DEV Community: Vitalii Kiro

Vitalii Kiro: AI hasn’t taken your job. But it’s already taking plenty of money

Vitalii Kiro — Tue, 02 Jun 2026 10:52:35 +0000

Sam Altman admitted he was wrong. The visionary who spent years predicting inevitable mass unemployment driven by artificial intelligence recently made a surprising statement at a Commonwealth Bank of Australia conference. He is "delighted to be wrong" about how quickly AI would eliminate jobs. Altman is now pushing back against the white-collar apocalypse that most Silicon Valley experts – including himself – once forecast.

It’s worth asking why the CEO of OpenAI suddenly turned so "pessimistic" about his own industry. The AI hype shows no signs of cooling, and plenty of office workers have already convinced themselves the technology is about to render them obsolete.

A quick terminology check

When media, investors, and managers talk about "artificial intelligence," they almost always mean Large Language Models, or LLMs. This is not intelligence in any meaningful sense – it’s a statistical engine that predicts the next word in a sequence based on trillions of examples scraped from the internet.

ChatGPT, Claude, Gemini – all LLMs. They don’t think, don’t understand, and certainly don’t feel. They generate plausible text. They do it well. And they do it at enormous cost.

True artificial intelligence – capable of learning, setting goals, and acting autonomously in an open-ended environment – remains either science fiction or a very distant prospect. The gap between "very smart autocomplete" and genuine reasoning is vast. AI startup founders tend to avoid this topic, since blurring the distinction makes it considerably easier to raise billion-dollar rounds.

Business is starting to sober up

Reality is beginning to bite. Chaac Pizza Northeast, one of Pizza Hut’s largest franchisees with over 110 locations on the US East Coast, is suing parent company Yum! Brands for $100 million. The franchisee was forced to adopt an AI dispatch system called Dragontail. Instead of speeding up deliveries, it slowed them down. On-time delivery rates dropped from 90% to less than half, and New York sales fell by roughly 10%. "Dragontail did the exact opposite of what it promised," the lawsuit states.

Starbucks, meanwhile, quietly pulled an AI inventory tool from all its North American stores just nine months after launch. The system couldn’t reliably count bottles of syrup. In the promotional video from the launch event, the scanner visibly missed a bottle of peppermint syrup. An omen, in hindsight.

Uber’s COO has publicly acknowledged that AI spending is increasingly hard to justify. "If you’re not actually able to draw a direct line between token usage and useful features shipped to users, that trade becomes harder to justify," he said — after the company burned through its entire annual AI budget in four months.

Microsoft is counting the cost too. In May 2026, the company began revoking Claude Code licenses for engineers across its Experiences & Devices division — the teams that build Windows, Teams, and Surface. The reason was straightforward: token-based billing had exhausted the annual budget well before the year was out.

A company that has invested roughly $13 billion in OpenAI and aggressively champions AI across its product lines couldn’t survive its own bill.

The scale of "optimism"

Research firm Gartner warned back in 2025 that more than 40% of agentic AI projects would be cancelled by the end of 2027, due to escalating costs, unclear business value, and weak returns on investment.

Despite this, Nvidia CEO Jensen Huang used GTC 2026 to propose giving engineers a token budget worth half their annual salary to drive more productive AI use. Under his framework, an engineer earning $500,000 a year should be spending another $250,000 on tokens. "Otherwise I will be deeply alarmed," Huang said.

The industry, meanwhile, keeps moving on momentum. According to Gartner’s latest forecast, global IT and AI spending in 2026 will exceed $6.3 trillion, with the AI sector alone accounting for more than $2.5 trillion. These astronomical figures keep climbing – with no guarantees of a return.

I don’t believe in the apocalypse. But I don’t believe in magic wands either.

AI is a powerful and expensive tool currently passing through what technologists call the "trough of disillusionment" – the phase that follows every peak of inflated expectations. We’ve seen this before: the dot-com crash of the early 2000s, the blockchain craze of 2018 that was going to "replace the banking system any day now."

Altman walking back his predictions, Pizza Hut filing lawsuits, Starbucks returning to hand-counting syrups – none of this is a collapse of the technology. It’s the normal process of a technology growing up.

Dismissing AI entirely would be foolish. LLMs are already delivering real, measurable value to doctors, lawyers, journalists, and engineers. That value is genuine, and the tools arriving in the next few years will be more capable still.

But panic and blind faith are equally unhelpful. While AI hasn’t taken your job, it is reliably taking enormous sums of money from everyone who bought the hype in bulk.

The difference between "artificial intelligence" and a "large language model" is the difference between a mind and a very clever parrot. Both have their uses. But nobody is handing the parrot the keys to the company just yet.

At least not right now.

Vitalii Kiro: The Drone War Is Over. The War of Algorithms Begins

Vitalii Kiro — Wed, 27 May 2026 11:27:30 +0000

When people say that Ukraine has become a “testing ground” for Western technologies, there is some truth to it. But that truth is incomplete — and even offensive. What is happening here has long outgrown the scale of someone else’s experiment. Ukraine is not merely testing borrowed technologies under combat conditions; it is independently creating technologies that will define the nature of future wars and the value of human life for decades to come.

More than 2 million hours — approximately 228 years — of combat footage have already been accumulated by one Ukrainian non-profit organization, which centralized video streams from over 15,000 frontline drone operators. Two million hours of real warfare — dust, smoke, camouflaged tanks, shattered roads, night attacks — are being transformed into one of the world’s largest and most unique combat datasets. And it is on this material that Ukraine is training its artificial intelligence.

Numbers That Change the Understanding of War

Let us begin with accuracy. According to estimates from the Center for Strategic and International Studies (CSIS) and Ukrainian developers, autonomous navigation and AI-assisted targeting can increase the effectiveness of FPV drones from 10–50% to 70–80%. The exact result depends on battlefield conditions and electronic warfare systems.
These are not theoretical assumptions but real frontline data confirmed in CSIS reports.

Yet dry numbers are only the tip of the iceberg. The real breakthroughs lie in the details.
The “Avengers” platform identifies enemy equipment in just 2.2 seconds. The algorithm was trained on a unique array of combat videos: tanks hidden in tree lines, armored personnel carriers stuck on muddy dirt roads, camouflaged artillery. No peaceful laboratory in the world could have assembled such a dataset even over decades. Ukraine obtained it in just three years of war.

The Griselda system operates on an even larger scale. It fully automates the interception and analysis of enemy communications: transcribing conversations, analyzing semantics, and building connection graphs between people, military units, and events. This reduces the need for manual analysis by 99%. The entire cycle — from signal interception to delivering ready intelligence into the Delta battlefield management system — takes around 28–30 seconds.

Half a minute from interception to decision-making. In modern warfare, this is enormous speed, although just a year ago the same process took hours.
Until recently, the concept of “cheap war” remained a theory discussed by military analysts. Ukraine has put it into practice. An FPV drone costing a few hundred dollars destroys equipment worth millions. Yet the key value now lies not in the drone itself but in its software. The advantage belongs to whoever adapts algorithms to new battlefield challenges faster. In this confrontation, software defeats hardware. That is why Ukrainian engineering teams updating systems directly in combat zones work far more effectively than traditional defense giants burdened by years of bureaucratic procedures.

Not a Testing Ground, but a Laboratory

There is a fundamental difference between a testing ground and a laboratory. On a testing ground, others’ hypotheses are tested. In a laboratory, your own are created.

Ukraine’s state defense-tech accelerator Brave1 has already registered more than 300 AI projects. More than 70 artificial intelligence and computer vision systems are already operating along the front line. After visiting Ukraine, former Google CEO Eric Schmidt invested more than $10 million into the military accelerator D3, calling the pace of local innovation “truly impressive.” Turkish company Baykar invested around $100 million into its own research and production center in Ukraine. This is the same Baykar whose Bayraktar drones became symbols of the first phase of the war in 2022.

People do not come here merely to exploit resources. They come here to learn.
The “Drone Line” project envisions the creation of a 15-kilometer unmanned kill zone along the front line, with plans to expand it to 40 kilometers. At the same time, the startup Swarmer is testing swarm technology that allows one operator to control multiple drones simultaneously.

The most important shift in this war is not the drones themselves, but the speed of the adaptation cycle. Classical defense industries implement new technologies over years. In Ukraine, this process has been reduced to weeks. Russians strengthen electronic warfare systems — Ukrainian teams rapidly change navigation algorithms. The enemy jams GPS signals — drones switch to computer vision for autonomous targeting. Enemy camouflage changes — developers instantly retrain target-recognition models. Ukrainian defense forces operate not like a traditional army, but like a dynamic software platform.

After Victory: What Will Remain

Once the war ends, the world will face enormous demand for two Ukrainian products. The first is obvious: combat experience unique in modern Europe. The second — far more valuable in the long term — is military artificial intelligence hardened in real combat.

Datasets, algorithms, architectural solutions, and engineering teams that developed systems under missile strikes will become a powerful strategic asset. Ukrainian defense tech has long reached the global stage. American company Palantir Technologies integrates analytics into Ukraine’s military ecosystem, German defense giant Rheinmetall is launching joint production projects, and European startup Helsing considers Ukraine a key platform for military AI development. American company Shield AI is studying Ukraine’s experience with autonomous systems and drone warfare. Brave1 has become the main gateway for cooperation between Ukrainian developers, NATO, and global defense corporations. The world sees Ukraine not as a theater of war, but as a laboratory for a new military-technological doctrine.

Ukraine is becoming the military equivalent of the legendary American DARPA (Defense Advanced Research Projects Agency). It was DARPA that created the internet, GPS, autonomous systems, and the first drones. Yet the United States spent decades funding universities and laboratories. Ukraine is building its own DARPA directly on the battlefield — under shelling, during air raids, and in constant confrontation with Russian electronic warfare systems. That is why Ukrainian innovations evolve faster: a technology either proves its effectiveness in combat or disappears within days.

But an important question already demands an answer today: who will continue developing these technologies in the future?

Education as a Strategic Shortage

Ukraine faces a paradox: we have achieved a unique technological advantage, yet we risk losing the people capable of capitalizing on it.
Students and school graduates usually choose traditional IT fields — software development, design, or marketing. Military AI, robotics, autonomous systems, and computer vision in defense remain niche areas. There is a lack of specialized programs, public demand, and understanding that this is precisely where national security intersects with successful careers.

Without at least a partial restructuring of the education system today, within 5–7 years Ukraine may inherit a unique technological legacy without the specialists capable of developing it further. In that case, the market will be filled by foreign talent, or Western corporations will simply acquire Ukrainian startups together with their teams and relocate them abroad.

This is not a theoretical threat but the natural logic of the global market. Ukraine is holding the technological front that will shape our future for the next twenty years. The only question is whether we will manage to educate a generation capable of inheriting this legacy.