The soaring payouts for discovered vulnerabilities aren’t a gesture of Big Tech goodwill—they are a symptom of quiet panic. When Apple launched its bug bounty program in 2016, the top reward was $200,000. By 2019, it had climbed to $1 million, and last year it breached the $2 million mark. This tenfold leap in less than a decade illustrates the collapse of the traditional hacking economy. As agentic AI models learn to autonomously breach software and mass-produce exploits, traditional audits performed by 'white hat' hackers are becoming relics of a bygone era. We are witnessing a rapid devaluation of human labor: the cost of generating high-quality exploits is falling faster than corporations can update their budgets.
The industry standard of a 90-day responsible disclosure window—the time traditionally granted to vendors to patch holes before public release—is officially dead. Security researcher Himanshu Anand aptly notes that this timeline was designed for a world where bug hunters were a rare commodity and exploit development took months. Large Language Models (LLMs) have compressed both scales simultaneously. This acceleration forces businesses into a state of permanent emergency, where the gap between discovery and weaponization is shrinking toward zero.
For most cybersecurity departments, the influx of AI-generated reports is already causing operational paralysis. Where an analyst once reviewed three coherent reports a week, they are now drowning in hundreds of automated notifications. Researcher Joseph Thacker notes that he is already submitting three times as many bugs as he did last year and predicts that payouts for giants like Google could surge by 2x to 10x. This is a direct path to financial exhaustion: while tech titans can still throw money at the problem, mid-sized businesses will simply go under. The market will inevitably split: human hunters will focus on the most complex logic flaws, while AI agents 'vacuum up' all standard vulnerabilities.