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- Today25 min
AI Is Changing How Engineers Actually Work
AI coding tools are not just changing how software gets written. They are changing how teams work, how engineers are evaluated, and where bottlenecks show up.Scott Breitenother, CEO and cofounder of Kilo, joins The Tech Trek to talk about what engineering looks like when developers are managing multiple agents, work continues overnight, and the real constraint is no longer typing code, but judgment, ownership, and process design.Scott shares how Kilo uses Kilo to build its own product, why AI only creates speed when companies rethink their workflows, and how teams can build trust in agent generated code without creating a new layer of busywork.Practical Takeaways• AI does not automatically make teams faster. If approvals, meetings, and handoffs stay the same, the bottlenecks simply move.• Engineers using coding agents still own the outcome. AI can assist with the work, but accountability for quality does not disappear.• The strongest teams will find a middle ground between blindly accepting AI output and reviewing every line as if nothing changed.• Agentic engineering may feel novel now, but Scott believes it will eventually just be called engineering.• Always on agents are already useful for monitoring, triage, and preparing recommended fixes, even if full autonomy is still selective.Episode Highlights00:38 Scott explains what Kilo is building across AI coding, open source infrastructure, and always on agents.01:16 How Kilo uses its own tools internally, and why developers are shifting from working with one agent to managing many at once.05:34 Why companies often fail to see AI speed gains when they layer new tools onto old processes.08:51 The trust curve with coding agents, from early experimentation to accountability, review, and better judgment.12:39 Why Scott sees agentic coding as a transition phase, not a permanent category.15:32 Two habits he thinks matter most right now, staying curious and trying a wide range of models and tools.18:03 What always on agents can already do today, and how that could expand over the next year.One Line That Stuck“Bringing in AI does not remove accountability from whoever creates the PR.”Pro Tips• Start small with AI assisted workflows, then expand into single agents, multiple agents, and automated review as trust grows.• Match review depth to risk. A mission critical system deserves more scrutiny than a simple cosmetic change.• Use automated review to guide human reviewers toward the areas that deserve the most attention.• Keep experimenting. A tool that fails on Monday may be materially better by Wednesday.Stay ConnectedSubscribe to The Tech Trek for more conversations on how modern technical teams are building, operating, and adapting around AI, data, platform, product, and engineering execution.
- 3 days ago24 min
AI Can Handle the Tax Code. What Still Needs a Human?
Tax is one of the hardest places to earn trust with AI. The work is complex, the stakes are personal, and being mostly right is not good enough.In this episode of The Tech Trek, David Kang, founder and CEO of Keeper, explains how his team is applying AI to tax workflows without pretending humans disappear from the process. He breaks down why tax is such a strong fit for language models, where AI can reduce manual review, how Keeper decides when a case needs human escalation, and why the best products may feel less like autonomous agents and more like systems that make experts sharper.Key Takeaways• AI is most valuable when it removes repetitive work while preserving human judgment where risk is highest.• High trust products need clear escalation logic, especially when edge cases drive most of the anxiety.• Tax is a strong fit for AI because much of the work involves language, rules, validation, and workflow routing.• The smartest AI adoption often starts with bounded operational tasks before moving into more domain specific decisions.• Consumer trust in AI can change quickly, but messaging still matters when the product sits inside sensitive workflows.Highlights00:34 Where Keeper fits for people who have outgrown DIY tax software but do not need a traditional personal accountant.02:27 Why tax may be one of the more practical use cases for AI, even in a high stakes environment.07:15 The accounting talent shortage, what automation may replace, and how roles could shift.10:55 How Keeper uses AI before professional review to flag possible issues and optimization opportunities.13:51 Why the company moved from keeping AI in the background to talking about it more directly.17:58 How Keeper separates the routine parts of a tax return from the parts that need expert attention.21:05 The path from simple customer support automation to more advanced tax focused AI workflows.One Line That Stuck“Across tens of thousands of returns and clients, you can kind of get to the point where you err on the side of safety.”Follow The Tech Trek for more conversations with founders, operators, and technical leaders building through the next wave of AI, data, and engineering change.
- 5 days ago29 min
He Built a Public Company. Now He Is Starting Over
What happens after you build a public company, spend nearly three decades at the helm, and then find yourself starting over?Rob Locascio, CEO and founder of Uare.ai, joins The Tech Trek to talk about that exact journey. Rob previously founded LivePerson, helped create web chat for customer service, took the company public, and later scaled it into a major conversational AI business. Now he is back in founder mode, building a new company around individual AI, personal knowledge, and human control over data.This conversation gets into what it takes to return to zero, why strong ideas need more than belief, how Uare.ai evolved from a personal loss into a broader AI platform, and why Rob sees the current AI moment as bigger and more complex than the dot com era.Practical Takeaways• Ideas are not the asset. The ability to turn them into something people understand, join, and buy is what matters.• Starting over after success requires shedding the habits of scale and getting back into a true startup mindset.• The first version of a company may only be an entry point. The deeper opportunity often reveals itself through real users.• Rob believes the future of AI should include individual systems built from a person’s own knowledge, voice, and data, not only large aggregated models.• The current AI wave has stronger infrastructure than the dot com era, but also more pressure from incumbents and government involvement.Timestamped Highlights00:33 Rob explains Uare.ai and its approach to building AI around individual human knowledge.01:17 The LivePerson story, from inventing web chat to building a large conversational AI company.03:13 What it felt like to leave the company he spent 28 years building and become a founder again.06:04 The personal and family tradeoffs of starting another company later in life.09:06 Why Rob compares building a company to writing a song, and what it means to manifest an idea.15:52 How the original idea for Uare.ai came from wanting to preserve his father’s voice and memory.24:00 Rob compares the dot com boom with the current AI cycle, including where he sees real differences.One Line That Stuck“They may be able to take your company, but they can’t take your ideas and they can’t take you.”Practical Founder Advice• Find the smallest real entry point for the idea and get moving.• Do not let criticism kill something before the market has a chance to respond.• Pay close attention to who shows up early. The wrong people can distort a young company quickly.• Expect the company to evolve. Staying loyal to the original insight does not mean staying frozen in the original product.Subscribe or follow The Tech Trek for more conversations with founders, technical leaders, and operators building through major shifts in AI, data, product, and engineering.
- 1 weeks ago23 min
Why Fintech Products Get Stuck Before Launch
Snigdha Kumar, CEO and co founder at Bricco, joins The Tech Trek to talk about a part of fintech most people never see, state by state licensing.For any financial company trying to launch in the United States, licensing can be slow, expensive, and operationally painful. Snigdha explains why that barrier limits experimentation, how Bricco is trying to automate the process, and why better compliance infrastructure could help more useful financial products reach the market.Practical takeaways• Financial innovation is not only a product problem. Licensing, compliance, reporting, audits, and exams can shape what gets built before a product ever reaches customers.• Lowering the cost of licensing does not remove regulation. It makes the process more efficient while keeping important protections in place.• The biggest barrier for fintech founders is often not knowing what path is available. Education and clearer process design can keep teams from avoiding licensing or choosing expensive workarounds.• Better financial products still need better distribution and awareness. Easy access is not the same as helping people find the right product for their actual financial life.• Responsible financial behavior may need better product design, better incentives, and a stronger cultural signal, not just more advice.Timestamped highlights00:43, Snigdha explains how Bricco is automating state by state regulatory compliance for financial licensing.02:15, How her career has focused on reducing barriers to financial services across Asia, Africa, and the United States.05:05, The reverse culture shock of finding major access gaps inside the US financial system.06:08, Why licensing costs can run into the millions and shrink the number of fintech experiments.09:58, Why reducing the barrier matters, but eliminating it completely would create real risk.12:21, The difference between making financial products easy and making sure people are using the right product.16:05, Why spending has a social identity, but saving and responsible investing often do not.21:10, How Bricco uses education and content to help founders treat licensing as a strength instead of a blocker.One Line That Stuck“Think about licensing as a strength, think about it as a way to own your destiny.”Practical TakeawaysFor fintech founders and operators, the message is simple. Do not treat licensing as a late stage legal detail. It can affect product timelines, market access, capital needs, and the type of company you are able to build.For technical and product leaders, this is a reminder that infrastructure is not always code. Sometimes the biggest product constraint is the operating system around the business.Subscribe or follow The Tech Trek for more conversations with founders, builders, and operators working through the real decisions behind modern technical companies.
- 1 weeks ago27 min
Coding Isn’t the Hard Part Anymore
Adam Kirk, CTO and cofounder of Jump, joins The Tech Trek to talk about what it really takes to build AI native products for people who do not want to think like technologists.Jump serves financial advisors, a market where ease of use, trust, workflow fit, and domain context matter as much as the model itself. Adam shares how his team validates product ideas, uses coding agents across engineering, and is rethinking how technical teams build, review, and hire in the AI era.What You’ll Take Away• AI native products still win or lose on adoption. If the user feels like they are programming, the product is already too complicated.• The engineering bottleneck is moving. AI can generate code faster, but teams still need humans to review, validate, and understand the tradeoffs.• Product teams can now get closer to the build. PMs using AI to prototype create sharper product definition, even when engineers still rebuild the final version properly.• Technical debt is not disappearing. Code may be cheaper to write, but data models, migrations, architecture, and judgment still carry real risk.• Engineering interviews are breaking. If engineers use AI every day, hiring teams need better ways to assess ownership, judgment, and technical taste.Timestamped Highlights00:38Adam explains how Jump helps financial advisors turn client meetings into notes, CRM updates, and advisor specific workflows02:20Why less technical users force better product validation, and why a flexible interface can still feel like programming.07:00How Jump uses coding agents across the engineering team, and why code review matters more as AI generated code improves.11:15Why PMs vibe coding product ideas can help engineers understand what needs to be built.14:08Where AI is creating real productivity gains, and where human coordination still slows things down.18:00Why some technical debt may get easier to manage, but data modeling and migrations remain hard.20:51How AI is forcing engineering leaders to rethink coding interviews, referrals, and what great engineers should be measured on.One Line That Stuck“Generating code is really not the bottleneck anymore. It is validating the code, reviewing the code, and sharing the context around to the team.”Practical Takeaways• Test product ideas with real users before engineering builds too far.• Treat AI prototypes as product definition, not production architecture.• Use coding agents to speed up the work, but do not skip review.• Assess engineers for judgment, ownership, and decision quality, not just raw syntax.Follow The ShowSubscribe to The Tech Trek for more conversations with technical leaders building the next generation of AI native products, teams, and workflows.
- 1 weeks ago29 min
How AI Is Changing the Way Engineering Teams Work
Krishna Sai, CTO at SolarWinds, joins The Tech Trek to talk about one of the biggest shifts happening inside IT and engineering teams: AI is moving people from operators to orchestrators.The conversation goes beyond faster code and automation. Krishna explains why AI is changing how teams think about systems, governance, validation, observability, and the skills technical leaders will need as work moves from manual execution to higher level oversight.Key Takeaways• AI is raising the level of abstraction for IT and engineering teams. The work is shifting from operating systems manually to designing systems that can increasingly run, adapt, and respond on their own.• AI does not automatically reduce workload. In many teams, it changes the type of work by moving effort from execution into validation, judgment, risk management, and governance.• Code generation is only one part of the delivery system. Without testing, security review, observability, and strong engineering process, faster code can create more problems faster.• The best AI outcomes depend on strong foundations. Clean data, connected systems, clear ownership, and resilient architecture matter more as AI becomes part of core workflows.• Technical professionals will need stronger systems thinking, business context, adaptability, and domain understanding as AI changes the shape of day to day work.Timestamped Highlights00:00Krishna Sai joins the show and sets the stage for a conversation about AI, IT responsibility, skill gaps, and the latest SolarWinds IT Trends Report.02:14Why IT is moving from operator to orchestrator, and what that means for teams that used to spend most of their time responding to tickets and manually managing systems.04:54Krishna explains why AI feels different from prior technology shifts. This is not just infrastructure change. It touches individual workflows, jobs, and decision making.08:56The messy middle of AI adoption. Teams are getting faster at some tasks, but the workload has not disappeared. It has moved into validation, review, and oversight.14:46How AI may force teams to rethink the software delivery cycle, sprint structure, feedback loops, and the speed at which customer issues can be resolved24:27Krishna shares how principles from distributed systems, including loose coupling and high cohesion, can help leaders build AI systems that can change without breaking everything around them.Standout Moment“AI is a multiplier. It does not magically fix all your problems. It multiplies your current state.”Pro Tips• Do not measure AI success only by how much faster a team can generate code or complete a task.• Look at the full system around the work, including testing, review, security, observability, and ownership.• Build AI workflows with enough flexibility to swap tools, models, and processes as the technology changes.• Invest in systems thinking and domain knowledge. Those skills become more valuable as execution becomes easier to automate.Call to ActionSubscribe to The Tech Trek for more conversations with technology leaders on how AI, data, engineering, and modern systems are changing the way companies build.
- 2 weeks ago37 min
Why AI Still Needs Human Judgment
Dan Wald, cofounder and chief AI officer at Sciemo, joins The Tech Trek for a sharp conversation about what AI can and cannot do inside real business workflows.The big question: can AI move beyond quick answers and actually support the messy, context heavy work that still lives in Excel, data teams, and functional expertise?Dan breaks down why consumer style AI has trained people to expect instant answers, why that creates risk inside companies, and why the next wave of AI products needs more than a chat box. It needs context, transparency, guardrails, and humans who understand the work well enough to challenge the output.The conversation also gets into AI agents, coding, entry level talent, narrow workflow specific AI, and why replacing judgment is a much harder problem than replacing repetitive tasks.Key takeaways• AI tools are only useful when they understand the context behind the question, not just the wording of the prompt.• Excel remains powerful because users can see the data, change assumptions, and understand the logic. AI products need to earn that same level of trust.• The best AI workflows are not black boxes. They let users inspect assumptions, challenge outputs, and adjust the answer.• Agents can speed up work, but they still need human judgment, especially when the task requires strategy, constraints, or domain expertise.• AI may change entry level work, but companies still need people who can think critically, solve new problems, and understand why the output is right or wrong.Timestamped highlights00:40 Dan explains how Sciemo helps consumer brands unify messy data and apply AI to inventory, pricing, assortment, and promotion decisions.02:30 Why the single prompt experience has changed what people expect from AI, and why that expectation can break down inside the workplace.04:19 How purpose built AI differs from general AI, especially when the workflow requires context, guardrails, and a clear goal.07:41 Why Excel is still hard to replace, and what AI systems need to learn from the control and transparency users already expect.12:57 Dan compares AI agents to unlimited interns, useful for many tasks, but still limited without expert direction.21:57 The slap chop analogy, and why faster tools do not automatically make someone better at the underlying craft.31:15 Why predictions about technology and work are so hard to get right, even when productivity clearly improves.A line that stuck“Used properly, they’re great. Used poorly, it’s a very new technology. There will be more mistakes than there are winners.”Practical points worth taking• Do not treat a confident AI answer as a complete answer.• Build AI around real workflows, not generic prompts.• Keep humans close to the assumptions, especially when the decision has business impact.• Use AI to move faster, but make sure someone still understands the logic behind the work.Listen nextFollow The Tech Trek for more conversations with founders, operators, and technical leaders building through the next wave of AI, data, and product change.
- 2 weeks ago22 min
Why AI Will Not Fix Broken Data Teams
Most data teams do not have an AI problem yet. They have an operating model problem.Mike Doll, VP of Data at Guitar Center, joins The Tech Trek to talk about why analytics teams often become reactive ticket factories, and what it takes to turn data into a true business partnership.As companies push harder into AI, automation, and faster decision making, the foundation matters more than ever. If the data team is buried in scattered requests, unclear priorities, and dashboard maintenance, AI will not magically fix the problem. It may only expose it faster.Mike shares how modern data teams can rethink intake, structure analytics partnerships, separate quick BI needs from deeper analytical work, and create a more consultative model that helps the business answer harder questions.Key Takeaways• AI will not fix a broken data operating model. Teams still need clear intake, trusted data, business context, and a better way to prioritize work.• Data teams become ticket factories when every request is treated the same and stakeholders do not understand what happens after they ask for help.• BI and analytics serve different needs. Quick reporting should be fast and reliable, while deeper analytics requires judgment, framing, and business partnership.• Self service only works when the data foundation is strong. Without that foundation, it can create more confusion instead of more speed.• The future of analytics is not just faster answers. It is better questions, stronger context, and data teams that understand how the business actually operates.Timestamped Highlights00:41 Mike explains his role leading Guitar Center’s central data organization, including data engineering, analytics, BI, data science, and data strategy.02:09 How data teams become ticket factories, and why unstructured requests can turn analytics into a black box for the business.05:29 Why analytics delivery is different from software delivery, and why data teams need closer alignment with business leaders.07:28 Where self service helps, where it breaks down, and why simple questions need a different model than complex business problems.09:47 Mike explains the consulting model for analytics teams, with dedicated business partners, stronger dialogue, and shared value creation.15:35 How AI is changing quick BI workflows, and why harder analytics questions still require human judgment and problem framing.18:00 How Mike started shifting Guitar Center away from reactive ticket taking by improving intake, visibility, communication, and trust.Line Worth Remembering“The value that analytics teams can bring is answering those hard questions.”Practical MovesFor data leaders trying to move beyond reactive analytics, Mike’s advice is to start with the biggest points of friction.That might mean creating a clearer intake process, giving stakeholders visibility into work, assigning dedicated analytics partners to key business areas, or rebuilding trust through fast but meaningful wins.The point is not to add process for the sake of process. The point is to create a data function that can move quickly without losing context, accountability, or connection to business value.Stay ConnectedFollow The Tech Trek for more conversations with technology leaders on data, AI, engineering, platforms, and the operating models behind modern technical teams.
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About The Tech Trek
The Tech Trek is a podcast for founders, builders, and operators who are in the arena building world class tech companies. Host Amir Bormand sits down with the people responsible for product, engineering, data, and growth and digs into how they ship, who they hire, and what they do when things break. If you want a clear view into how modern startups really get built, from first line of code to traction and scale, this show takes you inside the work.
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