I dropped out of my PhD in AI (why)

2026-07-17 · Source: What's AI by Louis-François Bouchard · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

The author, associated with Towards AI, recently revealed they dropped out of their AI PhD three years ago. This decision stemmed from the significant gap between theoretical AI research conducted in a lab and its practical application in real-world company settings, a challenge amplified by the advent of ChatGPT. Recognizing that the critical issues in AI have shifted from model selection to effective implementation, Towards AI has launched "Towards AI Deployment." This new service offers end-to-end assistance, including identifying areas where AI can genuinely impact a company, building tailored systems, and training internal teams for seamless handover. The initiative addresses the current industry need for practical AI integration solutions.

Key takeaway

For Directors of AI/ML or AI Engineers struggling with practical AI integration, this highlights that successful deployment now outweighs model selection. You should prioritize end-to-end implementation strategies, focusing on how AI truly moves the needle within your organization. Consider external expertise for building robust systems and training your teams, ensuring AI solutions are not just theoretical but deliver tangible business value.

Key insights

The primary challenge in AI has shifted from model selection to real-world implementation and deployment.

Principles

• Lab-based AI research often fails in real-world deployment.
• Post-ChatGPT, practical AI implementation is a universal problem.
• Effective AI integration requires end-to-end system building and team training.

Method

Towards AI Deployment involves identifying high-impact AI applications within a company, building a complete system, training the client's team, and ensuring a smooth handover.

In practice

• Focus on implementation over model choice.
• Prioritize end-to-end AI system development.
• Invest in team training for AI adoption.

Topics

• AI Deployment
• AI Implementation
• AI Engineering
• Enterprise AI
• MLOps
• Practical AI

Best for: CTO, VP of Engineering/Data, Executive, AI Engineer, Director of AI/ML, Consultant

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Editorial summary, takeaway, and curation by AIssential. Original article published by What's AI by Louis-François Bouchard.