Alexander Vasylenko, a financial analyst at a large steel producer, has taken an unconventional approach to securing his career future: spending his evenings training the very AI models that could potentially replace him. Working from 9 AM to 9 PM daily, Vasylenko dedicates his post-work hours to AI training projects, earning between $50 to $160 per hour depending on the complexity of the work.
Vasylenko’s journey into AI training began in 2023 when a recruiter contacted him on LinkedIn seeking finance experts to teach AI models. Initially skeptical, he quickly discovered both the financial benefits and strategic value of understanding the technology reshaping his industry. The technological progress has been astonishing: two years ago, AI models required step-by-step guidance to calculate basic metrics like free cash flow. Today, these models can process five PDF files, reference multiple external sources, and perform complex financial analyses with minimal direction.
His AI training work involves creating challenging prompts designed to “stump” large language models, a task that now takes three to eight hours per prompt and is trending toward full-day efforts as models become more sophisticated. As both a prompt writer and reviewer, Vasylenko earns substantially more than his initial $30/hour rate with Remotasks, with experienced trainers commanding up to $160/hour. The work is project-based but consistently available, with team leads regularly reaching out with new assignments.
Vasylenko’s background spans multiple countries and roles: he started as a stock trader in his native Ukraine, worked as an analyst at CIBC in Canada, and relocated to New York following the war in Ukraine. His family lost everything in the conflict, driving his intense work schedule to support relatives. Despite the demanding hours—including most Saturdays and occasional Sundays—he views AI training as both a financial necessity and strategic investment in his future, describing it as a “hobby” that combines his finance expertise with emerging technology.
Key Quotes
Two years ago, I would have had to walk an AI model through every single step and input to have a large language model calculate free cash flow. Now, I can provide five PDF files, reference three other outside sources, and ask the model to use these assumptions to calculate free cash flow.
Vasylenko describes the dramatic acceleration in AI capabilities for financial analysis, highlighting how models have evolved from requiring detailed guidance to handling complex, multi-source tasks independently in just 24 months.
It’s a bit scary when you wonder how useful you’ll be going forward.
This candid admission reveals the existential anxiety many finance professionals face as AI systems rapidly approach and potentially exceed human capabilities in analytical tasks that once required years of training.
The people who will evolve in this environment are those who know their subject matter well and those who know how to combine their knowledge with AI.
Vasylenko articulates his core thesis for career survival in the AI era, emphasizing that deep domain expertise combined with AI literacy will be the differentiating factor for professionals navigating technological disruption.
Right now, it takes between three to eight hours to write a task that triggers model failure, and we’re heading to a world where it could take a day or two.
This observation underscores how sophisticated AI models have become, requiring increasingly complex and nuanced challenges to identify their limitations—a trend that paradoxically increases the value of expert human trainers even as it demonstrates AI’s growing capabilities.
Our Take
Vasylenko’s story represents a fascinating case study in proactive career adaptation. Rather than waiting passively for AI disruption, he’s essentially conducting reconnaissance on the technology that threatens his profession. His 12-hour workdays reflect both economic necessity and strategic positioning, but they also reveal a broader truth: the AI transition is creating temporary arbitrage opportunities for those willing to work at the intersection of human expertise and machine learning.
What’s particularly striking is the accelerating difficulty of stumping AI models—a metric that serves as a real-time indicator of how quickly these systems are approaching human-level performance in specialized domains. The fact that creating challenging prompts now requires three to eight hours, trending toward full days, suggests we’re approaching an inflection point where AI capabilities may plateau or where human trainers become exponentially more valuable. Vasylenko’s bet is essentially that understanding this technology intimately will make him irreplaceable as a human-AI interface manager rather than a traditional analyst.
Why This Matters
This story illuminates a critical paradox facing white-collar professionals in the AI era: the need to actively participate in developing the technology that may disrupt their own careers. Vasylenko’s experience demonstrates how rapidly AI capabilities are advancing in specialized fields like financial analysis, with models progressing from basic calculations to complex multi-source analysis in just two years.
The financial incentives for AI training work—up to $160/hour for experienced trainers—reveal how dependent AI development remains on human expertise, even as these systems grow more capable. This creates a temporary but lucrative opportunity for professionals willing to bridge their domain knowledge with AI development.
More significantly, Vasylenko’s strategy represents an emerging career survival tactic: becoming indispensable by understanding both the subject matter and the AI tools transforming it. His prediction that future professionals will primarily oversee AI bots rather than perform tasks directly suggests a fundamental shift in how knowledge work operates. Those who position themselves at this intersection—combining deep expertise with AI literacy—may find themselves managing the transition rather than being displaced by it. This approach could become a blueprint for professionals across industries facing similar AI-driven disruption.
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Source: https://www.businessinsider.com/training-ai-investment-analyst-job-security-bet-2025-12