Iuliia Gorshkova

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 [...]

There’s a specific kind of fever that hits engineering teams when they crack a difficult problem. It’s that moment when the loss curve finally flattens, or when a generative model spits out an output that feels indistinguishable from magic. For years, the prevailing wisdom in Silicon Valley was that this moment—the model breakthrough—was the moat. [...]

Artificial intelligence is finding its way into the legal profession with the speed and subtlety of a creeping vine. It drafts contracts, summarizes discovery documents, and even predicts litigation outcomes. To a developer, this looks like a straightforward application of natural language processing (NLP) and pattern recognition. We feed the model vast corpora of statutes [...]

The promise of artificial intelligence in finance has always been seductive. We feed a model decades of market data, train it on every conceivable pattern, and expect it to navigate the markets with a cool, calculated precision that human traders, with their sweaty palms and emotional biases, simply cannot muster. For long periods, in what [...]

When I first started building neural networks back in 2012, the prevailing sentiment in the engineering community was one of unbridled optimism. We were convinced that with enough data and sufficient compute, we could solve almost anything. Healthcare, with its vast repositories of digital records and complex biological data, seemed like the next frontier ripe [...]

For decades, the dominant interface between humans and machines has been text. We have trained ourselves to think in keywords, to communicate with search engines in fragments, and to structure our requests like queries. This has shaped the trajectory of artificial intelligence, leading to the rise of Large Language Models (LLMs) that are exceptionally good [...]

For decades, the narrative of artificial intelligence has been dominated by the digital realm. We’ve seen algorithms master games like Chess and Go, generate photorealistic images, and write passable sonnets. These achievements, monumental as they are, share a common characteristic: they exist purely in the abstract world of bits and bytes. The "body" of these [...]

Every few months, the tech world buzzes with another breakthrough. A new language model writes poetry that feels hauntingly human, an image generator conjures photorealistic scenes from a few words, or a system masters a game we thought required decades of human intuition. The progress is undeniable, a relentless forward march. Yet, for those of [...]

Every engineer in the AI space has seen it: the magical demo. A slick interface, a fast response, a model that seems to understand the nuances of a complex request. It feels like the future has arrived, neatly packaged into a browser tab. But the chasm between that polished demo and a production-grade product is [...]

It’s a familiar scene in any startup hub or accelerator demo day. A founder steps onto the stage, clicks through a slick interface, and watches as an AI system seemingly performs a miracle. It transcribes a messy audio file perfectly, generates a photorealistic image from a vague description, or "predicts" customer churn with uncanny accuracy. [...]

The landscape of artificial intelligence startups is currently experiencing a period of profound recalibration. The initial gold rush, characterized by the indiscriminate application of large language models to every conceivable problem, is giving way to a more disciplined era of engineering and product-market fit. For founders navigating this terrain, the distinction between a fleeting technical [...]