Radiolab – "Time is Honey"

Date: February 13, 2026
Podcast: Radiolab (WNYC Studios)
Hosts: Lulu Miller & Latif Nasser
Main Guests: Sunil Nakrani, Dr. Tom Seeley, Craig Tovey

Episode Overview

"Time is Honey" explores how studying honeybee behavior inspired a revolutionary algorithm now used to keep the Internet running smoothly under heavy, unpredictable demand—revealing a deep parallel between natural evolution and human technology. Through interviews, story-driven experimentation, and signature sound design, Radiolab investigates how lessons from bees have shaped the modern Internet, influencing not only how content is delivered but also how we solve complex organizational problems elsewhere.

Key Discussion Points & Insights

1. Sunil Nakrani and the Problem of Flash Internet Demand

• Sunil’s Background: Grew up in Kenya, India, and the UK; studied electrical engineering; worked at IBM as the Internet was just emerging in the late '80s and early '90s.
  • “Bachelor's and a master's degree in electrical engineering.” (00:48, D)
• September 11, 2001: Experiences firsthand how Internet infrastructure collapses under sudden, massive demand for information, with websites crashing or serving only text.
  • “Why is it that at the very moment when I and the world want to access something the most, that's when I can't access it?” (02:22, A)
• Frustration & Obsession: Wonders how to build systems that can handle unpredictable surges, “Internet flash floods”.

2. Seeking Solutions: From Internet Overload to Honeybees

• Reaching Out: Sunil, split between Oxford (PhD) and Atlanta (wife’s job), emails Georgia Tech out of desperation, getting a response from operations researcher Craig Tovey within 30 minutes.
• Meeting Craig Tovey:
  • “Come by my office.” (03:57, D, quoting E)
• Craig’s Revelation: After listening to Sunil’s problem, Craig hands him a research paper on forager allocation among flower patches by honeybee colonies.

3. The Wisdom of the Hive: Tom Seeley’s Bee Experiments

• Dr. Tom Seeley’s Background: Legendary honeybee researcher at Cornell, "pioneered the study of how honeybees live in the wild.” (05:59, F)
• Hive Challenge: Bees efficiently collect nectar from uneven, unpredictable flower patches—no boss, no central planner.
• Experimental Setup: At Cranberry Lake Biological Station, they tag bees, set up two different “flower” feeders (one closer, one further, sometimes with differing sugar content), and observe how bees allocate themselves.
  • “Each bee had to be individually recognizable...little two digit number on their thorax and a little dab of paint on their abdomen.” (12:28, E)
• Key Finding:
  • Waggle dance as communication; bees return sooner from closer, richer patches and their frequent dances attract more bees. Crowding slows trips; bees naturally redistribute as conditions change, achieving efficient resource allocation.
• Memorable Experiment Detail:
  • “If there are a lot of bees going to this five-minute patch, eventually Craig says there will be more and more depleted flowers...a five-minute patch, if it's crowded, is no longer a five-minute patch...eventually, you're in equilibrium.” (16:35–17:41, E/A)

4. From Bees to Servers: Mathematical Modeling

• Honeybee Algorithm:
  • Encapsulated mathematically by Craig and Tom; efficient resource allocation is achieved by simply sending more “workers” (bees, servers) to where the return trip is quickest.
  • “If one flower patch has a smaller round trip time than the others, send more bees there.” (18:54, E)
• Craig’s Frustrations: While not useful for his earlier robotics projects, he instantly recognizes the match for Sunil’s Internet bottleneck problem.
• Quote: “I saw that the problem was similar to the honeybee problem.” (28:56, E)

5. Applying the Bee Algorithm to the Internet

• Explaining with the 'Hamster Dance':
  • Analogy: A viral video is like a flower patch suddenly bursting with nectar (attention); overloaded servers are like bees needing reinforcement; servers ‘ping’ for backup, just like bees recruit through their waggle dance.
  • “The server hosting that video is like a single bee being, like, holy motherly bonanza of nectar over here. I'm gonna need some backup.” (31:30, A)
• Massive, Ongoing Dynamic Allocation:
  • Servers (bees) are continually reallocated in real time to new viral content (‘flowers’), optimizing delivery as demand changes.
  • “This is happening times like a gazillion, right? All the time.” (32:58, C)

6. Testing and Reality Check

• Algorithm Face-off:
  • Compared the bee-derived algorithm to “omniscient” (future-knowing) and standard human algorithms.
  • “Bees, even without knowing the future, they were coming within, like 20, 15% of the optimal behavior. In a bunch of their tests, the human algorithms didn't even come close.” (34:04, D/A)
• Open Source Spirit:
  • Sunil’s thesis defense committee’s first question: “Have you patented this?” (34:40, A)
  • But he’d already published it, giving it away for free.

7. Impact and Broader Applications

• Adoption:
  • “Server farms all over the world worked this bee algorithm into the Internet.” (34:59, A)
• Efficiency:
  • “Made it 10, 20% more efficient, which means…we have the bees to thank for the Internet being this place where you can get whatever you want.” (35:08, A/E)
• Beyond the Internet:
  • The approach migrated to other fields: forecasting, car design, MRI imaging, wood quality control, exchange rates, and more.
• Wider Philosophical Reflection:
  • “It’s throwing away the future. It is only responding to the present.” (37:17, C)
  • The bee solution operates “at the edge of the present,” focusing on robust feedback loops rather than risky prediction.

Notable Quotes & Memorable Moments

• “Why is it that at the very moment when I and the world want to access something the most, that's when I can't access it?” — A (02:22)
• "The colony is intelligent in some way that the individual bees are not." — E (08:53)
• "Equilibrium...even though the bees don’t have stopwatches, they equalize the round trip time." — E (17:29)
• “If one flower patch has a smaller round trip time than the others, send more bees there.” — E (18:54)
• “We have the bees to thank for the Internet being this place where you can get whatever you want.” — A (35:13)
• “It’s throwing away the future…It is only responding to the present.” — C (37:17)
• “You have made the Internet so enjoyable that you have cost me days and hours, potentially even years of my life. So I should be...I should be mad at you.” — A to D (40:35)

Important Timestamps

• 00:23 – Introduction to Sunil Nakrani
• 01:30 – 9/11 and witnessing Internet collapse
• 03:35–04:48 – First meeting: Sunil and Craig Tovey, the bee-paper moment
• 05:46–07:11 – Tom Seeley’s bee research background
• 12:08–15:13 – Cranberry Lake experiment setup, bee personalities and behaviors
• 17:29–18:42 – Finding the hive’s equilibrium, efficiency rules
• 24:44–25:55 – Using the Hamster Dance as an Internet overload metaphor
• 28:56–29:50 – Realizing direct parallels: bees/servers, nectar/web traffic
• 34:04–34:44 – Human vs. bee algorithm efficiency; patenting (or not)
• 35:13–36:17 – Internet and real-world outcomes of bee algorithm
• 37:17–37:54 – Philosophical reflection: “edge of the present”
• 40:35 – Latif jokes about internet enjoyment costing time, thanks to the bee algorithm

Summary Table: The Bee Algorithm

| Bee World | Internet World | Algorithmic Rule | |-----------------------------|----------------------------------------|-----------------------------------------------| | Bees seek closest nectar | Servers seek most popular website/video| Allocate more workers to where return is quickest | | Waggle dance = Recruitment | Server 'ping' or digital nudge | System rebalances as conditions shift | | Hive achieves equilibrium | Server farms rebalance under load | All without a central planner |

Conclusion

"Time is Honey" vividly demonstrates how looking to nature’s time-tested strategies can transform our technology. The episode offers not only a compelling origin story for a foundational Internet protocol but a meditation on collective intelligence, the power of present-focused feedback, and the sometimes surprising ways simple rules can produce stunningly effective solutions. If you’ve ever marveled at the seamless streaming of a viral video, thank a bee—and the researchers who paid close attention to their dance.

For further reading:

• Tom Seeley’s latest memoir, Piping Hot Bees and Boisterous Buzz Runners
• Terrestrials episode: “The Crystal Ball: Honeybees Who Predict the Future”