Original Summary

Building generic AI software is dead. The real money is in finding high-paying, niche enterprise markets, taking existing models, training them on hyper-specific data sets, and integrating them into targeted software workflows. As a 2nd-year business undergrad with no prior tech experience, I'm using the IITM BS in Data Science explicitly to execute this. I don't care about the underlying architecture - I care about data training efficiency and high-margin integration. Change my mind: Traditional CS grads are too busy fighting over base model architectures to realize that data arbitrage and market integration are where the actual revenue is. What technical bottleneck breaks this model, or are engineers just mad that business majors are coming for the highest-paying sectors?   submitted by   /u/BornOfAshAndAscent [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/10/19 12:47:00