The Tech Trek

This is an early conversation I am bringing back because it feels even more relevant now, the intersection of AI and art is turning into a real cultural shift.

I sit down with Marnie Benney, independent curator at the intersection of contemporary art and technology, and co-founder of AIartists.org, a major community for artists working with AI. We talk about what AI art actually is beyond the headlines, where authorship gets messy, and why artists might be the best people to pressure test the societal impact of machine learning.

Key takeaways

• AI in art is not a single thing, it is a spectrum of choices, dataset, process, medium, and intent

• The most interesting work treats AI as a collaborator, not a shortcut, a back and forth that reshapes the artist’s decisions

• Authorship is still unsettled, some artists see AI as a tool like an instrument, others treat it as a creative partner

• The fear that AI replaces creativity misses the point, artists can use the machine’s unexpected output to expand human expression

• Access matters, compute, tooling, and collaboration between artists and technologists will shape who gets to experiment at the frontier

Timestamped highlights

00:04:00 Curating science, climate, and public engagement, the path into tech driven exhibitions

00:07:41 What AI art can mean in practice, datasets, iteration loops, and choosing an output medium

00:10:48 Who gets credit, tool versus collaborator, and the art world’s evolving rules

00:13:51 Fear, job displacement, and a healthier frame, human plus machine as a creative partnership

00:22:57 The new skill stack, what artists need to learn, and where collaboration beats handoffs

00:29:28 The pushback from traditional art circles, philosophy and intention versus novelty

00:37:17 Inside the New York exhibition, collaboration between human and machine, visuals, sculpture, and sound

00:48:16 The magic of the unknown, why the output can surprise even the artist

A line that stuck

“Artists are largely showing a mirror to society of what this technology is, for the positive and the negative.”

Pro tips for builders and operators

• Treat creative communities as an early signal, artists surface second order effects before markets do

• If you are building AI products, study authorship debates, they map directly to credit, accountability, and trust

• Collaboration beats delegation, when domain experts and technologists iterate together, the work gets sharper fast

Call to action

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Most teams are approaching AI from the wrong direction, either chasing the tech with no clear problem or spinning up endless pilots that never earn their keep. In this episode, Amir Bormand sits down with Steve Wunker, Managing Director at New Markets Advisors and co author of AI and the Octopus Organization, to break down what actually works in enterprise AI.

You will hear why the real challenge is organizational, not technical, how IT and business have to co own the outcome, and what it takes to keep AI systems valuable over time. If you are trying to move beyond experimentation and into real impact, this conversation gives you a practical blueprint.

Key takeaways

• Pick a handful of high impact problems, not hundreds of small pilots, focus is what creates measurable ROI

• Treat AI as a workflow and change program, not a tool you bolt onto an existing process

• IT has to evolve from order taker to strategic partner, including stronger AI ops and ongoing evaluation

• Start with the destination, redefine the value proposition first, then redesign the operating model around it

• Ongoing ownership matters, AI is not a one and done delivery, it needs stewardship to stay useful

Timestamped highlights

00:39 What New Markets Advisors actually does, innovation with a capital I, plus AI in value props and operations

01:54 The two common mistakes, pushing AI everywhere and launching hundreds of disconnected pilots

04:19 Why IT cannot just take orders anymore, plus why AI ops is not the same as DevOps

07:56 Why the octopus is the perfect model for an AI age organization, distributed intelligence and rapid coordination

11:08 The HelloFresh example, redesign the destination first, then let everything cascade from that

17:37 The line you will remember, AI is an ongoing commitment, not a project you ship and forget

20:50 A cautionary pattern from the dotcom era, avoid swinging from timid pilots to extreme headcount mandates

A line worth keeping

You cannot date your AI system, you need to get married to it.

Pro tips for leaders building real AI outcomes

• Define success metrics before you build, then measure pre and post, otherwise you are guessing

• Redesign the process, do not just swap one step for a model, aim for fewer steps, not faster steps

• Assign long term ownership, budget for maintenance, evaluation, and model oversight from day one

Call to action

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Manufacturing is getting faster, messier, and more expensive when quality slips.

Daniel First, Founder and CEO at Axion, joins Amir to break down how AI is changing the way manufacturers detect issues in the field, trace root causes across messy data, and shorten the time from “customers are hurting” to “we fixed it.”

Episode Summary

Daniel First, Founder and CEO at Axion, explains why modern manufacturing is living in the bottom of the quality curve longer than ever, and how AI can help companies spot issues early, investigate faster, and actually close the loop before warranty costs and customer trust spiral. If you work anywhere near hardware, infrastructure, or complex systems, this is a sharp look at what “AI first” means when real products fail in the real world.

You will hear why quality is becoming a competitive weapon, how unstructured signals hide the truth, and what changes when AI agents start doing the detection, investigation, and coordination work humans have been drowning in.

What you will take away

Quality is not just a defect problem, it is a speed and trust problem, especially when product cycles keep compressing.

AI creates leverage by pulling together signals across the full product life cycle, not by sprinkling a chatbot on one system.

The fastest teams win by finding issues earlier, scoping impact correctly, and fixing what matters before customers notice the pattern.

A clear ROI often lives in warranty cost avoidance and downtime reduction, not just “efficiency” metrics.

“AI first” gets real when strategy becomes operational, and contradictions in how teams prioritize issues get exposed.

Timestamped highlights

00:00 Why manufacturing is a different kind of problem, and why speed is harder than it looks

01:10 What Axion does, and how it detects, investigates, and resolves customer impacting issues

05:10 The new reality, faster product cycles mean living in the bottom of the quality curve

10:05 Why it can take hundreds of days to truly solve an issue, and where the time disappears

16:20 How to evaluate AI vendors in manufacturing, specialization, integrations, and cross system workflows

22:40 The shift coming to quality teams, from reading data all day to making higher level decisions

28:10 What “AI first” looks like in practice, and how AI exposes misalignment across teams

A line worth repeating

“Humans are not that great at investigating tens of millions of unstructured data points, but AI can detect, scope, root cause, and confirm the fix.”

Pro tips you can apply

When evaluating an AI solution, ask three questions up front: how specialized the AI must be, whether you need a full workflow solution or just an API, and whether the use case spans multiple systems and teams.

Treat early detection as a first class objective, the longer the accumulation phase, the more cost and customer damage you silently absorb.

Align issue prioritization to strategy, not just frequency, cost, or the loudest internal voice.

Follow:

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Synthetic data is moving from a niche concept to a practical tool for shipping AI in the real world. In this episode, Amit Shivpuja, Director of Data Product and AI Enablement at Walmart, breaks down where synthetic data actually helps, where it can quietly hurt you, and how to think about it like a data leader, not a demo builder.

We dig into what blocks AI from reaching production, how regulated industries end up with an unfair advantage, and the simple test that tells you whether synthetic data belongs anywhere near a decision making system.

Key Takeaways

• AI success still lives or dies on data quality, trust, and traceability, not model hype.

• Synthetic data is best for exploration, stress testing, and prototyping, but it should not be the backbone of high stakes decisions.

• If you cannot explain how an output was produced, synthetic only pipelines become a risk multiplier fast.

• Regulated industries often move faster with AI because their data standards, definitions, and documentation are already disciplined.

• The smartest teams plan data early in the product requirements phase, including whether they need synthetic data, third party data, or better metadata.

Timestamped Highlights

00:01 The real blockers to getting AI into production, data, culture, and unrealistic scale assumptions

03:40 The satellite launch pad analogy, why data is the enabling infrastructure for every serious AI effort

07:52 Regulated vs unregulated industries, why structure and standards can become a hidden advantage

10:47 A clean definition of synthetic data, what it is, and what it is not

16:56 The “explainability” yardstick, when synthetic data is reasonable and when it is a red flag

19:57 When to think about data in stakeholder conversations, why data literacy matters before the build starts

A line worth sharing

“AI is like launching satellites. Data is the launch pad.”

Pro Tips for tech leaders shipping AI

• Start data discovery at the same time you write product requirements, not after the prototype works

• Use synthetic data early, then set milestones to shift weight toward real world data as you approach production

• Sanity check the solution, sometimes a report, an email, or a deterministic workflow beats an AI system

Call to Action

If this episode helped you think more clearly about data strategy and AI delivery, follow the show on Apple Podcasts and Spotify, and share it with a builder or leader who is trying to get AI out of pilot mode. You can also follow me on LinkedIn for more episodes and clips.

Tom Pethtel, VP of Engineering at Flock Safety, breaks down the real learning curve of moving from builder to manager, and how to keep your technical edge while scaling your impact through people.

You will hear how Tom’s path from rural Ohio to leading high stakes engineering teams shaped his approach to leadership, hiring, and staying close to the customer.

Key Takeaways

• ​ Promotions usually come from doing your current job well, plus stepping into the work above you that is not getting done
• ​ Great leaders do not fully detach from the craft, they stay close enough to the work to make good calls and keep context
• ​ Put yourself where the real learning is happening, watch customers, go to the failure point, get proximity to the source of truth
• ​ Hiring is not only pedigree, it is fundamentals plus grit, the willingness to solve what looks hard because it is “just software”
• ​ As you scale to teams of teams, your job becomes time allocation, jump on the biggest business fire while still making rounds everywhere

Timestamped Highlights

00:32 What Flock Safety actually builds, from AI enabled devices to Drone as a First Responder

02:04 Dropping out of Georgia Tech, switching disciplines, and choosing software for speed and impact

03:30 A life threatening detour, learning you owe 18,000 dollars, and teaching yourself to build an iPhone app to survive

06:33 Why Tom values grit and non traditional backgrounds in hiring, and the “it is just software” mindset

08:46 Proximity and learning, go to the problem, plus the lessons he borrows from Toyota Production System

09:55 A practical story of chasing expertise, from Kodak to Nokia, and hiring the right leader by going where the knowledge lives

14:27 The truth about becoming a manager, you rarely feel ready, you take the seat and learn fast

19:18 Leading teams of teams, you cannot be everywhere, so you go where the biggest fire is, without neglecting the rest

22:08 The promotion playbook, stop only doing your job, start solving the next job

A line worth stealing

“Do your job really well, plus go do the work above you that is not getting done, that’s how you rise.”

Pro Tips for engineers stepping into leadership

• ​ Stay technical enough to keep your judgment sharp, even if it is only five or ten percent of your week
• ​ If you want to grow, chase proximity, sit with the customer, sit with the failure, sit with the best people in the space
• ​ Measure your impact as leverage, if a team of ten is producing ten times, your role is not less valuable, it is multiplied
• ​ When you lead multiple disciplines, rotate your attention intentionally, do not camp on one fire for a full year

Call to Action

If this episode helped you rethink leadership, share it with one builder who is about to step into management. Subscribe on Apple Podcasts, Spotify, and YouTube, and follow Amir on LinkedIn for more conversations with operators building real teams in the real world.

Phil Freo, VP of Product and Engineering at Close, has lived the rare arc from founding engineer to executive leader. In this conversation, he breaks down why he stayed nearly 12 years, and what it takes to build a team that people actually want to grow with.

We get into retention that is earned, not hoped for, the culture choices that compound over time, and the practical systems that make remote work and knowledge sharing hold up at scale.

Key takeaways

• Staying for a decade is not about loyalty, it is about the job evolving and your scope evolving with it

• Strong retention is often a downstream effect of clear values, internal growth opportunities, and leaders who trust people to level up

• Remote can work long term when you design for it, hire for communication, and invest in real relationship building

• Documentation is not optional in remote, and short lived chat history can force healthier knowledge capture

• Bootstrapped, customer funded growth can create stability and control that makes teams feel safer during chaotic markets

Timestamped highlights

00:02:13 The founders, the pivots, and why Phil joined before Close was even Close

00:06:17 Why he stayed so long, the role keeps changing, and the work gets more interesting as the team grows

00:10:54 “Build a house you want to live in”, how valuing tenure shapes culture, code quality, and decision making

00:14:14 Remote as a retention advantage, moving life forward without leaving the company behind

00:20:23 Over documenting on purpose, plus the Slack retention window that forces real knowledge capture

00:22:48 Bootstrapped versus VC backed, why steady growth can be a competitive advantage when markets tighten

00:28:18 The career accelerant most people underuse, initiative, and championing ideas before you are asked

One line worth stealing

“Inertia is really powerful. One person championing an idea can really make a difference.”

Practical ideas you can apply

• If you want growth where you are, do not wait for permission, propose the problem, the plan, and the first step

• If you lead a team, create parallel growth paths, management is not the only promotion ladder

• If you are remote, hire for writing, decision clarity, and follow through, not just technical depth

• If Slack is your company memory, it is not memory, move durable knowledge into docs, issues, and specs

Stay connected:

If this episode sparked an idea, follow or subscribe so you do not miss the next one. And if you want more conversations on building durable product and engineering teams, check out my LinkedIn and newsletter.

Pete Hunt, CEO of Dagster Labs, joins Amir Bormand to break down why modern data teams are moving past task based orchestration, and what it really takes to run reliable pipelines at scale. If you have ever wrestled with Apache Airflow pain, multi team deployments, or unclear data lineage, this conversation will give you a clearer mental model and a practical way to think about the next generation of data infrastructure.

Key Takeaways

• Data orchestration is not just scheduling, it is the control layer that keeps data assets reliable, observable, and usable

• Asset based thinking makes debugging easier because the system maps code directly to the data artifacts your business depends on

• Multi team data platforms need isolation by default, without it, shared dependencies and shared failures become a tax on every team

• Good software engineering practices reduce data chaos, and the tools can get simpler over time as best practices harden

• Open source makes sense for core infrastructure, with commercial layers reserved for features larger teams actually need

Timestamped Highlights

00:00:50 What Dagster is, and why orchestration matters for every data driven team

00:04:18 The origin story, why critical institutions still cannot answer basic questions about their data

00:07:02 The architectural shift, moving from task based workflows to asset based pipelines

00:08:25 The multi tenancy problem, why shared environments break down across teams, and what to do instead

00:11:21 The path out of complexity, why software engineering best practices are the unlock for data teams

00:17:53 Open source as a strategy, what belongs in the open core, and what belongs in the paid layer

A Line Worth Repeating

Data orchestration is infrastructure, and most teams want their core infrastructure to be open source.

Pro Tips for Data and Platform Teams

• If debugging feels impossible, you may be modeling your system around tasks instead of the data assets the business actually consumes

• If multiple teams share one codebase, isolate dependencies and runtime early, shared Python environments become a silent reliability risk

• Reduce cognitive load by tightening concepts, fewer new nouns usually means a smoother developer experience

Call to Action

If this episode helped you rethink data orchestration, follow the show on Apple Podcasts and Spotify, and subscribe so you do not miss future conversations on data, AI, and the infrastructure choices that shape real outcomes.

Sandesh Patnam, Managing Partner at Premji Invest, breaks down how long duration capital changes the way you evaluate companies, founders, and moats. We talk about what most growth investors miss, why product strength still matters, and how to separate real AI businesses from thin wrappers in a noisy market.

Premji Invest is a captive, evergreen fund built to grow an endowment that supports major education work, which gives the team flexibility on time horizon and partnership style. Sandesh shares how that shows up in diligence, how they think about backing contrarian founders, and why the best companies in this AI era may still be ahead of us.

Key Takeaways

Focus on the long arc, not quarter by quarter optics, founders make better decisions when they are not trapped in short term metrics

In growth investing, TAM models and KPI spreadsheets can distract from the core question, does the product have real strength and an expanding roadmap

Enduring outcomes often come from backing a contrarian view early, then helping it move from contrarian to consensus over time

Evergreen capital changes behavior, you can slow down, build relationships, and partner across private and public markets instead of treating IPO as the finish line

In AI, separate the stack into data center, foundation models, and applications, then look for defensibility like vertical depth, data moats, and compounding usage value

Timestamped highlights

00:38 Premji Invest explained, evergreen structure, one LP, and why public markets can be part of the journey, not the exit

04:47 Two common growth investor lenses and what gets missed when product and roadmap do not lead the thesis

08:48 Partnership mindset, building trust, and being the first call when things get hard

12:48 The contrarian to consensus path, what creates alpha, and how to support founders through the lonely middle

19:54 Why rushing decisions is a trap, and how flexibility changes when and how you can partner with a company

20:55 AI investing framework, three layers, what looks frothy, what can endure, and where moats still exist

26:48 The cost of intelligence is collapsing, why this may still be the early internet moment, and what that implies for the next wave

A line that stuck with me

“We want to be the first port of call when the seas are turbulent.”

Practical moves you can steal

Pressure test the roadmap, ask when product two ships, what adjacency comes next, and what tradeoffs change at scale

When evaluating AI apps, demand a defensibility story beyond the model, look for proprietary data, vertical workflow depth, and value that improves with usage

Treat speed as a risk factor, if you cannot complete your churn cycle of doubt and validation, step back rather than force certainty

Call to Action

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Software engineering is changing fast, but not in the way most hot takes claim. Robert Brennan, Co founder and CEO at OpenHands, breaks down what happens when you outsource the typing to the LLM and let software agents handle the repetitive grind, without giving up the judgment that keeps a codebase healthy. This is a practical conversation about agentic development, the real productivity gains teams are seeing, and which skills will matter most as the SDLC keeps evolving.

Key Takeaways

AI in the IDE is now table stakes for most engineers, the bigger jump is learning when to delegate work to an agent

The best early wins are the unglamorous tasks, fixing tests, resolving merge conflicts, dependency updates, and other maintenance work that burns time and attention

Bigger output creates new bottlenecks, QA and code review can become the limiting factor if your workflow does not adapt

Senior engineering judgment becomes more valuable, good architecture and clean abstractions make it easier to delegate safely and avoid turning the codebase into a mess

The most durable human edge is empathy, for users, for teammates, and for your future self maintaining the system

Timestamped Highlights

00:40 What OpenHands actually is, a development agent that writes code, runs it, debugs, and iterates toward completion

02:38 The adoption curve, why most teams start with IDE help, and what “agent engineers” do differently to get outsized gains

06:00 If an engineer becomes 10x faster, where does the time go, more creative problem solving, less toil

15:01 A real example of the SDLC shifting, a designer shipping working prototypes and even small UI changes directly

16:51 The messy middle, why many teams see only moderate gains until they redraw the lines between signal and noise

20:42 Skills that last, empathy, critical thinking, and designing systems other people can understand

22:35 Why this is still early, even if models stopped improving today, most orgs have not learned how to use them well yet

A line worth sharing

“The durable competitive advantage that humans have over AI is empathy.”

Pro Tips for Tech Teams

Start by delegating low creativity tasks, CI failures, dependency bumps, and coverage improvements are great training wheels

Define “safe zones” for non engineers contributing, like UI tweaks, while keeping application logic behind clearer guardrails

Invest in abstractions and conventions, you want a codebase an agent can work with, and a human can trust

Track where throughput stalls, if PR review and QA are the bottleneck, productivity gains will not show up where you expect

Call to Action

If you got value from this one, follow the show and share it with an engineer or product leader who is sorting out what “agentic development” actually means in practice.

Deborah Hanus, Co-founder and CEO at Sparrow, joins Amir to unpack the founder journey from academia to building a scaled company. They dig into why leave management is still a messy, high stakes problem, and how Sparrow is turning it into a clean, guided experience for both HR and employees.

Sparrow helps companies provide employee leave across the United States and Canada, and Deborah shares what it really takes to scale a compliance driven business without slowing down. From founder resilience and early stage emotional swings to hiring, onboarding, and culture design, this one is packed with lessons for operators and builders.

Key takeaways

• Academia can be real founder training, especially for building resilience and hearing “no” without losing your edge

• Early stage startups feel brutal because you have too few data points, it is easy to overreact to every win or setback

• Compliance and leave are fundamentally data problems, the right info to the right person at the right time changes everything

• Scaling leadership is mostly communication and alignment, five people and 250 people require totally different systems

• Culture does not stay stable by accident, values must drive hiring, training, rewards, and performance management

Timestamped highlights

00:37 What Sparrow does, and the 300 million dollars in payroll cost savings milestone

01:37 Why academia can prepare you for founding, and how customer pain beats outside skepticism

03:40 The leave compliance mess, and why state by state rules made the problem explode

08:25 The two real ways startups die, and why morale matters as much as cash

12:55 Leading at scale, onboarding, clarity, and the feedback questions that keep teams aligned

19:54 “Scale intentionally” as a culture principle for a company that cannot afford to break things

25:48 Keeping values stable while everything else evolves as the team grows

A line worth sharing

“Companies end when you run out of cash or you run out of morale.”

Pro tips you can steal

• Treat the employee journey like a product journey, from recruiting through promotions and hard moments

• Before a big change, collect questions early so the message lands where people actually are

• After a meeting, ask “What were the main points?” to see what people heard, then tighten your messaging

• Invest in onboarding and goal clarity to prevent teams from drifting into competing priorities

Call to action

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