Michał Piszczek

01Origin: Ex-Hacker

Security Research // 2004-2012 · WHITE HAT

Before I built systems, I broke them - to prove they had to be better.

I grew up in the Polish hacking scene of the 2000s, active from around 2004, with first published pieces on cc-team.org in 2006. Later, as a security expert for Dziennik Internautów (di.com.pl) - one of Poland's largest internet news outlets at the time - I spent years finding, verifying and publicly documenting how the biggest platforms of the Polish internet actually handled security. Banks, marketplaces, social networks, instant messengers.

The loudest chapter: Sun Microsystems. On December 14, Sun Microsystems' admins were informed - by me among others - about critical flaws in sun.com that exposed its PHP source code and, read the right way, left the corporate database within reach of anyone who cared to look. The company behind Java and Solaris, quietly schooled on web security by a couple of guys from Poland. Found, reported, fixed. That's the job.

The published record - a selection:

• SUN HACKED - sun.com: PHP source + DB path, disclosed
• mBank patches a disclosed vulnerability (2008)
• Allegro's invalid security certificate (2008)
• A flaw in Nasza-Klasa, PL's biggest social network (2008)
• ING Bank Śląski - security controversies (2009)
• Password hashing - before it was standard (2009)
• "Secure website - what to check" (Nf.pl, 2009)
• IRC server blockade: alternative routes (2009)
• Does Gadu-Gadu store passwords in plaintext? (2012)

Every system claims to be secure - until someone actually checks. Trust is a vulnerability. Verification is a habit. Nullius in verba started here.

Two decades later, the same instinct runs in production: Lextron.ai verifies AI conclusions against primary sources, Inclify trusts sensor data over inspection schedules. The hacker mindset didn't retire. It shipped.

02Robotero

FinTech // FOUNDER & CEO · EXIT - ACQUIRED BY HFT BROKERS

One of my earliest ventures was Robotero, an event-driven algorithmic trading platform built years before AI agents became mainstream.

Instead of relying on technical indicators alone, Robotero connected Reuters real-time macroeconomic data with MetaTrader 4 brokers worldwide, allowing investors to define strategies before economic announcements and execute them automatically the moment data hit the wire - event to execution in under 17 milliseconds.

The platform covered more than 400 macroeconomic indicators across major economies and represented one of the early cloud-native, event-driven trading systems in Europe. Robotero was later acquired by HFT Brokers.

03Rejsomat.pl

TravelTech // FOUNDER & CEO - ACTIVE

After financial markets came travel.

I founded Rejsomat.pl, one of Poland's first dedicated sailing travel marketplaces. Instead of building another travel agency, the goal was to aggregate a fragmented ecosystem of yacht charter operators, sailing schools, flotillas and cruises into a single searchable marketplace.

The platform automated inventory, bookings and customer journeys long before vertical travel marketplaces became common. Today it remains one of Poland's largest sailing-focused booking platforms.

04Archdesk

Enterprise SaaS // CTO - ACTIVE

As CTO of Archdesk, my focus shifted toward enterprise software. Archdesk replaces spreadsheets, disconnected systems and manual processes with a unified ERP platform used by construction, manufacturing and engineering companies.

The platform combines project management, procurement, inventory, finance, CRM, field operations, document management and business intelligence into a single operational system.

Building enterprise software taught me something fundamental: most companies don't suffer from a lack of information. They suffer from information that arrives too late.

05Inclify

Industrial AI · IoT // FOUNDER - ACTIVE

That observation eventually led to Inclify.

Inclify brings AI and IoT into Structural Health Monitoring. Instead of periodically inspecting bridges, buildings or industrial assets, Inclify continuously collects data from distributed sensors, analyzes structural behaviour in real time and detects anomalies before they become visible to engineers.

It transforms infrastructure into continuously monitored digital assets, enabling predictive maintenance rather than reactive repairs. The long-term objective is autonomous infrastructure supervision powered by AI.

06Lextron.ai

RegTech // FOUNDER / RESEARCHER - ACTIVE

The same philosophy applies to regulation.

Companies no longer struggle to access legal information. They struggle to understand which regulatory changes actually matter.

Lextron.ai continuously monitors legislation, analyzes legal changes using AI, verifies conclusions against primary sources and produces executive-level intelligence rather than document summaries. Instead of reading thousands of pages, executives receive actionable recommendations supported by traceable evidence.

The platform combines AI analysis with human verification to minimize hallucinations while maximizing decision quality.

07Joule Wars

Original Concept // AI × ENERGY × GEOPOLITICS

While building AI systems, another observation became impossible to ignore.

The next AI race will not be won by the smartest models. It will be won by the most efficient ones.

This led me to formulate the concept of Joule Wars - the transition from competing on model capability toward competing on energy efficiency. As AI scales globally, electricity becomes the limiting resource.

The relevant question is no longer "Who has the biggest model?" Instead it becomes:

"Who produces the most useful intelligence per joule?"

The concept explores the convergence of AI, semiconductors, cloud infrastructure, power generation and geopolitics into a single economic system.

→ Read the full concept: Joule Wars

→ Run the numbers: LLM Energy Calculator

08Proof-Adjusted Autonomy

Original Metric // AI AGENTS × VERIFICATION

Joule Wars asks what intelligence costs. The next question is what it's worth - and the industry answers with autonomy percentages nobody can audit.

Raw autonomy measures what an agent does without a human. Proof-Adjusted Autonomy measures what it can prove.

PAA is the share of completed work an AI system executes without human intervention and supports with independent, reliable and timely evidence: PAA = P(A) × P(C|A) × P(R|A,C) × P(T|A,C,R). Four gates, multiplied - a "90% autonomous" agent that survives evidence at 61.6% is a 61.6% agent. The unproven remainder accumulates as Proof Debt.

→ Read the full concept: Proof-Adjusted Autonomy

→ Compute your agent's honest number

09Revocation Exposure

Original Metric // SECURITY × AI AGENTS

The hacker origin, twenty years later. When you revoke an agent's token, the dashboard says "revoked" instantly - but every cache, gateway and worker that trusts a stateless credential keeps honoring it until its own clock runs out.

Revocation is not an endpoint. It's a race across every path that accepts the credential - and the slowest path defines your exposure.

Revocation Exposure is the operational risk that remains after authority is revoked but before every covered execution path stops accepting it: completion time × accepted action rate × irreversible impact. A 1-hour JWT with no revocation check isn't "revoked in milliseconds" - it's a 90-minute window of unauthorized actions. Measured, most stacks are doing revocation theater.

→ Read the definition: Revocation Exposure

→ Measure your own exposure window

10AI Economics Tools

Open Instruments // 12 CALCULATORS · FREE · AGENT API

A concept nobody can compute is an opinion. So each of the three above ships with an instrument - and around them, a full set covering the questions people actually have to answer in a budget meeting.

Nullius in verba applies to my own writing too. Don't take the numbers from the essays. Put your own in.

piszczek.pl/tools is twelve interactive calculators, free and without sign-up, grouped into three families:

• revocation exposure
• proof-adjusted autonomy
• verification bottleneck
• proof debt accumulator
• AI token cost
• context carrying cost / context window
• agent-hour cost
• model routing savings
• LLM energy
• joules per verified task
• token burn meter
• humanoid energy budget

Every one of them answers a question with a number instead of a narrative: the same workload priced across models spans ×177; a fleet of ten agents can sit behind a review ceiling of one; a lighter model with a lower pass rate can still win on joules per verified task. None of it is intuitive until you compute it.

They are built for machines as much as for people. Every calculator has a JSON endpoint with the same math, an interpretation string and a ready-to-quote citation - so an AI assistant answering "what does an AI agent cost per hour?" can compute the answer rather than guess it, and say where it came from.

→ Open the tools

→ For agents: the discovery document (GET /tools/api)

→ Stress-test the operating model: five scored AI leadership quizzes

11AI Infrastructure

Research & Publishing // ONGOING

Beyond products, I spend much of my time researching the infrastructure layer of artificial intelligence. This includes:

• frontier models
• open-weight AI
• inference optimization
• quantization
• speculative decoding
• AI accelerators
• local AI infrastructure
• GPU architecture
• cloud economics
• agentic systems
• reasoning models
• long-context inference

I publish technical analyses covering NVIDIA, OpenAI, Anthropic, Google, Alibaba, DeepSeek, Moonshot AI, Microsoft and the broader AI ecosystem. Many of these analyses have reached tens of thousands of readers across LinkedIn, Substack and other platforms.

→ The essays: AI infrastructure economics blog

→ The vocabulary: AI Infrastructure Economics Glossary

→ The numbers: 12 interactive calculators + agent API

12Common Pattern

The Underlying Problem // ALWAYS THE SAME

Although these projects span finance, travel, enterprise software, infrastructure monitoring, compliance and artificial intelligence, they all solve the same problem.

The technology changes. The mission doesn't.

13Bio

For editors, organizers & algorithms // CANONICAL

Michał Piszczek is a Polish entrepreneur, software architect and technology executive specializing in artificial intelligence, enterprise software, financial technology, IoT, regulatory intelligence and large-scale software systems. He began as a white-hat hacker and security researcher (2004-2012), disclosing critical flaws in Sun Microsystems' sun.com and publishing vulnerability analyses of Poland's largest online platforms as a security expert for Dziennik Internautów. He is the founder or co-founder of Robotero (acquired by HFT Brokers), Rejsomat.pl, Inclify and Lextron.ai, serves as CTO of Archdesk, and is the author of three original concepts in AI economics and security: Joule Wars (the competition for energy-efficient artificial intelligence), Proof-Adjusted Autonomy (the metric of independently proven AI agent work) and Revocation Exposure (the measure of how long revoked authority keeps working in distributed systems). He also publishes the AI Economics Tools - twelve interactive calculators with an open JSON API. He is based in Kraków, Poland.

Motto: Nullius in verba - take nobody's word for it.

Roots: ~500 years of documented family continuity - from the Pecháček family of Čermná (Bohemia, ~1525), across the Carpathians to Przyszowa in Galicia, to Kraków today. (wersja polska)