AI Hype Cycle: How to Find Real and Practical vs. Shiny | OpenAI's Louis Brandy
Plus, no one knows how AI works, how to maximize 10x work and avoid thoughtless daily 1x work routines and 6 disciplines companies need to get the most out of Gen AI.
AI is the biggest hype cycle happening in tech right now, but how do you know what’s actually going to make an impact for your product and team vs. what’s just new and shiny?
This week, LinearB COO & Co-founder Dan Lines sits down with Louis Brandy, Member of Technical Staff at OpenAI and ex-VP of Engineering at Rockset. Louis shares his unique perspective on the evolution of AI, drawing from his experiences with early days AI work at Meta and now with OpenAI following their acquisition of Rockset. He shares grounded insights into the realities of AI, separating fact from fiction in an industry often clouded by buzzwords and unrealistic expectations.
Listeners will learn about the practical applications of AI, the challenges and opportunities it presents, and how to go past the hype to find AI's potential. Whether you're an AI enthusiast, a skeptic, or a professional looking to understand the impact of AI for engineering teams, this episode offers an insightful look at one of the most talked-about topics in tech today.
“We're always going to find ourselves attracted to some shiny thing at any given moment. Some of them are much more real than others. My big hot take on AI is that it's going to get radically better at things you don't expect. Two years from now, it's going to be shockingly good at something you did not expect it to be that good at that quickly.
But there will probably also always be ways that it's bad in ways that are surprising. You'll look at it be amazing at this thing you thought it would do, but it's going to fail in some interesting way that was, that's maybe unexpected. So it's always going to be this like lopsided creature that's getting better in various ways.”
Episode Highlights:
00:32 Louis Brandy's background with AI at Meta
04:31 The current AI hype cycle
13:09 How should engineering leaders think about AI and the pressure to use it?
17:58 How to know if you’re falling into the hype cycle
25:50 AI vs. human code
34:42 Real-time when it comes to AI
38:36 What should an IC do about AI in their career path?
The Download
The Download is engineering leadership content we’re reading, watching, and attending that we think you might find valuable.
1. No One Knows How AI Works
The term “black box” gets used frequently, especially when it comes to anything AI-related.
points out the funding disparity between efforts to understand AI mechanisms and those advancing AI complexity, leading to a growing gap in our knowledge.This lack of understanding poses risks as AI becomes more integrated and powerful, and Alberto Romero urges a shift towards curiosity-driven research to truly comprehend AI before it advances beyond our control.
2. The 6 Disciplines Companies Need to Get the Most Out of Gen AI
Moving beyond the high-level abstractions of AI as a whole, Harvard Business Review’s article “The 6 Disciplines Companies Need to Get the Most Out of Gen AI” offers a great, actionable way for knowledge workers to implement Gen AI solutions. Their 6 disciplines include:
Behavioral changes to use gen AI appropriately.
Controlled experimentation to determine the effectiveness of gen AI in various contexts.
Measurement of business value to rigorously measure the impact of gen AI on productivity and business outcomes.
Data management - robust processes for collecting, storing, and curating unstructured data.
Human capital development - focus on using AI to augment employee capabilities, and invest in training employees to understand how to integrate gen AI into their roles.
Systems thinking to redesign business systems to incorporate gen AI.
Read: The 6 Disciplines Companies Need to Get the Most Out of Gen AI
The Essential Guide to Software Engineering Intelligence (SEI) Platforms (Sponsor)
Whether Software Engineering Intelligence is a new concept or you’re actively evaluating solutions, LinearB’s new Essential Guide to SEI Platforms is perfect for any engineering organization looking to drive continuous improvement.
SEI Platforms help drive operational excellence and business impact, and LinearB’s three-part guide highlights what SEI has to offer, how to adopt it, and how to determine what exactly you’re looking for in a platform. Download your free copy 👇
3. How to Maximize 10x Work and Avoid Thoughtless Daily 1x Work Routines
The rise-and-grind mindset of waking up at 5 am, doing cold plunges, and yoga to start your day is all the rage, but how beneficial is it to productivity?
argues against the overemphasis on rigid routines and productivity hacks like morning yoga, and advocates for embracing serendipity and focusing on high-impact "10x work." Instead of filling your schedule with routine, try prioritizing tasks with the potential for significant impact to achieve greater productivity and success.
4. The Rise of Vector Search with Pinecone
“We're still in February and AI might already be the buzzword of 2023 - and for good reason.”
Little did we know, this was just the beginning of AI’s meteoric rise. In early 2023, we sat down with Pinecone’s Edo Liberty for one of our first heavy AI episodes offering a deep dive on all things vector search.