Fifteen years of building companies together, one year into magentIQ, and Ian Barkin and David Brain use the anniversary to talk about what has not changed: people are what make it work. They get into why a company of only agents would be a pretty joyless place, how you actually train someone into an expert when the expertise is weeks old, and why the winning move is still asking not just whether AI can do something, but whether it should. Warm, reflective, and full of hard won practical wisdom.

(0:35) Founders journal: Nashville, live music, and the ongoing battle not to impulse buy cowboy boots

(1:02) One year of magentIQ: the anniversary all hands, and why a company of only agents would be a sad one

(11:59) Fifteen years back: the spreadsheet by the pool that started it all, and doing it again

(12:40) Inside the managed service provider world: the teams keeping small business running and fielding the AI hype

(16:55) Fable 5 is still down: why an outage is pushing people toward open source and portability

(17:36) The single vendor worry: clients asking how to avoid getting locked into one tool

(20:03) Can AI do this versus should AI do this: why the right tool sometimes predates AI entirely

(20:41) When an integration problem is not an AI problem: structured, cheaper, more governable options

(26:29) Proudly unsexy: why boring, dependable solutions quietly win

(28:17) You cannot build a house in an hour: the speed obsession and the value of constraints

(34:52) The new bottleneck: as code gets cheap to produce, human review and QA become the constraint

(38:59) Growing the talent AI cannot replace: building your own curriculum when the expertise is brand new

(44:37) Rethinking the forward deployed engineer: why process and re-engineering matter as much as code

(47:24) The expert paradox: everyone wants years of experience in tools that launched last week

Listen now, and tell us how your team is growing the people behind the technology.

Find us in your favorite streaming service:

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL

iHeartRadio: https://iheart.com/podcast/333292440

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].

Ian Barkin and David Brain get into what happens when you trust a single AI model too much, and what to do instead. A client's agent confidently got a healthcare policy answer wrong, which sets up David's most practical idea yet: a council of agents, several different models cross checking each other so no one blind spot slips through. From there, the quiet trend of organizations rehiring the very experts they let go, and why human judgment keeps proving itself the differentiator. Positive, practical, and a little bit cheeky.

(1:13) What's in this episode: AI employees, the council of agents, symbiotes, and a second grade confession

(2:25) Founders journal: Nashville, live music, and the managed service providers fielding "where did you host that vibe coded thing?"

(5:38) Fable 5 is back: a model released, pulled, and now re-approved, and what that says about depending on any single model

(7:55) Tiered access to frontier models: who gets the most capable tools first, and what it means for smaller players

(12:01) AI sovereignty and the kill switch: why where a model is governed is now part of your strategy

(22:48) AI employees and the accountability gap: how agents are quietly disrupting who owns the work

(24:10) Confident and wrong: a major report built on dozens of fabricated sources, and the guy in the pub problem

(25:20) Ian's second grade dictionary confession, and why made up facts are so convincing

(29:52) The story that started it: a single agent hallucinates on a healthcare policy question

(30:35) The council of agents: borrowing from crowdsourcing to have multiple models check each other for accuracy

(32:40) Why the council has to be multi vendor, and how it also protects you from a single model outage

(37:48) The great rehiring: carmakers, banks, and consultancies bringing back the experts they replaced when AI fell short

(41:21) The mediocrity trap: why hiring "good enough plus AI" costs you the experts you will need in five to ten years

(45:45) From robots to agents to symbiotes: the new label for an old truth, humans and AI working better together

Listen now, and tell us where a second opinion would have saved the day.

Find us in your favorite streaming service:

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL

iHeartRadio: https://iheart.com/podcast/333292440

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].

Ian Barkin and David Brain are back, and getting comfortable. This one ranges from World Cup soccer to the real bottleneck in AI, which turns out not to be tokens or building, but knowing what to build. They dig into why great ideas beat raw consumption, why last year's models are quietly the smart buy, and what a sudden model shutdown teaches every operator about not putting all their eggs in one basket. Plus a genuinely new idea about keeping the business running when the model goes dark, involving, of all things, senior centers.

(1:06) Founders journal: World Cup soccer, manufactured hydration breaks, and Europeans touring America

(8:39) Apple's WWDC, the AI Siri that is still not here, and the chip shortage driving up prices

(11:10) Token maxing and return on token: why using more compute just means more to wade through

(15:00) Why great ideas matter more than token maxing: the real constraint is imagination, not the ability to build

(18:00) The psychology of innovation: how to choose a few ideas and retire the rest without losing your best people

(22:15) Why older models are the smart buy: getting it right with last year's Gemini as prices keep dropping

(24:51) Commoditization, margins, and where AI pricing is heading

(30:00) Fable 5 gets shut off in 72 hours: a frontier model here one moment, gone the next

(37:17) The geopolitics of AI: models built on humanity's collective knowledge, and the push toward open source and local

(41:12) The ratchet effect and agentic business continuity: what happens when the model your operation runs on goes dark

(44:32) The senior center solution: the analog escrow of people who still remember the old way

(47:52) Staying model agnostic: why you prototype in the latest and greatest but build so you can switch

(50:46) The build update: the imagination platform, the holodeck, and why your co-pilot is now a sheep

Listen now, and tell us where your own imagination gap is biggest.

Find us in your favorite streaming service:

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL

iHeartRadio: https://iheart.com/podcast/333292440

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].

Ian Barkin and David Brain go behind the scenes on the agentic platform they have been building, and dig into why the biggest model is rarely the right one, why this year's AI savings are quietly funding next year's budget, and why going faster will not fix a process that was never designed for AI in the first place. A practical, pattern matching conversation from two people who have lived through this cycle before.

(0:00) Why there is nothing safe and low hanging in AI transformation, and how big, bold, brash expectations slow you down

(0:55) Founders journal: black t-shirts, decision fatigue, GLP-1s, and the great tailor shortage

(10:54) The Bain study: AI is not delivering the expected impact, and next year's budget is built on savings that never showed up

(16:08) The underpants gnomes problem: use AI, question mark, value is not a plan

(21:30) Capturing the thinking, not just the steps: David on building an agentic platform rooted in process re-engineering

(28:28) The 10x myth: why companies keep trying to 10x a broken process instead of redesigning it

(30:49) Why the forward deployed engineer puts too much on one set of shoulders

(38:45) Orchestrating the right model: vendor lock-in, and why you don't need a McLaren to drive down the street

(40:31) Small and custom language models: lower cost, lower energy, and good enough for most real work

(43:49) Chinese and open source models, the nine month gap, and the regulation that may be coming

(48:54) Recursive, self improving models and why a human master architect still has to stay in the loop

(53:07) The hype versus reality gap, told through the Siri that won't tell you if it's going to rain

Listen now, and send us your questions. We work through this stuff too.

Find us in your favorite streaming service:

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL

iHeartRadio: https://iheart.com/podcast/333292440

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].

People can plan a week of meals, diagnose an ailment, and draft a proposal with AI on a Sunday, then walk into work on Monday and find everything still stuck in silos, legacy systems, and tribal knowledge. In Episode 9 of The magentIQ Show, Ian Barkin sits down with industry analyst and icon Phil Fersht to name that dissonance, the velocity gap, and to talk honestly about why so few enterprises are closing it, what real AI transformation actually demands, and why authentic human voice matters more than ever in an age of AI slop.

Phil Fersht is widely recognized as one of the world's leading analysts on reinventing business operations to exploit AI and global talent. He is the founder and CEO of HFS Research, the analyst who authored the first report on Robotic Process Automation in 2012, and the voice who coined OneOffice, trademarked the Generative Enterprise, and more recently framed the rise of Services-as-Software. He is also the author of Horses for Sources, the most widely read blog in the global services industry, now in its nineteenth year, and a three time Analyst of the Year.

Here is what Ian and Phil unpack:

• Why the velocity gap exists, the widening distance between how fast people adopt AI personally and how slowly their organizations actually move, with only a small fraction of the global 2000 making real headway
• Why LLMs are not AI transformation, and why meaningful change requires CEO and COO sponsorship and a wholesale rewiring of people, data, and process rather than incremental tweaks
• Why authentic voice is becoming a differentiator as AI slop floods inboxes and feeds, and how to use these tools for research and structure without losing the soul of what you write
• Why the future of services belongs to nimble, process led, outcome driven firms, and why being close to the operation beats dropping a full stack engineer into a problem

The throughline is energizing rather than alarming. The organizations that win will be the ones whose leaders treat AI as a fundamental rethink of how the business runs, stay close to their processes, and keep real human judgment and voice at the center. The opportunity to leap ahead is wide open, especially for the small and nimble.

Listen now and tell us how wide the velocity gap looks in your own organization.

Resources from Phil Fersht:

Podcast, From the Horse's Mouth, Intrepid Conversations with Phil Fersht:

• Apple: https://podcasts.apple.com/us/podcast/from-the-horses-mouth-intrepid-conversations-with/id1769638427
•  Spotify: https://open.spotify.com/show/7uS2O3N9JdQnpWLT22qy5O
•  YouTube: https://www.youtube.com/@FromTheHorsesMouthPod

Blog, Horses for Sources: https://www.horsesforsources.com/

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].

The company burning billions on AI is somehow getting less done. In Episode 8 of The magentIQ Show, Ian Barkin and David Brain dig into why, working through the mistakes the AI era keeps repeating even though most of them were supposed to be learned a decade ago.

Here is what Ian and David unpack:

• Why the era of unlimited tokens is turning, and why disciplined AI is proving cheaper and more productive than handing everyone a blank cheque to use as much as possible
• Why choosing the tool before defining the problem still trips teams up, and why a demo is no substitute for knowing where you are and where you actually want to go
• Why expecting staff to 10x themselves overnight is a tall order, and why the forward deployed engineer cannot be a whole team of specialists squeezed into one person
• Why speed for its own sake is not a strategy, and how the old trade off between time, cost, and quality still holds no matter how fast the tools have become

The throughline is encouraging rather than cautionary. The teams that win are the ones that map where they are, agree on the outcome they want, choose the right tool for the job, and give their people the design time and support to do it well. Enthusiasm builds momentum, but pairing it with context and a little hard won history is what turns an AI project into a real result.

Listen now and tell us which lesson you see playing out most in your own organization.

Find us in your favorite streaming service:

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL

iHeartRadio: https://iheart.com/podcast/333292440

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].

AI can draft a report in seconds, but it cannot put its name on it. In Episode 7 of The magentIQ Show, Ian Barkin sits down with Andreas Welsch, ten year AI veteran, LinkedIn Learning instructor, and author of two books written entirely by hand. Their conversation cuts past the hype to a question every leader is now living with: when output is effortless, how do you keep the quality, the judgment, and the accountability that make it worth anything at all?

Here is what Ian and Andreas unpack:

• What work slop really is, how AI output that just shifts the labor and the bottleneck onto whoever receives it, and why you are never off the hook for the quality of what you ship
• Why staying in the loop matters, drawn from Andreas's own experience letting AI build an app unsupervised and ending up with something he could not understand or trust
• How to enable people to use AI well, with real training and a learning curve, rather than throwing a tool over the fence and expecting productivity to appear
• Why the human edge becomes the differentiator as everyone converges on the same tools and data, and why people remain the heart and soul of a business

The throughline is refreshingly practical. AI is a powerful new tool, not a substitute for craft, and the leaders who win will be the ones who pair it with real expertise, keep people close to the work, and treat quality as something they still own. Smart, sharp, and hype free.

Resources from Andreas Welsch:

LinkedIn Learning courses: https://www.linkedin.com/learning/instructors/andreas-welsch

Books: 

• The HUMAN Agentic AI Edge: https://intelligence-briefing.com/human-agentic-ai-edge/
• AI Leadership Handbook: https://intelligence-briefing.com/ai-leadership-handbook/

Listen now and tell us how you are keeping quality and accountability in your own AI workflows.

Find us in your favorite streaming service:

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL

iHeartRadio: https://iheart.com/podcast/333292440

The whether of AI is settled, or at least that's what our webinar audiences told us. What people are hungry for now is the how. In Episode 6 of The magentIQ Show, Ian Barkin and David Brain answer the questions that came straight from the people in their recent webinars, share fresh survey data on where AI adoption really stands, and walk through a client who saved six figures a year by building AI in the right way, with people firmly in the loop.

In this episode, Ian and David unpack:

• What the latest survey data reveals about AI maturity, and why the sharp jump into production over six months is a genuinely positive signal
• Why token maxing a model to do a job a simple automation could handle is bringing a bazooka to a knife fight, and how to match the tool to the task
• The John Briggs story: how one founder saved 150 to 200 thousand dollars a year and built a smarter client portal by partnering rather than going it alone
• How confidence scores and human QA keep people in the loop, so you trust AI's answers without losing the ability to check its work

Find out why the operators who win are the ones who start where the pain is, choose the right tool, design for outcomes, and build AI and people to work better together. The opportunity is real, the wins are already happening, and the path from why to how is more achievable than the noise would have you believe.

Listen now and tell us how you are moving from why to how in your own organization.

Find us in your favorite streaming service:

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL

iHeartRadio: https://iheart.com/podcast/333292440

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].

Ian Barkin and David Brain are joined by Tom Davenport, President's Distinguished Professor of Information Technology and Management at Babson College and author of twenty-five books on AI, analytics, and the future of work. Tom, David, and Ian have co-authored numerous journal articles and a book on citizen development. Tom is a friend, collaborator, and expert with an unrivaled perspective on the agentic era. Welcome to Episode 5 of The magentIQ Show.

In this episode, Ian, David, and Tom unpack:

• Why process slop happens when everyone in a workflow uses AI to write and review the same documents
• The 30x gap between AI rhetoric and capability: 60% of companies have cut jobs, but only 2% have the systems to absorb the work
• Why the forward deployed engineer is just an AI-washed consultant, and why the next great job title might be the backward deployed engineer
• The return of the blue book, the lost generation of graduates, and what David plans to tell a room full of schoolboys about using AI well
• Whether humans can keep pace with agentic workflows, or whether they just become a dipping bird hitting the enter key
• The Thoreau origin of brain rot, and why teaching the next generation to think with AI rather than around it may be the defining educational challenge of the decade

Further reading and resources from the conversation

Co-authored by Tom Davenport and Ian Barkin:

• "All Hands on Tech: The AI-Powered Citizen Revolution" is the definitive guide to vibe coding in the agentic era. Drawing on years of joint research, Ian and Tom argue that the next wave of enterprise transformation will not come from a handful of technologists at the top, but from the millions of business users now equipped to build, automate, and innovate with AI tools. The book lays out how leaders can mobilize this citizen workforce, govern it responsibly, and turn it into a durable source of competitive advantage. Available at Amazon here. 
• "We're All Programmers Now", Harvard Business Review, 2023, HbrWe’re All Programmers Now
• "Harnessing Grassroots Automation", MIT Sloan Management Review, Mitsloanreview.mit.edu/article/harnessing-grassroots-automation

Co-authored by Tom Davenport and David Brain:

• "Before Automating Your Company's Processes, Find Ways to Improve Them", Harvard Business Review, 2018, HbrBefore Automating Your Company’s Processes, Find Ways to Improve Them

Ian Barkin and David Brain return for Episode 4 of The magentIQ Show, picking up where the headlines leave off. AI is everywhere at work right now, but the loudest signal is not better results. It is token maxing and layoffs justified by an AI story that often runs ahead of reality. Ian and David dig into what happens when leaders measure AI adoption by volume of usage instead of business value, and why that choice quietly rewires incentives across the entire organisation.

The conversation moves through the latest wave of org hacking making the rounds: Coinbase's one-person product teams, Jack Dorsey's ambition to cut his company down to two layers, and the broader fantasy that agentic AI can absorb the messy human parts of building products and running teams. Ian and David name the constraints those models ignore. The pace of a single operator. The bias of a single perspective. The management work that does not disappear just because you can generate a status report. They also surface a risk more leaders need to say out loud, which is the use of AI to manage performance when the system will confidently fill in blanks, hallucinate context, and send signals that ripple through motivation and retention.

From there the lens widens to enterprise AI implementation and the rising tide of AI consultancies, forward deployed engineers, and services arms trying to turn licences into outcomes. Ian and David tackle commoditisation, lock-in, the puzzle problem of orchestrating a thousand-piece agentic stack, and why buying into the inner circle of a model provider does not solve the hard part of deployment.

In this episode, Ian and David unpack:

• Why AI-driven layoffs are often a budget reshuffle dressed as transformation, and what is really being freed up
• One-person product teams as org hacking, and the speed and bias limits the pitch leaves out
• Flattening the org chart versus the real work of coaching, context, and care
• The risks of letting AI manage people when the model is built to sound confident, not to be correct
• Token maxing as a vanity metric that floods teams with low-trust output
• The graduate backlash to a decade of "AI will take your job" messaging, and what it signals about the next workforce
• Why enterprise AI implementation still needs governance, security, and services to land
• The Sunday tinkering and Monday reality framework for building real AI fluency
• A practical path for small and medium businesses: learn personally, prototype safely, then harden for production

The takeaway is constructive. The agentic workforce is real, the opportunity is genuine, and the operators who win will be the ones who design for value rather than volume, who keep the human craft of management intact, and who treat citizen creativity as an input to disciplined deployment rather than a substitute for it.

Listen now and tell us where you stand on the question shaping the agentic era: are you optimising for tokens, or for outcomes?

Find us in your favourite streaming service: 

iTunes: https://podcasts.apple.com/us/podcast/the-magentiq-show/id1896570951 

Spotify: https://open.spotify.com/show/033f2oKxnFr5fmcpdSHjSL 

iHeartRadio: https://iheart.com/podcast/333292440

Got a perspective worth sharing? We are always looking for guests. Reach out at [email protected].