In remote, services-based businesses, pressure to deliver quality and the difficulty of junior mentorship make hiring senior engineers a necessity. The cost and complexity of building remote training programs often outweigh the benefits of hiring less experienced talent.
A seasoned CTO finds negligible performance differences between major AI coding tools (Claude, CodeX, Cursor) for rapid prototyping. The primary value is speed, not marginal accuracy. Subscribing to multiple services is more for staying current with market trends than for a specific tool's superiority.
To effectively leverage AI, treat it as a new team member. Take its suggestions seriously and give it the best opportunity to contribute. However, just like with a human colleague, you must apply a critical filter, question its output, and ultimately remain accountable for the final result.
Many technical leaders initially dismissed generative AI for its failures on simple logical tasks. However, its rapid, tangible improvement over a short period forces a re-evaluation and a crucial mindset shift towards adoption to avoid being left behind.
A fractional CTO sees AI's impact in two ways: enhancing current capabilities (making things faster or better) or adding entirely new ones previously out of reach. For example, AI enables 24/7 support for an SMB laundromat, a function that was previously financially unfeasible.
To effectively learn AI, one must make a conscious mindset shift. This involves consistently attempting to solve problems with AI first, even small ones. This discipline integrates the tool into daily workflows and builds practical expertise faster than sporadic, large-scale projects.