mentorship
Beyond Market Intelligence keeps mentorship in one place: 6 stories so far. The section currently leads with “Master the Code by Slowing Down in the Age of AI”, “Accelerating Tomorrow’s Builders: A New Path for High School Grads”, and “Training Developers for an AI-Native Future”. Ben Linders argues that real mastery in software engineering comes from slowing down, even as generative AI accelerates output. High school graduates don't need another four-year holding pattern. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every mentorship story on Beyond Market Intelligence, newest first.
Master the Code by Slowing Down in the Age of AI
Ben Linders argues that real mastery in software engineering comes from slowing down, even as generative AI accelerates output. He's right. Tools can produce code quickly, but they don't build understanding. Engineers still need to grasp how systems work to truly learn. Senior developers who coach juniors through deliberate, slower techniques are preserving the craft. For more on what AI agents can, and cannot, do with code, see our coverage of "300K lines refactored for $4,000: what a C codebase taught AI agents."
Accelerating Tomorrow’s Builders: A New Path for High School Grads
High school graduates don't need another four-year holding pattern. They need a launchpad. This new academy sits somewhere between a trade school, Y Combinator, and Peter Thiel's Fellowship, offering a direct route to building real ventures. It's an accessible answer for those ready to skip the traditional script. While the broader investing conversation heats up, including the upcoming Wahlberg and Lee discussion at Disrupt 2026, this program focuses on doing. It transforms raw ambition into tangible progress, and that's a path worth exploring.
Training Developers for an AI-Native Future
When AI agents take over the routine work that once trained junior developers, where does that leave the next generation? That's the question Michael Stiefel put to Scott Hanselman in our latest podcast. His answer is as practical as it is provocative: borrow the preceptorship model from nursing, pairing novices with seasoned mentors in structured, hands-on settings. It's a grounded alternative to the chaos of AI-era job listings. For those navigating this shift, our guide on shifting AI job requirements offers useful context.
Streamlining Peer Review: ACL Introduces Reviewer-Linked Submission Model
ACL's new Sustainable Reviewing Policy is a direct answer to a problem many of us feel: submission numbers are outpacing the people qualified to review them. The core proposal makes sense. Each paper should bring its own reviewer, or risk a lottery for leftover capacity. That is not harsh; it is practical. The caps of 20 total submissions and 5 first-author papers per cycle are generous limits that still encourage serious work.
Freedom to explore your PhD path when you're ready to drive your own research
A PhD is a marathon of self-direction, but even the most independent researcher needs a compass. This setup offers total freedom, which sounds liberating, yet the near-total absence of feedback creates a real risk of drifting. For the right person, it is a dream; for others, it is a lonely trap. The choice hinges on whether you thrive on isolation or require a mentor's voice to sharpen your thinking. For more on navigating complex systems, explore our piece on unlocking LLM training.
How small teams can use AI to compete in ML research.
A solo researcher can now lean on an LLM for coding help, literature review, and sharper writing, tasks that once required a supportive lab or seasoned mentors. That is real progress. It does not replace research taste, but it does lower the barrier between a raw idea and a polished paper. The question is whether this levels the field or simply gives well-resourced teams more runway.