Poland has lengthy been generally known as one in every of Europe’s strongest software program improvement and IT outsourcing hubs, supplying engineering expertise and enterprise supply capabilities to firms throughout Western Europe and the US.
Whereas nations like Germany centered extra closely on fintech or SaaS merchandise, Poland carved out its personal service area of interest. As AI reshapes how software program is constructed, that legacy is popping right into a strategic benefit.
Miquido is a Polish software program improvement and AI options firm headquartered in Kraków. Based in 2011, the corporate builds cellular apps, internet platforms, AI techniques, and enterprise software program for startups, scaleups, and enormous organisations throughout industries reminiscent of fintech, healthcare, ecommerce, and leisure.
Initially recognized for cellular improvement, Miquido has more and more centered on AI-native software program improvement, integrating generative AI and agentic AI workflows into enterprise environments.
I spoke to CEO Jerzy Biernacki to study extra concerning the path and alternatives for Poland and the rise of what he calls Software program 3.0.
For Biernacki, Poland’s greatest power is its expertise pool, which is quickly embracing AI instruments and constructing processes round them to ship extra, sooner, with out sacrificing high quality.
On the identical time, whereas Poland just isn’t but one of many high ecosystems in Europe, firms like ElevenLabs and DocPlanner are serving to lead that cost with huge funding rounds. Particularly in AI, Poland now has two independently developed giant language fashions — BIELIK and PLLuM. That is uncommon, particularly for Central Europe and even throughout the EU extra broadly.
Poland is alos residence to a number of top-tier software program improvement businesses — together with Miquido, in addition to firms like Netguru, 10Clouds, and Spyrosoft.
From copilots to Agentic AI
The rise of huge language fashions has already pushed software program improvement by way of a number of distinct phases, in response to Biernacki. Within the early days of the AI growth, many believed builders would shortly get replaced altogether as firms rushed to undertake generative AI instruments.
“The primary section was: ‘LLMs can be utilized in all places, they usually’ll exchange everybody,’ notably programmers and builders,” he stated.
That narrative coincided with main layoffs throughout the tech sector, though Biernacki argues lots of these cuts have been extra carefully tied to firms correcting pandemic-era overhiring than AI-driven alternative.
Initially, AI coding instruments primarily functioned as copilots, serving to builders autocomplete code and speed up workflows inside their IDEs. However Biernacki says the market underwent a significant shift in 2025 with the arrival of agentic AI coding techniques.
“I believe the most important breakthrough was when Claude Code was launched in Might final 12 months. That was a turning level,” he defined.
“Agentic AI code technology actually grew to become a factor, and it modified the way in which we work with software program improvement.”
The later launch of Codex additional accelerated the transition. Based on Biernacki, the 2 platforms now dominate a lot of the enterprise AI coding market.
“These two instruments mainly overtook the market. They now have one thing like 75–80 per cent of the enterprise phase between them,” he stated.
The shift is reshaping excess of coding itself. Biernacki argues that AI is reworking your entire software program improvement lifecycle, from necessities evaluation and design to testing, deployment, governance, and validation.
How Software program 3.0 Is reshaping software program improvement
As these workflows turn out to be more and more autonomous, Biernacki argues the position of the developer itself is basically altering, evolving towards oversight and verification fairly than handbook coding.
“The majority of the work is not writing code — as a result of writing code is now comparatively low-cost — however verification, structure, governance, and validation.”
He believes automated validation will turn out to be probably the most crucial layers in AI-assisted software program improvement as enterprises look to securely deploy more and more autonomous coding techniques.
This has led to a shift in native hiring practices, with firms like Miquido putting higher emphasis on tender expertise than on whether or not somebody is aware of a language’s syntax completely.
By way of youthful hires, Biernacki characterises the significance of college graduates who used AI all through their research and are naturally comfy working with AI. He admits youthful hires nonetheless want time to adapt to enterprise environments and operational self-discipline.
“We search for a distinct ability set now: individuals who can translate enterprise necessities into prompts and who can work naturally with AI brokers.
Additionally they inject recent power into groups. Generally senior engineers get caught in current processes, and having recent blood helps present other ways of working. They’re wanting to study, wanting to adapt, keen to check new issues. So I believe they’re a extremely invaluable addition to firm buildings.”
“Software program 3.0 “creates a brand new divide between startups and enterprises
The divide between startups and enterprises is turning into more and more seen within the AI-driven software program period as firms undertake to Software program 3.0 improvement workflows.
Based on Biernacki, startups are transferring far sooner than bigger organisations as a result of they’re being compelled to undertake AI-native improvement practices to outlive:
“Actually, I believe startups are main the cost proper now. Startups want to point out traders tangible outcomes extraordinarily shortly throughout software program sectors.
They will’t afford to not depend on AI-driven coding workflows — particularly for early merchandise and have improvement.”
Because of this, startups are delivery merchandise and options at an more and more quick tempo. Enterprises, nonetheless, function beneath very totally different constraints.
“Startups don’t have to fret as deeply about enterprise-grade safety, status threat, or huge scalability from day one,” stated Biernacki.
“Enterprises have to maneuver extra fastidiously as a result of they want stability.
Their main issues are status, compliance, safety, and governance.”
For software program improvement corporations serving enterprise prospects, reliability due to this fact turns into the crucial differentiator.
“You’ll be able to’t obtain that with pure ‘vibe coding’ and no safeguards. You want enterprise processes making certain high quality.”
Biernacki contends that the entire ecosystem is transferring from “we offer builders” towards offering augmented supply techniques.
“I believe the adoption price in Poland might be among the many highest in Europe.”
He sees Poland’s edge just isn’t about being the largest AI market, as a result of clearly it isn’t, however fairly having an ecosystem of software program firms that spent 10–15 years constructing enterprise software program for Western European and international shoppers — and are actually quickly retooling round AI.
“The factor Poland may very well be main in isn’t frontier AI analysis itself, however AI-augmented enterprise supply. That’s the place I believe the nation’s actual worth lies proper now.”
Biernacki believes the software program business has now crossed a significant inflection level, with agentic AI basically reshaping how software program is constructed and maintained, and resulting in a aggressive benefit.
“Firms like us have a brief benefit over firms that aren’t aggressively adopting agentic AI workflows but.
Although I believe that benefit will ultimately slim as others catch up.”
On the identical time, enterprises are dealing with quickly rising regulatory and compliance complexity round AI techniques and software program governance.
“There’s additionally the rising complexity round regulation and compliance in enterprise software program,” stated Biernacki.
He argues that serving to enterprises navigate this more and more sophisticated panorama represents one of many greatest rising alternatives for software program firms. Biernacki additionally believes the business is experiencing a type of Jevons Paradox, the place making software program improvement cheaper by way of AI truly will increase general demand for software program fairly than decreasing it.
The long-term winners within the AI software program period is not going to essentially be the businesses producing probably the most code, however these able to redesigning their organisations round governance, reliability, and operational high quality.
“The winners will likely be firms that redesign their processes round this new actuality — together with governance, compliance, and enterprise reliability — not the businesses writing probably the most code or just ‘vibe coding,’” he stated.
Startups to observe:
AIstats
AIstats is a soccer analytics startup that makes use of AI, laptop imaginative and prescient, and machine studying to analyse soccer matches from normal video footage. Its expertise reconstructs video games in 3D, tracks participant and ball motion, and generates superior tactical and efficiency insights with out requiring stadium sensors or wearable units.
The platform is designed to assist golf equipment, scouts, and brokers higher perceive participant behaviour, workforce dynamics, decision-making, and tactical patterns by way of automated AI-driven evaluation. The corporate additionally operates a consumer-facing soccer app that delivers dwell scores, predictive analytics, AI-generated match insights, and efficiency metrics throughout 1000’s of leagues worldwide.
Carein
Carein develops dietary dietary supplements designed to enhance pores and skin and hair well being from the within fairly than relying solely on topical skincare.
Based in 2021, the corporate targets points reminiscent of zits, hormonal zits, redness, pigmentation, ageing pores and skin, and hormonal hair loss.
Its merchandise mix nutritional vitamins, minerals, probiotics, plant extracts, adaptogens, and different energetic substances to assist pores and skin regeneration, hormonal steadiness, and the gut-skin connection. It really works with medical consultants to formulate its merchandise and markets itself as a science-led premium skincare wellness model.
DefendEye
DefendEye is a defencetech startup growing absolutely autonomous, AI-powered “search drones” designed for speedy intelligence, surveillance, and reconnaissance (ISR) missions.
Based in 2023 and headquartered in Kraków, the corporate builds tube-launched drones that may deploy in beneath 10 seconds with out requiring a pilot, joystick, or conventional drone coaching. Customers merely launch the drone from a transportable or mounted tube, and the onboard AI autonomously navigates, identifies people, tracks motion, and streams encrypted dwell video again to a cloud command centre in actual time.
The drones are constructed to function even in GPS-denied or jammed environments and embrace low-light and night-vision capabilities to be used in troublesome battlefield or emergency circumstances. The startup is positioning itself throughout the rising “drone as first responder” and autonomous defence techniques market, the place speedy situational consciousness is turning into more and more necessary for each navy and civil safety operations.
FormalFoundry.ai
FormalFoundry.ai is an AI governance and verification startup growing mathematically traceable AI techniques and compliance tooling. It builds a verification layer for AI techniques utilizing formal strategies and proof assistants, so firms could make AI outputs extra dependable, auditable, and compliant.
This helps groups flip area guidelines, laws, and professional information into machine-checkable logic to cut back hallucinations and errors.
Graphcode
Graftcode is a Polish software program infrastructure startup tackling the big period of time builders spend constructing and sustaining integrations between companies, languages, and infrastructure layers.
Based on Graftcode, engineering groups typically lose 30–40 per cent of improvement time to API upkeep, DTO mapping, queues, versioning, and associated backend complexity.
Its platform goals to take away that overhead by making a unified communication layer between functions, enabling sooner improvement, cleaner architectures, and simpler scaling for distributed techniques and AI-native software program environments.
As a substitute of constructing separate companies related by way of conventional backend plumbing, builders can name strategies throughout techniques as if all the pieces have been in a single shared codebase.