Iuliia Gorshkova

When you start digging into the regulatory landscape for artificial intelligence, the immediate instinct is to look for a single, monolithic law—a "GDPR for AI," if you will. But in the antipodes, specifically in Australia and New Zealand, that search comes up empty. Instead, you find something far more intricate: a patchwork of existing legal [...]

Canada’s approach to regulating artificial intelligence has emerged as a distinct middle path, navigating the chasm between the European Union’s comprehensive, risk-based legislation and the United States’ fragmented, enforcement-driven landscape. The Artificial Intelligence and Data Act, or AIDA, represents Ottawa’s attempt to future-proof governance without stifling the rapid innovation occurring in hubs like Toronto-Waterloo and [...]

When I first started digging into the regulatory frameworks governing artificial intelligence, I found myself constantly mapping the landscape back to my own experiences writing code. You know the feeling: you’re staring at a complex system, trying to understand its architecture, its dependencies, and its failure modes. Regulatory frameworks are no different. They are systems [...]

It's a peculiar paradox we find ourselves grappling with in the northern reaches of Europe. On one hand, the Nordic region is often hailed as a cradle of technological innovation—home to tech giants like Spotify and a vibrant ecosystem of startups tackling everything from fintech to green energy. Yet, when the conversation turns to the [...]

When discussing the trajectory of artificial intelligence, the conversation often orbits around the colossal regulatory frameworks of the European Union, the aggressive commercial expansion of Silicon Valley, or the state-driven strategies of China. Yet, tucked into the northern reaches of Europe, the Nordic bloc—Sweden, Finland, Denmark, and Norway—presents a fascinating, somewhat paradoxical case study. On [...]

When founders in Madrid or Barcelona pitch their latest AI-driven solution, they often rehearse their technical architecture, market fit, and burn rate with religious precision. Yet, when the conversation drifts to the regulatory landscape, a palpable vagueness settles in. It’s a curious blind spot, particularly given that the European Union’s Artificial Intelligence Act (AI Act) [...]

If you’re building an AI company in Madrid, Barcelona, Milan, or Lisbon, the regulatory landscape feels different than it does in Berlin or Amsterdam. It’s not just about the text of the European Union’s AI Act—it’s about how local bureaucracies interpret compliance, how labor courts treat algorithmic management, and how regional grants subtly shape your [...]

There’s a certain kind of shift that happens in the developer experience that isn’t immediately obvious from a changelog. It’s not about a new feature or a faster compiler; it’s about where the center of gravity lies when you’re building software. For decades, that gravity was anchored firmly to the text editor, the file system, [...]

The discourse surrounding prompt engineering has, for the last couple of years, taken on the proportions of a gold rush. You have seen the headlines, the boot camps promising six-figure salaries in weeks, and the LinkedIn profiles suddenly blooming with "Certified Prompt Engineers." It feels like a new frontier, a specialized discipline emerging from the [...]

There’s a specific moment in every engineering career when the toolchain shifts beneath your feet. Maybe it was the transition from manual memory management to garbage collection, or from on-prem servers to the cloud. For many of us in the field right now, that tectonic shift is happening again, but this time it’s not just [...]

Investing in artificial intelligence feels like standing at the edge of a gold rush, but the tools for panning are shrouded in marketing fog. Every week brings a new press release promising to "revolutionize" an industry with proprietary algorithms that sound impressive yet reveal little upon inspection. For an investor, the challenge isn't just picking [...]

Many founders I speak with imagine artificial intelligence as a monolithic entity—a magical box they can point at a problem and watch solutions materialize. They hear about startups raising millions on the promise of "AI-powered" efficiency and feel a mix of excitement and pressure to integrate it into their own ventures. Yet, when they approach [...]

Building AI products feels a bit like navigating through fog. You can see the destination, but the instruments you rely on—traditional software metrics—often give misleading readings. We’ve all been there: the model achieves 98% accuracy on the validation set, the deployment pipeline is green, and the stakeholders are eager. Yet, three weeks post-launch, the product [...]

The first time I truly understood the gap between AI performance and accountability was in a quiet lab at 2 AM. We had built a reinforcement learning agent to manage a small robotic arm sorting delicate components. For weeks, its performance metrics were flawless—99.98% accuracy. Then, one night, it dropped a component, not because of [...]

Every week, a new AI agent hits the headlines, promising to revolutionize workflows, book appointments, and even write code autonomously. The marketing materials are slick, the demos are mesmerizing, and the underlying architecture is often, upon closer inspection, a glorified state machine wrapped in a loop. We are currently living through a phase of AI [...]

When I first started building multi-agent systems, I remember staring at a wall of Python scripts, each trying to orchestrate a conversation between different LLM instances. It was messy. I had loops within loops, brittle state management, and debugging felt like deciphering a spiderweb. We’ve come a long way since those early, ad-hoc days. Today, [...]

The conversation around artificial intelligence often feels dominated by two extremes: the breathless hype of market analysts predicting trillions in value, and the existential dread of philosophers warning of rogue superintelligence. While both perspectives capture headlines, they obscure the messy, pragmatic reality faced by the engineers and founders actually building these systems. For the startup [...]

Anyone who has spent time in the trenches of software engineering knows the brutal gap between specification and implementation. We draft elegant architectures, define strict interfaces, and then watch as the system interacts with a chaotic environment, producing emergent behaviors that were neither predicted nor desired. In traditional software, we call this "technical debt" or [...]

There's a peculiar tension in the air whenever I sit down with a team of brilliant engineers to discuss AI safety. It's not skepticism, exactly. It's more like a collective holding of breath. We've built systems that can reason, generate, and predict with capabilities that feel almost alien, yet we're still deploying them using workflows [...]

For decades, the narrative surrounding artificial intelligence and professional labor has been dominated by a binary choice: either AI replaces human expertise, or it serves as a mere tool for efficiency. This dichotomy, however, fails to capture the nuanced reality unfolding in fields ranging from radiology to software engineering. The true transformation lies not in [...]

The conversation around Artificial General Intelligence (AGI) often feels like a collision between science fiction and quarterly earnings calls. On one side, you have the existential pondering of philosophers and futurists; on the other, the relentless drive of venture capital seeking exponential returns. Somewhere in the middle sits the engineer—the person actually tasked with building [...]

When we talk about the next decade in artificial intelligence, we're not just projecting the current trajectory of scaling laws onto a future timeline. That’s a rookie mistake, the kind that leads to linear extrapolations of exponential growth, which almost always miss the emergent properties that truly redefine a technology. The next ten years will [...]

Five years feels simultaneously like a lifetime and a blink in the world of artificial intelligence. Looking back from 2029, we will likely see the period between 2024 and 2029 not as a series of incremental updates, but as the era where AI transitioned from a fascinating novelty to a fundamental utility woven into the [...]

The conversation around artificial intelligence often orbits the gleaming satellites of capability: parameter counts, benchmark scores, and the uncanny valley of generative outputs. We discuss model architectures as if they exist in a vacuum, ethereal algorithms floating in the cloud. But there is no cloud. There is only a vast, sprawling network of physical infrastructure—silicon, [...]

When we talk about the economics of artificial intelligence, the conversation almost invariably gravitates toward the eye-watering sums spent on training large models. Headlines announce multi-million or even billion-dollar training runs, painting a picture of an industry where the deepest pockets win. While training costs are undeniably significant, they represent only a fraction of the [...]

There’s a pervasive myth in the technology sector, particularly within the AI community, that open-source software functions as a business strategy. It is often romanticized as a democratic force that inevitably outcompetes proprietary models through sheer collective momentum. While open-source has undeniably been the engine of modern computing—powering everything from the Linux kernel to the [...]

The concept of a "moat," popularized by Warren Buffett to describe a company's durable competitive advantage, takes on a fascinatingly complex dimension in the artificial intelligence landscape. Unlike traditional software businesses where the primary moat might be network effects or switching costs, AI companies derive their defensibility from a triad of resources: proprietary data, accumulated [...]