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 rebrand
3:17 Can AI be used for good?
8:15 Who is really dangerous?

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

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

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

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 

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

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” moment
0:02:55 Building Fedideck in Lovable
0:05:20 Learning to manage AI, not just use it
0:07:22 Pushing back on AI hype
0:09:18 Cutting podcast editing to one-to-one
0:10:31 Where to find Seth

How to find Seth: https://social.sethgoldstein.me

The Entrepreneur’s Enigma podcast: https://entrepreneursenigma.com

Fedideck: https://fedideck.app

Self Actual's CTO on self-honesty, feeling seen, and the year he was anti-AI

Jeremy Wright and I have known each other for two decades, dating back to the early social media days, and he has always been the one pushing further into the technology than the rest of us. In the AI arena, he’s continued that approach building Echo, an AI that didn’t mimic him online, it actually knew him: how he worked, how he showed up as a dad versus a co-founder versus a writer in creative mode. 

That project became the seed for Self Actual, the AI platform. Jeremy now serves as CTO, and the seat is staying open for him as they raise money.

Jeremy spent a stretch of his own life anti-AI, apologizing to people when he first started talking about what he was building. The shift came out of a hard year: aA major health crisis took him offline for months, his father was going through his own crisis at the same time, and when 

Jeremy came back online he stumbled into a friend's 30 Days of AI challenge with nothing but recovering brain energy and his own sideways way of working through things rather than following anyone's recipe. Two weeks in, he showed what he'd built to that same friend and got back the reaction that told him this was bigger than a personal project.

Watch or listen to Jeremy Wright on YouTube! Don't forget to subscribe!

Jeremy's insistence that building an AI that knows you requires knowing yourself first. I am impressed with the self-honesty it took to build Echo as much as the product itself. 

He told me about one of his most advanced users, whose Echo helped her realize she was autistic, and she cried for three days, not from sadness but from finally feeling seen. He is hopeful that an AI built to notices your flaws, and how to work with them rather than around them, is worth the conversation.

Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts!

0:34 Who is Jeremy Wright and what is Self Actual 
0:58 Building Echo, the AI that goes further than a digital twin 
1:52 The Jarvis analogy: an AI customized to you 
2:47 Jeremy's proudest AI success: the self-honesty it took to build Echo 
3:47 The AI skeptic who ended up building an AI company 
4:16 A health crisis, 30 Days of AI, and a two-week epiphany 
4:47 Wtfisecho.com and the sideways path to clarity 
5:41 The user who discovered she was autistic through Echo 
7:13 Where Jeremy sees AI benefiting society most 
7:54 Parting thoughts and how to connect

Fobby has spent the years since using AI as a genuine day-to-day tool rather than a talking point, in an industry that runs on paperwork, disclosures, and timelines that punish anyone who falls behind. Fobby doesn't think AI replaces mortgage professionals, he thinks the mortgage professionals who use AI replace the ones who don't. 

He's watched that gap widen inside his own team over the past year and a half. It's the same adaptation story that's played out since someone first hitched a horse to a cart, except this time the stakes show up in loan timelines instead of miles walked.

Fobby's read on where AI goes next in mortgage is less about automation and more about foresight, using AI to catch the fires in a loan file before they start rather than scrambling to put them out, and building AI agents into recruiting and client outreach so his own time opens up for the parts of the job that actually require a person. He's also watching regulation land, TCPA rules already apply and Fannie Mae's AI governance framework takes effect August 6th.

His hope for the industry, and for anyone listening, is straightforward: stop treating AI like a verdict on your career and start treating it like a tool, because a car can take someone to the hospital or it can do damage, and the tool was never the deciding factor. The technology doesn't decide who wins. The willingness to adapt does.

Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts!

1:20 The ChatGPT moment that changed how Fobby saw everything 

3:01 Why AI adoption will separate mortgage pros who thrive from those who don't 

4:25 His "now or never" moment diving headfirst into AI 

5:09 Using AI to catch the fires in a loan file before they start 

6:24 What Fannie Mae's incoming AI governance means for mortgage 

8:03 Fobby's advice: stop fearing AI, it's a tool like any other 

8:26 Where to find Fobby Naghmi

This week I interviewed David Berkowitz, founder of AI Marketers Guild and a longtime CMO now working with Mailchimp, about building real products without writing a line of code. Berkowitz shares a personal success story: teaching himself to ship landing pages, full sites, and usable products with AI tools, despite knowing only a sliver of HTML and none of the languages that make up a developer's toolkit.

David built his product line on Lovable, after starting with Base44, including Extra Lovable, a site that manages his other Lovable projects, and imfindable.com, a job search tool that went from idea to public launch in two days. He walked through why that speed changes the math entirely: a project that once cost tens of thousands of dollars and months of agency time now costs about fifty dollars in AI credits and a weekend of his own attention.

That thread runs through my own experience too, including a running clothes calculator built start to finish in four hours, and the two swap stories about how fast the ground has shifted since their social media days together. 

David also traces AI Marketers Guild back to 2022, when early access to the OpenAI Playground at Mediaocean produced a mock press release so convincing that his CMO thought it was too polished for an early beta model, months before ChatGPT ever launched. He closes with his hope for AI: unlocking the creative and analytical potential in people who never had the tools to bring their ideas to life before, and letting them finally bring their whole selves to their work.

Listen today on Spotify, iTunes, YouTube, or wherever you get your podcasts!

1:02 builtthatwithai.com Showcase 

1:31 Building Without Code 

2:32 Favorite AI Platforms 

5:09 Origin of AI Marketers Guild 

7:52 AI's Hope for Business 

9:04 Go Knicks

David on LinkedIn: https://www.linkedin.com/in/dberkowitz/

Build with That: https://highcaliberai.com/built-with-ai

AI Marketers Guild: https://aimarketersguild.com/

Geoff Livingston interviews Phil “Phil Gerb” Gerbyshak, a high school IT teacher and Substack writer, about using AI as a practical survival tool. Gerbyshak shares a personal success story: Walking into a new teaching role with no curriculum and using AI to navigate it. Phil used ChatGPT (plus some Gemini and Copilot) to build lesson plans, tests, and course materials for programming and video game design, saying he wouldn’t have made it through the year without it. 

Phil describes generally positive acceptance from fellow teachers, contrasts that with backlash he’s seen in marketing, and attributes resistance to fear and job-loss anxiety amid layoffs. He also discusses using AI as a developmental editor for his upcoming book, Slow Happiness (expected in October), and closes with a “happy tip” to slow down and enjoy life.

01:50  Teachers Embrace AI

03:11 Marketing Backlash

06:00 Slow Happiness Book

07:06 AI as Editor

08:46 Hopes for AI

09:25 Happy Tip 

Follow Phil on Linkedin: https://www.linkedin.com/in/philgerb/

Phil’s Happiness Practices project: https://happyaf.substack.com/

Mastis shares a personal AI success story: using an LLM to plan a winter trip to Greec. She also shares using AI tools for self-coaching, translating her family history into Greek, and communicating via translation apps to connect with her ancestral “martyr village,” meet locals, and speak with a survivor who fled as a child during wartime fires. 

She recalls ChatGPT’s launch as a “now is gone” moment that reignited the excitement she once felt for augmented reality, contrasting it with newsroom resistance to new technology. Mastis is most optimistic about medical AI, citing at-home diagnostic tools and augmented-reality-assisted training that could reduce medical errors and unnecessary ER visits.

01:30 AI Trip Planning Greece

02:50 Finding Ancestral Village

05:46 ChatGPT Beat Breakthrough

07:55 Newsroom Resistance to AI

09:16 Hopeful Future Medical AI

You can find Lindsey on LinkedIn here.
Check out her company Blue Moves Media here.

Jeannie Walters, USA Today bestselling author of Experience Is Everything and founder of Experience Investigators, joins Geoff Livingston for the second episode of AI Hope. They explore how AI is reshaping customer experience, from journey mapping to the front lines of healthcare.

Walters shares her AI home run: using vibe coding tools like Lovable to rapidly prototype ideas as a founder, turning concepts into working demos that her team can then refine, which is dramatically accelerating how she brings her vision to life.

They discuss using AI as an accelerant for customer journey mapping, warning against organizational paralysis in pursuit of perfect, end-to-end maps when a back-of-the-napkin exercise can spark faster action. Walters emphasizes that AI's real power is in pattern recognition and turning data into insights, but only when anchored to a clear mission and measurable goals.

Walters also shares her vision for AI in patient experience, highlighting how AI can remove administrative friction like delayed lab results or heads-down documentation. Instead, she sees a future where AI frees clinicians to be fully present for more human, meaningful interactions.

Listen Today!

You can follow Jeannie on LinkedIn: https://www.linkedin.com/in/jeanniewalters/

Get Experience Is Everything at every major bookstore.

Learn more about her business and book at experienceinvestigators.com

Aaron Strout, author of Wired for Purpose, joins Geoff Livingston on Episode 1 of AI Hope to discuss humanity as a differentiator in a digital world and how to approach AI with rational optimism amid reactive, toxic social media. 

Strout shares early lessons using generative AI tools, both helpful outputs and hallucinations then describes how AI now acts as a thought partner that saves him significant time across his work, podcasting, and content creation.

 They discuss staying calm under pressure (“flight attendant energy”) during Now Is Gone moments, experimenting with AI tools without getting attached to any single platform, and encouraging people to start using accessible AI like Gemini. 

Strout outlines AI’s potential in healthcare, including freeing clinician time, enabling synthetic trials and real-world data analysis, and accelerating pattern-finding and research, emphasizing technology-led but human-guided use
 
 Aaron is on an East Coast mini book tour to Washington, DC, New York and Boston (6/16-18). Get more details on his book site:  https://getwiredforpurpose.com/

You can follow Aaron directly on LinkedIn here: https://www.linkedin.com/in/aaronstrout/

And you can listen to the Reaching Higher Podcast here: https://reachinghigherpod.com/