AI Hope

This episode is a little different. It’s a dharma-like talk I gave to a small sangha at Radiance Yoga this past Saturday. It was a windy fall morning, with the leaves coming off the trees and change literally in the air. Trees handle impermanence with ease. Most of us, myself included, struggle with it, especially when the change is unwelcome, and AI has become one of the most unwelcome changes in many people’s working lives.I walk through how I came to Plum Village practice, from a childhood of iron Buddhas and incense to the retreat Caitlin brought me to, and why impermanence stuck with me through the years.. Staying relevant across 30 years of technology change, from fax machines to the web to AI, turned out to have very little to do with technology and everything to do with mindset. I also share how last year’s business collapse and my father’s death two weeks later sent me back to the mat, and how those morning sits became Now Is Gone.From there, I apply the Four Noble Truths to AI. We suffer when we cling, whether to absolutes like “I will never touch this” or “we’ll cut 30 percent of our workforce,” or to the way our kids, our bodies, and our careers used to be. I can’t stop every software company from bolting on a chatbot or every data center from going up without care for its community.I can voice my dissent, and I can use the technology where it genuinely helps, like supporting underfunded global health work in India and Africa. My hope is that we pause, reflect, and use AI only where it serves our North Star, like water finding its way to the sea.Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts! 1:36 AI as the latest change at work 3:37 Why staying relevant isn’t about technology 4:46 Impermanence and the Four Noble Truths 10:38 Surrender and letting go of what was 11:36 Dukkha: suffering and clinging 16:18 What I can’t stop about AI, and what I can do 18:27 Letting go of absolutes and binary thinking 21:43 Pause, reflect, and respond from your North Star 23:23 Taking stock and discarding what doesn’t serve you

She helped shape the nation’s tech security strategy from the White House. Now she helps companies clean up after sprinkling AI on everything like glitter.Camille Stewart Gloster has spent her career where emerging technology meets security. She served as the first deputy national cyber director for technology and ecosystem security at the White House, led global product security strategy at Google, and now runs CAS Strategies, a cybersecurity and emerging tech advisory firm. She is also the author of The Insider You Built.AI is nothing new in cybersecurity, where it has powered anomaly detection for years. Camille’s wake-up call came when ChatGPT launched and her mom called to ask what it was. After leaving government to help companies adopt AI, she found many of them “just kinda sprinkling some AI on things.” Like glitter, it looks fun until you’re the one who has to clean it up.Her biggest pet peeve is how organizations hand agents the keys to the kingdom. Agents are not people, she cautions, yet they act inside your organization with real access and authority, making decisions on your behalf. We traded intern analogies (plus a hypothetical agent that answers a request for a cheeseburger story by ordering actual cheeseburgers on DoorDash) and landed in the same place: agents need specific instructions, clear boundaries on what they should not do, and human checks.Camille hopes we land somewhere between Terminator and tech utopia, closer to the utopia, where AI helps solve challenges like climate change and health outcomes and gives us time back to be human. Getting there depends on people making intentional, risk-based decisions about AI, the same way they do about everything else in their lives.0:49 From the White House and Google to CAS Strategies2:40 Sprinkling AI like glitter, and who cleans it up3:51 Building an AI chief of staff without email access5:14 Why agents are like hiring a new employee7:26 Smart enough to be dangerous8:49 Camille’s hope for AI: intentional, risk-based decisionsLearn more about CAS Strategies at https://www.cas-strategies.com/aboutGet the Insider You Built: https://www.amazon.com/clp/1394430892

Data centers have dominated the AI news cycle lately, from local concerns about noise and heat to questions about who pays for the added strain on the electric grid. In this solo episode, I take stock of the build-out and why I think it’s happening with very little mindfulness. A conversation last week likened it to the railroad boom of the Industrial Revolution, when tracks went down everywhere, towns boomed or withered depending on where the line ran, and nobody planned for the environmental damage.Two questions rarely come up in the coverage. First, how much of this capacity is actually needed? Every frontier lab is building its own, with no real effort to treat compute as a shared resource that could lower the environmental impact. Second, why are we running every query through massive general-purpose language models? I compare them to department stores, where everything is on the shelf and none of it is as good as it could be.Running makes a good example. The AI coaching features in Strava, Runna, and Garmin lean on frontier models, and as a coach I think they suffer for it. A narrow model trained only on running knowledge would likely coach better and draw far less from the grid.So where’s the hope? I’d like to see companies build data centers alongside environmental groups and communities, pay for their own grid impact instead of passing it to taxpayers, and invest in narrower, more efficient models. For the rest of us, it comes down to mindfulness. Before reaching for a chatbot, ask whether the use case is warranted or just a dopamine hit. Is the scratch really worth it?0:58 Environmental and grid concerns2:22 How much capacity do we really need?4:11 AI as a department store6:12 Where the hope lies

Last week I gave efficacy versus efficiency about a minute. This week it gets the whole episode. On Thursday I spoke at the GAIN Conference to business-to-government marketers, a room full of contractors and professional services firms still recovering from DOGE while facing heavy pressure to adopt AI. The distinction landed immediately, and it’s resonating with the nonprofits and professional services firms I work with, too.Efficiency has become AI code for cut costs, cut people, cut, cut, cut. Employees know it, and few employers have promised to bring them along if AI succeeds. Meanwhile, this year’s labor data suggests the promised productivity gains largely haven’t shown up. That leaves a hard question many executive teams can’t answer: beyond doing things cheaper, how does AI help you reach your business goals?Efficacy is where the hope is. It’s the mission, the North Star, and the brand promise to the customers you serve. If AI helps me reach more people, or improves the quality of what I deliver to the people I already reach, that’s efficacy. It also gives leaders a simple filter for use cases: is this on mission, and does it make us more effective? So what’s your North Star, and which AI use cases actually move you toward it?0:36 The GAIN Conference and the federal market’s AI moment2:23 Efficiency as code for cut, cut, cut5:30 Efficacy as the mission and North Star7:31 Choosing AI use cases that stay on mission

This solo episode of AI Hope starts with a question I’ve been chewing on for months. AI brings a long list of real problems, from job losses and data center sprawl to cybersecurity, rogue agents, and what I call the dumbification of society. Hollywood and the press amplify the existential fears because doom sells tickets and gets clicks. Meanwhile, industry hasn’t offered a real answer, and few of us expect the federal government to act on our behalf.My answer is humans. I can’t control what Washington does, but I can control how AI gets adopted in my own organization, and so can you. That starts with use cases that serve your mission instead of reckless, use-it-for-everything deployment.It also means knowing the difference between efficacy, which is achieving your mission, and efficiency. When I hear executives tout AI efficiencies, I increasingly hear code for layoffs, and unless retraining and redeploying people is part of the plan, you should see yellow and red flags.I also get into the AI lobby, why elected officials only move when they’re in hot water, and why every agent needs built-in human triggers instead of rogue internet access. Even though I work in this business, I think the frontier model bubble needs deflating. These models are a department store: You get a little of everything, and none of it is great. I expect niche models and SaaS platforms to gain ground once the big IPOs pass.AI’s biggest problem is that we’ve let it go unchecked, deploying it with a shiny-object attitude that it would cure the world. The fix is us, whether that’s a human check inside a workflow or a bigger strategic call about how your organization uses the technology.Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts!0:42 Is there one answer to all of AI’s problems? 2:23 The answer is… 6:37 The department store problem and the rise of niche models