner roles, escalation, and documentation habits without overcomplicating the team.

Phase 3: test and scale, weeks 7 to 12

Step 3.1: run controlled checks on high-risk flows

Test AI outputs on edge cases and vulnerable user scenarios
Review false positives and false negatives
Simulate user complaints and appeal requests
Check whether staff can explain a decision clearly

Step 3.2: stress-test platform dependence

Measure how much revenue depends on one gatekeeper or channel
Check whether ranking or store placement changes would hurt you badly
Build one alternative acquisition or retention path
Review contract terms and dispute options with major platforms

DMA may improve market fairness over time, but do not build a startup on political hope. Build backup channels.

Step 3.3: create a monthly founder review

What changed in product logic?
What new user rights or notices are needed?
What complaints repeated?
What enterprise customers asked during procurement?
What platform dependency got worse?

That monthly loop matters more than a fancy policy deck. Founders love one-time documentation because it feels productive. Regulation punishes stale documentation.

What startup practices actually work in 2026?

1. Build a feature inventory before you build a policy binder

What it is: a live inventory of product functions, models, outputs, user groups, notices, and owners.

Why it works: you cannot classify or explain a system you have not named properly. Most startup compliance failure starts as product ambiguity.

Name each feature by action, not by marketing label.
Assign one owner.
Review monthly.

Common pitfall: teams list tools but not actual decisions or outputs.

How to avoid it: describe what happens to a user because the feature exists.

Metrics to track: percentage of features documented, review freshness, number of undocumented production changes.

2. Put human review where harm can happen

What it is: a real human checkpoint for sensitive outcomes such as rejection, ranking with consequences, fraud flags, or account restrictions.

Why it works: human oversight is not a ceremonial checkbox. It catches nonsense, bias, and edge cases that automated systems miss.

Define sensitive outcomes.
Create a queue and response rule.
Train staff to record reasons for overrides.

Common pitfall: “human review” exists in theory but no one has time or authority to act.

How to avoid it: set time windows, role authority, and escalation rules.

Metrics to track: override rate, average review time, repeat complaint rate.

3. Treat notices and appeals as product features

What it is: clear user-facing explanations, complaint channels, and appeals built into the interface and support flow.

Why it works: users trust systems they can question. Enterprise buyers also trust startups that can show a visible process.

Add clear AI and moderation notices in product.
Create one complaint form and one internal triage path.
Store outcomes and reasons.

Common pitfall: hiding rights in terms and conditions.

How to avoid it: surface the process where the decision happens.

Metrics to track: complaint resolution time, appeal outcomes, user confusion rate in support tickets.

4. Reduce channel dependence before you are forced to

What it is: lowering dependence on a single gatekeeper for acquisition, payments, distribution, or ranking.

Why it works: DMA may open some space, but platform power still hurts smaller companies. Optionality matters.

Measure revenue concentration by channel.
Build one owned channel such as email, community, partnerships, or direct sales.
Review platform contract terms every quarter.

Common pitfall: assuming current ranking or access will stay stable.

How to avoid it: act while growth is still decent, not after a traffic cliff.

Metrics to track: revenue concentration, owned audience growth, customer acquisition mix.

And if you are building AI products in security-sensitive contexts, keep an eye on policy discussions about model testing. Coverage from iTnews on voluntary frontier model cyber tests shows how safety expectations are widening beyond pure product UX into security and national-risk questions.

What mistakes do founders keep making?

Mistake 1: adding AI first and classifying later

Why founders do it: pressure to ship, fear of missing the wave, investor signaling.

The impact: product rework, messy procurement, confused support, and possible exposure if a use case crosses into sensitive territory.

Classify the use case before release
Document intended purpose in plain language
Set human-review rules early

If you already did this: freeze expansion of the feature, audit real use, and rewrite user notices before scaling.

Mistake 2: assuming DSA is only for giant social platforms

Why founders do it: they identify as “software,” not “platform.”

The impact: weak moderation, no appeal flow, poor seller checks, and ugly surprises when users or partners push back.

Map user-content and marketplace functions carefully
Create notice-and-action logic
Store moderation reasons

If you already did this: start with your most exposed area, usually listings, reviews, or uploads.

Mistake 3: thinking DMA means startups can relax about platform power

Why founders do it: they confuse new rights for new safety.

The impact: overdependence on one channel and no fallback if a gatekeeper changes rules or interfaces.

Track channel concentration monthly
Build owned channels now
Keep screenshots and records of platform changes that affect you

If you already did this: build direct retention loops first, then diversify acquisition.

Mistake 4: treating documentation as investor theater

Why founders do it: they write documents to impress, not to run the company.

The impact: the team ignores the documents, and the investor sees the gap in five minutes.

Document what people really do
Keep owners and dates on every file
Review live incidents, not just policy text

If you already did this: replace static binders with one living system register and incident log.

Policy debate in Washington also shows why founders should avoid betting on easy preemption or delayed scrutiny. Coverage from Let’s Data Science on the bipartisan AI bill draft highlights how model development and AI use may be treated differently. Founders need product-specific controls, not broad political assumptions.

How should startups measure success?

You do not need a giant dashboard on day one. You do need a few numbers that reveal whether your system is real or fake.

Foundational metrics to track first

Share of production AI features documented in the system register
Share of sensitive flows with human review
Average complaint response time
Appeal resolution time
Number of undocumented product changes affecting AI or moderation
Revenue concentration in top platform channel
Enterprise procurement questions answered without escalation

Advanced metrics to add after three months

False positive and false negative rates in sensitive AI workflows
Override rate by reviewers
Repeat complaint rate by issue type
Seller verification completion rate for marketplace functions
Time from feature concept to classification decision
Contract win rate where trust documentation was requested

What should be on your founder dashboard?

Live overview of documented features and exposed flows
Weekly trend view for complaints, appeals, and overrides
Channel concentration view by revenue and traffic
Alert triggers for undocumented production changes
Export-ready summaries for investors and enterprise buyers

Use a simple stack if needed. A spreadsheet, ticketing tool, shared drive, and product log can be enough early on. The point is not software prestige. The point is traceability.

What should different startup stages do?

Pre-seed and seed stage

Your reality: tiny team, uncertain market, fast pivots, almost no legal budget.

Document features before scaling them
Avoid sensitive AI use cases unless the business case is very clear
Build a complaint and human-review path early

Prioritize: classification, notices, logs, and channel diversification.

Defer: fancy tooling and oversized policy manuals.

Resource need: founder-led, 2 to 4 hours weekly after the initial setup.

Success looks like: you can explain every sensitive feature and answer buyer questions without guessing.

Series A stage

Your reality: growth pressure, product-team expansion, bigger customers, procurement friction.

Formalize owner roles across product, engineering, support, and legal help
Standardize review flows and evidence storage
Stress-test platform dependence and procurement responses

Prioritize: repeatable processes and audit-friendly records.

Defer: edge-case theory that does not touch revenue or user harm.

Resource need: cross-functional monthly review plus one clear internal owner.

Success looks like: fewer sales delays and fewer product reversals.

Series B and beyond

Your reality: market traction, multi-country exposure, bigger partner demands, more channels and teams.

Expand system registers into formal control libraries
Run internal reviews by market and product line
Prepare for more formal audits, public reporting, and partner scrutiny

Prioritize: consistency across products and jurisdictions.

Defer: nothing material. At this stage, sloppy evidence becomes expensive.

Resource need: dedicated owner plus regular external legal input.

Success looks like: market expansion without constant rule-related rework.

What is my blunt founder view on 2026 regulation?

Here it is. A lot of founders still treat regulation as a branding problem. They want the right words on the site, a policy PDF in the data room, and maybe a consultant who can make the deck look calm. That is cosmetic.

The better founders are treating regulation as a product-design discipline. They are asking:

What exactly does the system do?
What evidence can we keep without slowing the team?
Where can harm happen?
Who can appeal?
How dependent are we on gatekeepers?
Can a tired support person explain this feature in one minute?

That mindset wins. It wins because it is operational, not performative. It also matches my broader rule for startup education and startup building: learning must be experiential and slightly uncomfortable. If your compliance setup feels too neat and too safe, it probably has not been tested against real product decisions.

Even broader public debate reflects the tension between speed and control. Coverage from VitalLaw on AI policy risks raised before a House subcommittee shows that governments are wrestling with the same founder dilemma: move fast, but do not create systems you cannot contain. Startups should not wait for perfect political certainty. They need practical internal discipline now.

What should you do next?

Week 1

List all AI features, user-content areas, and platform dependencies
Assign one founder-level owner
Mark your top five exposures by revenue or harm

Week 2

Create your system register
Write plain-language notices for sensitive flows
Set up one complaint and appeal process

Week 3

Add logging for model and policy changes
Define human-review triggers
Measure channel concentration

Week 4 and after

Run monthly founder reviews
Fix repeated complaint patterns
Prepare investor- and buyer-ready evidence packs
Reduce dependence on any single gatekeeper

Glossary of terms founders should know

AI Act: EU law that regulates artificial intelligence systems by risk and use context.

DSA: Digital Services Act, the EU rule set for online intermediary and platform duties, including content and marketplace responsibilities.

DMA: Digital Markets Act, the EU rule set aimed at very large digital gatekeepers and market fairness.

High-risk AI system: an AI system used in a context where legal duties become stricter because the impact on people or safety can be serious.

Human oversight: a real human ability to review, intervene, override, or stop an automated process.

Notice-and-action: a process for receiving reports about illegal or problematic content and acting on them.

Gatekeeper: a very large digital platform with strong control over access between business users and end users.

System register: a live internal record of features, data, outputs, owners, notices, and review status.

Final takeaways

Regulatory Trends 2026: AI Act, DSA, and DMA Impact on Startups matters because these rules now shape product design, distribution, trust, and fundraising.
The right founder path is simple: inventory, classify, document, review, and reduce dependence.
Seed-stage teams should focus on a lean system register, notices, human review, and platform-risk mapping.
Series A and later teams need repeatable records, clearer owner roles, and better procurement readiness.
The startups that do this well will usually move faster because they waste less time on panic, rework, and investor doubt.

If you want one sentence to remember, keep this one: in 2026, compliance is not a side file, it is part of the product.

FAQ

How should founders decide whether a feature needs legal review before launch?

Use a simple trigger system during product planning. If a feature ranks people, affects access, moderates user content, or depends heavily on a major platform, review it before release. Add this check to sprint planning so compliance happens early, not during an emergency.

What is the biggest hidden cost of ignoring the AI Act, DSA, or DMA?

The biggest cost is usually operational drag, not fines. Teams lose time in enterprise procurement, investor diligence, support escalations, and product rework. Startups that document decisions, notices, and review flows early tend to close deals faster and avoid expensive internal confusion later.

Can a very small startup stay compliant without hiring a full legal team?

Yes, if the system is lean. One founder can own classification, one technical lead can own logs, and one support or ops person can own complaints and appeals. A small internal workflow beats a large unused policy binder every time for early-stage European startups.

How do these EU rules affect startups that are not based in Europe?

If you sell into Europe, serve EU users, depend on EU platform access, or process user content there, these rules can still shape your operations. The practical question is market exposure, not just company location. That is why the European startup playbook is useful for planning expansion.

What should founders ask vendors or AI providers before integrating their tools?

Ask what model is being used, how outputs are logged, what safety controls exist, how updates are communicated, and whether human override is possible. Also ask who carries responsibility if the feature creates harm. Vendor opacity becomes your problem once the feature reaches users.

How can startups make moderation and appeals manageable without building a giant trust team?

Start with narrow workflows. Define what users can report, who reviews complaints, what response times apply, and how decisions are recorded. Most early teams do not need scale first; they need consistency. Simple queues, templates, and escalation rules are usually enough to begin.

Does DMA reduce the risk of relying on app stores, search, or large platforms?

Not enough to justify complacency. DMA may improve fairness, but it does not remove dependency risk. Founders should still track revenue concentration, preserve evidence of harmful platform changes, and build owned channels like email, partnerships, or direct sales before distribution becomes unstable.

How can AI-heavy startups balance speed with documentation in 2026?

Treat documentation as a product asset, not a legal chore. Keep a live register of models, prompts, outputs, owners, and review dates. If your team is adding automations quickly, the AI automation guide helps frame how speed and operational control can work together.

What do investors and enterprise buyers usually want to see first?

They usually want evidence that your team understands risk in practice. Expect questions on feature inventory, human oversight, user notices, complaint handling, model changes, and platform dependence. A founder who can answer clearly signals stronger execution than one hiding behind vague policy language.

Which startup models should be most cautious in 2026?

HR tech, fintech, health tools, marketplaces, creator platforms, and SaaS products with AI copilots should be especially careful. These models often mix automated decisions, hosted content, and gatekeeper dependence. The higher the user impact, the more important it is to classify, explain, and review.

Violetta Bonenkamp

Violetta Bonenkamp, also known as Mean CEO, is a female entrepreneur and an experienced startup founder, bootstrapping her startups. She has an impressive educational background including an MBA and four other higher education degrees. She has over 20 years of work experience across multiple countries, including 10 years as a solopreneur and serial entrepreneur. Throughout her startup experience she has applied for multiple startup grants at the EU level, in the Netherlands and Malta, and her startups received quite a few of those. She’s been living, studying and working in many countries around the globe and her extensive multicultural experience has influenced her immensely. Constantly learning new things, like AI, SEO, zero code, code, etc. and scaling her businesses through smart systems.

Regulatory Trends 2026: AI Act, DSA, and DMA Impact on Startups | Ultimate Guide For Startups | 2026 EDITION