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Matthew Lynley

Research analyst and former journalist with 10 years of reporting, writing, and data analytics experience

Matthew Lynley is a seasoned technology industry journalist and a data science practitioner with over a decade of experience.

He began his career during the mobile era and iPhone 4's debut and has a deep understanding of analytics and data science tools.

Matthew's current focus is on big data and AI, covering these topics in his newsletter as an accessible resource for decision-making.

He holds a Bachelor of Arts degree in Mathematics and Journalism from the University of North Carolina at Chapel Hill, with a specialization in complex analysis.

Throughout his career, he has held various roles in organizations like Supervised, Insider, Inc., Pluralsight, A Cloud Guru, and Brex, covering positions from reporter to product marketing manager.

Matthew has contributed to renowned publications such as TechCrunch, BuzzFeed, The Wall Street Journal, Business Insider, and VentureBeat, showcasing his broad expertise in the tech and business journalism spheres.

Highlights

Oct 3 · twitter

A startup on my “watch list” from November 2023 🙂 https://t.co/KdCJMXHxSE

Sep 11 · linkedin

I’ve been asking the dozens of people I’ve talked to in the past few months the same thing: how have the questions enterprises are asking about AI changed in the last 6-9 months?

As it turns out, they have: instead of just jumping on the hype train, they’re being more careful and deliberate about what they are looking for. In the last nine months we’ve seen an incredible race to the bottom in terms of APIs and top-end model performance. And now enterprises that I speak with have determined they can’t just slap a wrapper on OpenAI and call it a day.

Instead they are now thinking on multi-year horizons, figuring out what the infrastructure they need looks like for more advanced use cases like RAG, and most importantly, how to keep costs under control. In many cases, these companies saw their first bill from OpenAI at the front of the wave and immediately got sticker shock.

That’s made them, for example, evaluate smaller models with a focus on making them work with proprietary data. Even the difficulty curve of putting together these smaller models is looking much more manageable when compared to the ease of use of the APIs, even when including OpenAI and Gemini fine-tune products. (Though, to be clear, these products very much have the eyes of enterprises.)

Part of all this is also a recognition that the fastest pathway to generate a return on investment is saving costs by automating much smaller and contained tasks—and doing that a lot. It means customizing a smaller or cheaper model through fine-tuning or RAG, for example, to just summarize sales calls and extract information. Another example that comes up often is managing the flow of customer service tickets, and when to escalate.

It feels like, based on all these conversations, the era of thinking you could wave a OpenAI-shaped wand and generate completely new lines of business or completely replace an entire class of workers is effectively gone. But at the same time, there’s enough signal that companies aren’t just throwing the idea of deploying a generative AI tool into the trash or writing it off as an experiment.

#ai #bigdata #openai

Matthew Lynley
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Location

San Francisco, California, United States