Using AI to Preserve 140 Years of History at the LA Times | Deepika Manglani, VP Product (LA Times)
Deepika Manglani discusses how her team at the LA Times is using AI to preserve over 12 million pages of news archives from as far back as the 1800s.
In this episode, we’re joined by Deepika Manglani, VP of Product and Program Management at the LA Times. Deepika’s career in media spans over 15 years, culminating in her current role, where she’s bringing the 140-year-old institution into the future.
In this episode, Deepika shares:
How her team is using AI to preserve a unique trove of historical data, over 12 million pages of news archives from as far back as the 1800s
What this digital archive and maturation of AI enables for future storytelling, media innovation, and news personalization
Why combining product and program management was critical to navigating massive transformation at the LA Times through a period of heavy M&A activity
1. Rebuilding a 140-year-old institution from scratch (3:10)
When the LA Times was sold in 2018, the newsroom stayed, but the technology backbone didn’t.
That meant rebuilding everything: networks, HR systems, billing, platforms, and products, while publishing daily.
“Imagine what that means. No network, no infrastructure, no products, no platforms, no people. And yet the newsroom has to run 24/7 for a 140-year-old company.”
To make it work, she merged product and program management into one organization — aligning vision, execution, and delivery under a single mission.
The takeaway: transformation isn’t about shiny tools. It’s about building operational foundations that let journalism survive and scale.
2. Why generative AI wasn’t good enough for history (10:45)
The LA Times has more than 12 million archived articles, many trapped in low-quality scans and microfiche.
Early estimates to re-digitize them ran the team millions of dollars before a multimodal AI solution arrived.
Even then, however, the team needed an AI tool that wasn’t “too smart” for this use case. Because when they “thought,” the tools hallucinated or altered facts:
“The problem is when these AI models start understanding the context, that’s when they start thinking. And if you don’t want them to think, you gotta eliminate that part.”
So her team deliberately chose a non-generative, OCR-style model that just copied text.
The takeaway: Picking the right model doesn’t mean choosing the shiniest new tool on the market, but the one that will best fit your use case.
3. Turning archives into living storytelling tools (15:30)
Today, the LA Times has already digitized 300,000+ articles, with a goal of 500,000 in the near term.
Once complete, these archives won’t just sit in storage — they’ll power modern journalism.
Deepika explains how historical context can reshape coverage of major events:
“If we can stitch that together… what happened back in [the 1994 World Cup] versus right now in 2026, that would make the story more interesting.”
Instead of researchers spending days digging through records, journalists will be able to search decades of history instantly.
With semantic search, archives become an active reporting tool, not just a museum.
4. The future of personalized news (25:10)
Beyond preservation, Deepika is thinking about how news consumption itself should evolve.
She wants media to learn from platforms like Spotify and TikTok without sacrificing credibility.
“I would love to have an app that reads news to me based on my history of reading.”
The infrastructure being built today — archives, metadata, personalization systems — makes this future possible.
Links
Chapters
00:00 Introduction
01:49 Deepika's career journey in media and product leadership
03:03 Building from scratch at LA Times
07:19 Digitizing historical archives
10:34 Challenges and innovations in AI and OCR
22:29 Future prospects and personalization in news
26:52 Conclusion
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