AI Hope

Geoff Livingston

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Technology

What's your AI success story? What made you realize you had to change? And what does a hopeful future with AI look like to you? AI Hope with Geoff Livingston asks three simple questions of people doing the hard work of adapting and thriving in the AI era.

4d ago

Ep. 15: The Answer to AI Problems Is...

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
Oct 4

Episode #14: Can AI Be Used for Good?

This week's AI Hope is another solo episode, part of an experiment to see how these commentaries compare to guest interviews. The topic is AI for good, which feels like a radical idea given the week's news. I open with quick reacts to ChatGPT's new agents and the press conference that rebranded AI as super intelligence, a label that still strikes me as ironic for anyone who uses these tools every day. So can AI be used for good? Yes. I'm working with two nonprofits right now, one focused on the environment and one on global health, and both see AI as a force multiplier for mission impact in a sector that never has enough resources. The politicized "it's evil, we won't use it" stance misses that. Much of the environmental cost comes from oversized large language models most professions don't need, and many of today's "agents" are really automations with probabilistic decision logic. Is AI evil? No. The danger comes from big tech companies and the government deploying it without rails while offering platitudes about responsibility. That's where we need to keep our eye on the ball. Let me know whether you prefer this monologue format or the guest interviews. I also touch on the industry's new moral responsibility agreement, which reads like more bluster from companies focused on building value for their investors, and on Anthropic's standoff with the Department of War. 0:36 The superintelligence rebrand3:17 Can AI be used for good?8:15 Who is really dangerous?
Sep 28

Episode 13: GovCIO's Amy Kluber on AI in Government and the Newsroom

Amy Kluber and her team at GovCIO cover how federal agencies adopt technology including AI. We recorded this conversation on September 22, the day after AI came up at the UN, capping six weeks of news that ran from security incidents to superintelligence talk to government regulation. As to those comments at the UN, Amy is reserving judgment on superintelligence until she sees AI operate at that level for her. Personally, Amy’s path into AI was gradual. As federal agencies started prioritizing it, she decided she couldn't judge the technology without using it herself. Now she's working through what AI means inside a newsroom: Whether you can trust its output, if your audience will trust you for using it, and what policies you need. Her most useful build so far is a Copilot agent that pulls four years of Flywheel Awards nominations out of scattered forms, Word docs, spreadsheets, and SharePoint folders, turning them into a database her team can ask questions of. Her starting point was the right one: find the annoying, repetitive process first, then apply automation to it. Amy's AI hope is that it shrinks the distance between people and what they're capable of doing, with human judgment still in the loop. For government, that means less time on hold, faster access to services and benefits, and quicker paths to a human when it matters. 1:09 Six weeks of AI news in a nutshell 2:25 AI in the newsroom: trust, ethics, and new policies 3:23 Why her adoption was gradual 5:14 Curing writer's block 5:34 Building a Copilot agent for the Flywheel Awards 7:17 Automating the ankle biters 8:30 How federal agencies are applying AI 8:52 Amy's AI hope 10:02 What AI could do for the American citizen
Sep 21

Episode 12: Is AI Regulation Actually Coming? Probably not from Washington.

This week's AI Hope is a solo episode. With everything that's happened, claims of civilization ending AI, cybersecurity failures, data center fights, job displacement, AI slop, IPO hype, is regulation of the AI industry actually coming? My answer is not on the federal level, not for at least the next two years. Congress isn't functional right now, and big tech is lobbying hard, with a lot of that influence flowing straight to the administration. The administration's own instincts lean toward supporting corporations over individual freedoms, whether that's LLMs claiming the right to scrape anyone's content to train, or defense-related rules aimed at companies like Anthropic. The real checks on overreach right now are happening on the state and local level and in the courts instead. More than fifteen hundred bills to regulate AI have been introduced, with serious legislative efforts underway in California and Texas, and local fights breaking out over data centers. And individuals suing frontier model companies directly, the case that reined in Meta's algorithmic harm to teenagers, the authors' case I was part of against Anthropic, are proving to be the most effective check we have. Abroad, the EU now requires AI content creators to disclose or watermark AI-generated material, and Anthropic complied immediately. Watch or listen to this episode on YouTube! Don't forget to subscribe! . I don't think meaningful federal regulation arrives in the next couple of years. But voters are angry enough that politicians on both sides are starting to notice, and that's worth watching. In the meantime, if you're running a company, assume regulation is coming from somewhere, Europe, a big state, a judge, and get ahead of it: be mindful with employee and customer data, skip the surveillance-style tools, and get real permission before you use anyone's information. Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts! 0:13 Why nobody trusts AI companies right now 0:36 Why federal regulation is stalled for the next two years 1:50 Big tech's lobbying grip on Congress 3:04 The administration's pro-corporate tilt on privacy and IP 3:26 How the EU is leading with disclosure and watermarking rules 4:34 State and local bills, from California to the data center fights 4:55 Civil suits: how individuals are reining in Meta and Anthropic 5:44 What corporations should do to get ahead of regulation
Sep 13

Episode 11: Seema Alexander on the Great Business Rewiring with AI

Episode 11: Seema Alexander on the Great Business Rewiring with AI A filmmaker showed her AI could turn six months of work into minutes. Geoff Livingston August 27, 2026 Seema Alexander is the founder and CEO of Disruptive AI, the host of the AI CEO podcast, and the alumni chair of DC Startup Week. Four years ago, right as ChatGPT hit, she was asked to advise Magic ML, one of the earliest agentic development platforms, later spotlighted at Google I/O. As part of her efforts, Seema worked with a cinematographer trying to compress six months of storyboard work into minutes, chaining ChatGPT, Midjourney, and Runway ML together. She told me it was the moment she knew AI was "10X bigger than the advent of the internet." Seema has not looked back since, and now calls this stretch of business history the great business rewiring. She notes how implementing AI is surfacing dysfunction: the corporate silos, the legacy systems, the initiatives that never quite got off the ground. Watch or listen to Seema on YouTube! Don't forget to subscribe! Today, Seema is building intelligent operating systems that connect the unstructured data companies already have, including a current project supporting portfolio intelligence for one of the largest international development banks in the world. She is also building a portfolio of AI native companies: One using AI built on human synthetic data to disrupt qualitative market research on the way to giving humanoid robots emotional intelligence, another building adaptive, personalized commerce through a company called Bridge. Her hope for AI is grounded in her kids, ages twelve and sixteen. She is betting the majority of people will use this technology for good, that it will finally move the needle on precision medicine, and that it can strip enough friction out of work that a single person and an AI agent can build a real business. Seema’s new conference, AI Business Con, runs March 2 and 3 at Fuse in Arlington,. Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts! 0:45 Meet Seema Alexander, founder of Disruptive AI and host of the AI CEO podcast 1:26 The moment AI clicked: advising Magic ML and watching a cinematographer compress six months into minutes 4:37 Why she calls this moment the great business rewiring 5:39 Building intelligent operating systems, including a portfolio intelligence project with a major development bank 7:15 Her portfolio of AI native companies: human synthetic data and adaptive commerce with Bridge 9:42 Her hope for AI: precision medicine, less friction, and businesses of one 12:49 AI Business Con and keeping humanity human as AI gets smarter 13:29 Where to find Seema and register for AI Business Con
Aug 16

Episode 10: AI Watermarking and Its Implications

Europe just moved on AI slop. The new rule forces the big AI companies and the startups riding their coattails to watermark whatever they generate, images and text alike, in a way ordinary users like you and me cannot strip back out. Anthropic was the first major player to implement mandatory watermarking, and given Europe's market size, the rest of the industry will fall in line whether they are enthusiastic about it or not. I think the effect lands unevenly, and mostly in a good way. Writers and artists who create without leaning on these tools stand to benefit, because watermarking makes their work newly distinguishable in a feed that has been drowning in AI output. Companies with a legitimate reason to use AI, a small organization that could never otherwise afford a training video vendor, for instance, get more transparency without more shame attached to using it. Where the rule will probably do the least is in everyday content consumption. Platforms that already profit from AI generation, and Meta is the example I keep coming back to, have little incentive to throttle their own output just because it carries a label, and most people scrolling past a watermark are about as likely to read it as they are the terms of service on a credit card statement. The disclosure opens up a real artisan's market. An ad agency or a studio can now say a piece of work is human made and mean something specific by it, the same way a Bourne movie's practical stunts read differently than overwrought CGI-ridden action adventures. Music might be the one category where people keep caring regardless of what the market does elsewhere, since a computer-generated jingle is a much harder sell than a human one. I do not think that changes. I do not expect watermarking to end the slop problem. Bad actors have had a decade-long head start on every enforcement mechanism built to catch them, and that head start does not close because of one regulation. But I still call this a hopeful moment, because it is an industry being pushed into a transparency it would not have chosen for itself, and a market finally starting to reward the people who are still doing the work by hand. 00:00:13 The EU's new AI watermarking regulation 00:01:05 Anthropic moves first, the rest of the industry will follow 00:01:50 How social networks will use watermarks (and why Meta won't) 00:03:20 Why bad actors will always stay ahead 00:03:48 Where AI-generated content is genuinely appropriate 00:04:47 The credit card statement problem: nobody reads disclosures 00:05:28 The rise of an "artisan's market" for human-made work 00:06:57 Music: the one place people still care 00:07:29 How each AI vendor will likely respond
Aug 9

Episode 9: Back to School with Gabriel Elias who Built His Own Classroom with AI

A high school history teacher explains why the best AI use in education looks nothing like ChatGPT homework. Geoff Livingston August 9, 2026 Gabe Elias teaches at Alexandria City High School, and has spent the four years since ChatGPT launched treating AI as a personal design tool rather than a classroom compliance problem. He jumped in the way he jumps into any new technology, testing that first release on history essay questions before it had many guardrails. Gabe was struck by how good some of the answers were even as he started poking holes in the rest.That instinct sets him apart from most of the teachers he talks to, who tend to write off AI the moment a student uses it to skip an essay. Gabe draws a sharper distinction by pointing to Teacher Pay Teachers, the marketplace where educators sell lesson plans to each other: for a decade, the best sellers weren't the most rigorous. They were made by teachers who happened to know Adobe Illustrator and Photoshop, because polish sells even when the underlying content doesn't change. AI puts that same design capability into the hands of any teacher, not just the ones with a design background. NotebookLM was a real breakthrough for Gabe, since it only draws on sources he uploads, so it won't invent standards or wander off the curriculum. He used it to build short explainer videos in his ESL students' own languages, learning the Pashto and Dari scripts himself just to caption them correctly. Then he found Claude Cowork, and the scale changed again. Instead of the fragile shared Google Docs his school runs on, where one stray keystroke wipes out a student's work, he started building entire lessons as standalone HTML apps: a decolonization scenario game where students make choices and earn points his old study guides embedded as interactive toggles an export console that sends everything a student does straight back to him. Asked for his vision of AI in education, Gabe gives two answers. The hopeful one is that skilled teachers will finally be able to differentiate content for every learner the way they've always wanted to but never had time for across 150 or 250 students. The harder one is that he sees real risk in schools using the same capability to thin out the workforce and hand curriculum design to companies that don't understand how kids actually learn. His big hope is that capable teachers stay in the loop as guides. Teaching remains a human-to-human skill no matter how good the tools get, and the difference between using AI as a design tool and just turning kids loose on ChatGPT is the same difference that existed between using the internet well and having kids Google random answers. Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts! 0:57 The ChatGPT Moment That Hooked Him 2:16 Why This Is the Inflection Point for AI in Education 3:58 Discovering NotebookLM 5:17 Vibe Coding an Entire Classroom with Claude Cowork 7:22 The Nice Answer and the Mean Answer on AI's Future in Teaching 10:01 Where to Find Gabe
Aug 2

AI Hope Episode 8: Seth Goldstein on Becoming an AI’s Manager

Seth Goldstein, host of The Entrepreneur’s Enigma podcast, had his now-is-gone moment about two years ago when ChatGPT-4 landed when he finally sat down to test it against something he didn’t think it could do. It provided a working answer where he expected a wall. The clearest example he offered was Fedideck. Fedideck is a TweetDeck-style app he built for the Fediverse, the network of apps including Mastodon and Blue Sky that filled the gap Twitter left when it killed off TweetDeck. Seth vibe coded it in Lovable, got a usable version in about an hour, and then kept going. In total, he spent roughly $500 in AI credits and close to 200 hours of iteration on what he and thousands of others now use. What Seth kept coming back to was the shift in posture required to use AI well. Typing into a chat window instead of building it yourself means learning to manage rather than execute. This requires explaining the task coherently enough that the work doesn’t wander off and waste tokens on the wrong thing. Seth was blunt about the alternative: One shot everything and you get something that looks finished and works terribly. Seth’s hope for AI is unglamorous by design. He wants it to help people work faster, better, and more completely without ever removing the human from the loop, and he has little patience for framing that treats the technology as an inevitability rather than a tool. The proof, for him, is his own day-to-day workflow. Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts! 0:01:20 The “now is gone” moment0:02:55 Building Fedideck in Lovable0:05:20 Learning to manage AI, not just use it0:07:22 Pushing back on AI hype0:09:18 Cutting podcast editing to one-to-one0:10:31 Where to find Seth How to find Seth: https://social.sethgoldstein.me The Entrepreneur’s Enigma podcast: https://entrepreneursenigma.com Fedideck: https://fedideck.app

What's your AI success story? What made you realize you had to change? And what does a hopeful future with AI look like to you? AI Hope with Geoff Livingston asks three simple questions of people doing the hard work of adapting and thriving in the AI era.

Creator

Geoff Livingston
Years Active

2026
Episodes

15
Rating

Explicit
Copyright

© (c) 2026 Generative Buzz Incorporated
Show Website

AI Hope