Nov 6, 2025

AI’s White-Collar Gig Boom: Startups Reshaping Banking and Law AI startups are fostering a new gig economy where bankers and lawyers train algorithms on complex tasks, automating junior roles while creating flexible freelance opportunities. This shift, highlighted by firms like OpenAI and Hebbia, promises transformation in professional services by 2026. AI’s White-Collar Gig Boom: Startups Reshaping Banking and Law Written by Eric Hastings Thursday, November 6, 2025 In the rapidly evolving landscape of artificial intelligence, a new gig economy is emerging that targets the high-stakes worlds of banking and law. Startups are leveraging AI to outsource complex tasks traditionally handled by junior professionals, creating flexible opportunities for experienced bankers and lawyers to train and refine these systems. This shift, driven by companies like OpenAI and Hebbia, promises to automate drudgery while opening up freelance avenues for white-collar workers. According to a recent newsletter from Bloomberg , AI firms are increasingly hiring former bankers and lawyers on a gig basis to teach algorithms the nuances of financial modeling and legal analysis. This mirrors broader trends where AI agents are moving beyond chat interfaces to handle enterprise workflows, as noted in Sequoia Capital’s AI 50 list for 2025. The Rise of AI Training Gigs OpenAI’s secretive project, codenamed Mercury, exemplifies this trend. The company has enlisted over 100 former investment bankers as contractors, paying them $150 per hour to train AI on financial modeling for transactions like mergers and acquisitions. “OpenAI looks to replace the drudgery of junior bankers’ workload,” reports The Economic Times , highlighting how these gigs automate entry-level tasks while providing flexible income for seasoned professionals. Similarly, AI startup Hebbia is transforming investment banking workflows. A demo reviewed by Business Insider showed how its tools could streamline data analysis and deal preparation, potentially reducing the need for round-the-clock junior analyst labor. Industry insiders note that this creates a gig market where bankers can contribute expertise remotely, without full-time commitments. Posts on X, formerly Twitter, reflect growing sentiment around this shift. One user highlighted, “A new gig economy is emerging, focused on training AI to perform tasks typically handled by white-collar professionals like bankers and lawyers,” echoing discussions on platforms about AI’s role in reshaping careers. Legal Sector Disruptions The legal field is not immune. Startups listed in AIMojo’s 2025 roundup of 12 AI companies in the legal sector are innovating with agentic AI for tasks like contract review and case prediction. These tools require human oversight and training, spawning gig roles for lawyers to fine-tune AI outputs and ensure accuracy. A Forbes article from March 2025 posits that AI is both a threat and opportunity in the gig economy, with freelancers who master AI tools thriving in legal tech. “AI is reshaping the gig economy, disrupting jobs while creating new opportunities,” the piece states, emphasizing adaptability for professionals. McKinsey’s 2025 State of AI survey, available at McKinsey , underscores how AI agents are driving value in professional services, including law, by automating routine tasks and enabling scalable gig work. Broader Economic Implications This AI-driven gig economy extends beyond individual roles. A Forrester report cited in IT Brief Asia predicts that by 2026, over half of younger adults will use generative AI for financial advice, transforming banking sectors and creating demand for gig-based AI trainers. CB Insights’ AI 100 for 2025, found at CB Insights , ranks emerging AI companies shaping intelligent systems in industries like finance and law, with a focus on agentic AI that relies on human input for complex reasoning. Startups like DeepSeek and Agility AI, featured in Bloomberg’s list of AI startups to watch in 2026, are building tools that could further expand this gig model by integrating AI into vibe-coding and robotics, potentially applicable to financial simulations. Challenges and Opportunities Ahead However, this transformation isn’t without hurdles. A RadCred survey reported in OpenPR reveals that 58% of gig workers seek emergency loans quarterly, highlighting financial instability in this emerging economy. X posts from users like Timo Rainio emphasize, “The landscape of business technology is undergoing a massive, AI-driven transformation, with Agentic AI emerging as the single most critical trend for 2025,” pointing to the need for persistent memory and security in AI systems trained by gig professionals. TechCrunch’s compilation of 33 US AI startups raising over $100 million in 2025, accessible at TechCrunch , shows heavy investment in AI infrastructure, fueling the gig economy’s growth in banking and law. Industry Adaptation Strategies For bankers and lawyers, adapting means embracing AI literacy. Startups Savant lists top AI startups to watch in 2025 at Startups Savant , many of which are creating platforms for gig-based AI collaboration in professional services. Sequoia Capital’s insights note that AI agents are taking on real workflows in law and customer service, as detailed in their AI 50 report , suggesting a future where gig workers specialize in AI oversight rather than traditional tasks. Finally, as AI integrates with IoT and blockchain, per X posts from SA News Channel, the gig economy for white-collar pros will likely expand, demanding continuous upskilling to leverage these technologies in banking and legal contexts. Subscribe for Updates AITrends Newsletter The AITrends Email Newsletter keeps you informed on the latest developments in artificial intelligence. Perfect for business leaders, tech professionals, and AI enthusiasts looking to stay ahead of the curve. AITrends By signing up for our newsletter you agree to receive content related to ientry.com / webpronews.com and our affiliate partners. For additional information refer to our terms of service . Notice an error?