How Denis Brovarnyy is closing the AI Skills gap with real-world training


The gap between finishing a course and actually being useful on a team, Denis Brovarnyy has seen it from both sides. And in an era where AI is reshaping every technical role, that gap is getting more expensive to ignore. Companies are no longer experimenting with AI. They are implementing it. And they need people who can contribute from day one, not after six months of onboarding. He spent years as a software engineer and then an engineering manager in Israel, building products and leading teams. He knew what it took to hire someone junior and get them productive fast. He also knew how rarely training programs produced that person.

When he lost his job, he didn’t immediately look for the next one. He sat with a question most people skip: “Is there a better use of what I know?” The answer became AIT Technology School, and a decade-long project to build education that actually translates into employment.

From engineer to educator

Denis has a background in computer science and systems analysis. In 2006, he earned his Bachelor’s degree in Computer Science. He later did research in mathematical modeling and GIS-based infrastructure systems. After moving to Israel, he worked in technical and engineering management roles for several years.

The layoff that prompted his pivot was a clarifying moment, uncomfortable at the time, useful in hindsight. He realized he had two choices: he could return to a familiar career, or try to fix the disconnect between formal education and what employers actually need. He chose the more difficult option. 

He joined an existing IT school in Israel and immediately started dismantling how it worked. Lectures went down. Real projects went up. Students worked in teams with actual deadlines, built portfolios with output that employers could evaluate, and learned what it felt like to ship something under pressure. It’s not a radical idea, it’s just rarely executed properly. Most programs say “hands-on” and mean exercises. Denis meant: you’re building something, it has to work, and someone is going to look at it the way a hiring manager would.

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Building AIT Technology School: Practical, Market-Driven, Employer-Focused

That philosophy became the core of AIT Technology School. The school runs programs focused on training AI engineers, a profession at the intersection of IT and artificial intelligence, helping companies automate routine work, improve customer service, and implement AI at scale. It is one of the fastest-growing and least-served roles in the market today. Feedback from managers is used to rebuild Curricula.

Under Denis’s leadership, AIT Technology School has trained more than 1,500 graduates and at one point managed more than 700 active students simultaneously. The growth came primarily from a single-minded focus on employment outcomes. When graduates get hired and perform well, the school’s reputation follows. 

Denis stays closely involved across curriculum, partnerships, marketing, and operations, not as a figurehead, but because he believes the moment leadership loses touch with the actual product, the product starts drifting from reality. Every program is evaluated against one question: “Can this person contribute on day one?

Expanding Across Borders

Taking AIT Technology School into Germany and then the United States wasn’t a matter of duplicating the model. Each market has its own hiring culture, its own expectations of what “ready” looks like, and its own pace.

In Germany, structured and specialised training carries more weight. Employers there tend to want depth in a defined area and a clear credentials trail. The market moves faster in the U.S., and range, the ability to pick up new tools and slot into different team structures, is valued. 

Denis and his team adjusted the core approach to fit each environment without abandoning the underlying principle: practical readiness, real projects, measurable outcomes.

This international expansion also reflects something about the people AIT Technology School serves. Many of its students are globally mobile, professionals who have moved across borders and need to re-enter a labor market in a new country, often in a compressed time frame. AIT’s model, with its emphasis on professional portfolio work and employer-aligned skills, is particularly well-suited to that challenge.

Education in the AI Era

Education is no longer about the knowledge people accumulate, it’s about how quickly they can put it to work.​​​​​​​​​​​​​​​​ AI has shifted the hiring calculus in ways that are still playing out. Technical knowledge alone no longer differentiates candidates the way it once did. What employers want now is someone who can pair foundational skills with modern tools, adapt quickly when those tools change, and produce results before they’ve had time to settle in.

Denis has been steering AIT Technology School toward exactly that. The emphasis is less on memorizing frameworks and more on building the problem-solving instincts and execution habits that hold up as technologies keep evolving. Most programs sell knowledge. AIT Technology School is built around a different premise: the labor market doesn’t pay for knowledge, it pays for the ability to solve real problems. That means enough hours, real projects, team work, and preparation for actual interviews. A sequence, not a course. That’s why AIT Technology School has shifted its focus to training AI engineers,  one of the fastest-growing and least-served roles at the intersection of technology, product, and business. 

In his view, the question education should be answering right now isn’t “what does this person know?” but “how quickly can they deliver results?”

In fast-changing fields, knowledge alone is no longer enough,” he says. “Traditional education often moves more slowly than the labor market, while real careers are shaped by how quickly people can deliver results in actual teams, workflows, and products.”

What He’s Actually Building Toward

Denis measures AIT’s success by one thing: did the graduate get a job, and could they do it? Not course completion rates. Not certificates issued. The outcome.

He wants to integrate education, employment, and entrepreneurship. Denis feels people need to keep on learning, even after starting their careers. 

He completed executive and entrepreneurship programs at institutions including York Entrepreneurship Development Institute, Technion, Israel Institute of Technology, and Stanford University. These experiences encouraged him to think about how education and company-building could be connected more intelligently.

Now based in Miami Beach, Florida, he continues to lead AIT’s day-to-day operations while driving its international growth. The path from software engineer to school founder wasn’t a reinvention. It was a direct application of everything he’d already learned about what it actually takes to build something that works.



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Recent Reviews


As I’m writing this, NVIDIA is the largest company in the world, with a market cap exceeding $4 trillion. Team Green is now the leader among the Magnificent Seven of the tech world, having surpassed them all in just a few short years.

The company has managed to reach these incredible heights with smart planning and by making the right moves for decades, the latest being the decision to sell shovels during the AI gold rush. Considering the current hardware landscape, there’s simply no reason for NVIDIA to rush a new gaming GPU generation for at least a few years. Here’s why.

Scarcity has become the new normal

Not even Nvidia is powerful enough to overcome market constraints

Global memory shortages have been a reality since late 2025, and they aren’t just affecting RAM and storage manufacturers. Rather, this impacts every company making any product that contains memory or storage—including graphics cards.

Since NVIDIA sells GPU and memory bundles to its partners, which they then solder onto PCBs and add cooling to create full-blown graphics cards, this means that NVIDIA doesn’t just have to battle other tech giants to secure a chunk of TSMC’s limited production capacity to produce its GPU chips. It also has to procure massive amounts of GPU memory, which has never been harder or more expensive to obtain.

While a company as large as NVIDIA certainly has long-term contracts that guarantee stable memory prices, those contracts aren’t going to last forever. The company has likely had to sign new ones, considering the GPU price surge that began at the beginning of 2026, with gaming graphics cards still being overpriced.

With GPU memory costing more than ever, NVIDIA has little reason to rush a new gaming GPU generation, because its gaming earnings are just a drop in the bucket compared to its total earnings.

NVIDIA is an AI company now

Gaming GPUs are taking a back seat

A graph showing NVIDIA revenue breakdown in the last few years. Credit: appeconomyinsights.com

NVIDIA’s gaming division had been its golden goose for decades, but come 2022, the company’s data center and AI division’s revenue started to balloon dramatically. By the beginning of fiscal year 2023, data center and AI revenue had surpassed that of the gaming division.

In fiscal year 2026 (which began on July 1, 2025, and ends on June 30, 2026), NVIDIA’s gaming revenue has contributed less than 8% of the company’s total earnings so far. On the other hand, the data center division has made almost 90% of NVIDIA’s total revenue in fiscal year 2026. What I’m trying to say is that NVIDIA is no longer a gaming company—it’s all about AI now.

Considering that we’re in the middle of the biggest memory shortage in history, and that its AI GPUs rake in almost ten times the revenue of gaming GPUs, there’s little reason for NVIDIA to funnel exorbitantly priced memory toward gaming GPUs. It’s much more profitable to put every memory chip they can get their hands on into AI GPU racks and continue receiving mountains of cash by selling them to AI behemoths.

The RTX 50 Super GPUs might never get released

A sign of times to come

NVIDIA’s RTX 50 Super series was supposed to increase memory capacity of its most popular gaming GPUs. The 16GB RTX 5080 was to be superseded by a 24GB RTX 5080 Super; the same fate would await the 16GB RTX 5070 Ti, while the 18GB RTX 5070 Super was to replace its 12GB non-Super sibling. But according to recent reports, NVIDIA has put it on ice.

The RTX 50 Super launch had been slated for this year’s CES in January, but after missing the show, it now looks like NVIDIA has delayed the lineup indefinitely. According to a recent report, NVIDIA doesn’t plan to launch a single new gaming GPU in 2026. Worse still, the RTX 60 series, which had been expected to debut sometime in 2027, has also been delayed.

A report by The Information (via Tom’s Hardware) states that NVIDIA had finalized the design and specs of its RTX 50 Super refresh, but the RAM-pocalypse threw a wrench into the works, forcing the company to “deprioritize RTX 50 Super production.” In other words, it’s exactly what I said a few paragraphs ago: selling enterprise GPU racks to AI companies is far more lucrative than selling comparatively cheaper GPUs to gamers, especially now that memory prices have been skyrocketing.

Before putting the RTX 50 series on ice, NVIDIA had already slashed its gaming GPU supply by about a fifth and started prioritizing models with less VRAM, like the 8GB versions of the RTX 5060 and RTX 5060 Ti, so this news isn’t that surprising.

So when can we expect RTX 60 GPUs?

Late 2028-ish?

A GPU with a pile of money around it. Credit: Lucas Gouveia / How-To Geek

The good news is that the RTX 60 series is definitely in the pipeline, and we will see it sooner or later. The bad news is that its release date is up in the air, and it’s best not to even think about pricing. The word on the street around CES 2026 was that NVIDIA would release the RTX 60 series in mid-2027, give or take a few months. But as of this writing, it’s increasingly likely we won’t see RTX 60 GPUs until 2028.

If you’ve been following the discussion around memory shortages, this won’t be surprising. In late 2025, the prognosis was that we wouldn’t see the end of the RAM-pocalypse until 2027, maybe 2028. But a recent statement by SK Hynix chairman (the company is one of the world’s three largest memory manufacturers) warns that the global memory shortage may last well into 2030.

If that turns out to be true, and if the global AI data center boom doesn’t slow down in the next few years, I wouldn’t be surprised if NVIDIA delays the RTX 60 GPUs as long as possible. There’s a good chance we won’t see them until the second half of 2028, and I wouldn’t be surprised if they miss that window as well if memory supply doesn’t recover by then. Data center GPUs are simply too profitable for NVIDIA to reserve a meaningful portion of memory for gaming graphics cards as long as shortages persist.


At least current-gen gaming GPUs are still a great option for any PC gamer

If there is a silver lining here, it is that current-gen gaming GPUs (NVIDIA RTX 50 and AMD Radeon RX 90) are still more than powerful enough for any current AAA title. Considering that Sony is reportedly delaying the PlayStation 6 and that global PC shipments are projected to see a sharp, double-digit decline in 2026, game developers have little incentive to push requirements beyond what current hardware can handle.

DLSS 5, on the other hand, may be the future of gaming, but no one likes it, and it will take a few years (and likely the arrival of the RTX 60 lineup) for it to mature and become usable on anything that’s not a heckin’ RTX 5090.

If you’re open to buying used GPUs, even last-gen gaming graphics cards offer tons of performance and are able to rein in any AAA game you throw at them. While we likely won’t get a new gaming GPU from NVIDIA for at least a few years, at least the ones we’ve got are great today and will continue to chew through any game for the foreseeable future.



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