The Tech Trek

Machine learning teams are moving faster, but the hard part has not disappeared. The work is shifting from writing and debugging every line of code toward defining the right problem, setting requirements, reviewing outputs, and deciding what belongs in a durable platform.

Niels Bantilan, Chief Machine Learning Engineer at Union AI, explains how machine learning work has changed, why coding agents are accelerating prototyping, and what engineers must consider when building infrastructure that supports many teams instead of optimizing one model. He also shares how customer needs become product decisions, why machine learning roles are becoming more specialized, and why measuring AI productivity remains difficult.

Key Takeaways

• Coding agents reduce time spent on implementation, debugging, and exploration, but engineers still need judgment around architecture, quality, and business value.
• Platform teams must balance experimentation with stability by giving users freedom at the edges while protecting a reliable foundation.
• Machine learning engineering now spans a wider range of skills, from low level performance work to customer empathy, education, documentation, and developer advocacy.
• The best model for a task may depend on complexity. Smaller self hosted models can handle tightly scoped changes, while longer and more complex work may still require stronger hosted tools.

Episode Highlights

00:50 What Union AI means by an AI runtime for production
02:10 How machine learning work has changed over the past five years
10:40 The mindset shift from model building to platform engineering
15:00 Turning customer problems into reusable product capabilities
19:00 Why machine learning roles are becoming more specialized
21:50 Using coding agents through specifications, tickets, and code review
26:50 Token costs, productivity measurement, and choosing the right model

One Line That Stuck

“I’m still solving problems. It’s just the level at which I’m doing it doesn’t require me to necessarily get into the weeds of the implementation.”

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Healthcare providers can wait 60 to 75 days to get paid, while many hospitals spend 5% to 7% of revenue on the collection process. That makes revenue cycle management more than a back office issue. It affects margins, staffing, patient experience, and access to care.

Akash Magoon, cofounder and CEO of Adonis, joins The Tech Trek to explain how agentic AI can help medical groups and hospitals automate denials, accounts receivable work, and other manual billing processes. He also shares how Adonis applies AI internally across engineering, sales, and customer success.

The conversation goes beyond automation. Akash explains why healthcare companies often win through distribution, not product quality alone, why focused solutions can create more progress than broad attempts to fix healthcare at once, and why leaders need to frame AI as a tool that helps people work at the top of their license.

Practical Takeaways

• Start with a narrow, material problem rather than trying to rebuild healthcare all at once.
• Measure AI through business outcomes, including net collection rate and cost to collect.
• Invest in marketing and distribution early, even when the product is strong.
• Build employee trust by showing how AI improves effectiveness, not only efficiency.

Approximate Highlights

00:45 How Adonis applies agentic AI to revenue cycle management
02:05 Lessons from building a second healthcare technology company
04:45 Using AI for customers and inside the company
08:55 Why healthcare progress often starts with focused swim lanes
14:25 The distribution lesson Akash carried into Adonis
20:40 How operational efficiency may improve patient access and rural healthcare

One Line That Stuck

“Healthcare ends up becoming a very humbling place to build.”

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Most companies are not short on data. They are short on the time, cost, and coordination required to turn it into action.

Ethan Ding, co founder and CEO of TextQL, joins The Tech Trek to explain how AI agents are changing enterprise analytics. The conversation moves beyond faster dashboards into a larger shift, analysts managing fleets of agents, business teams asking far more questions, and companies finding revenue and cost opportunities that were previously too expensive to pursue.

What Technical Teams Can Take From This

• Making answers cheaper does not reduce analytics work. It increases the number of questions people ask.
• Analysts may spend less time assembling dashboards and more time managing agents, data sources, permissions, quality, and costs.
• The clearest ROI comes from decisions with direct financial outcomes, including fraud prevention, upsell opportunities, churn risk, and unused vendor spend.
• Faster analysis matters most when teams can act on valuable opportunities they previously could not afford to investigate.
• Token costs will force AI companies and buyers to reconsider where software budgets go, especially across BI tools and data platforms.

Moments Worth Hearing

00:00 Ethan explains how TextQL agents work across messy enterprise systems including Cognos, Teradata, Snowflake, Databricks, Tableau, and Power BI.
04:52 Why giving people faster answers does not create free time. It creates even more demand for analytics
07:10 How self service analytics quickly moves from asking what a number is to asking whether it matters and what to do next.
10:08 The analyst role shifts toward managing fleets of agents and tuning an insight factory for the business.
14:38 Why faster access to data can reveal valuable opportunities that were previously too expensive to investigate.
19:55 A practical way to measure analytics ROI through fraud prevention, upsell opportunities, and other direct financial outcomes.
24:18 How token costs, AI margins, and easier migrations could reshape spending on traditional BI tools.

One Line That Stuck

“It becomes much more of an operations manager job. It is a factory. It takes in tokens and churns out dashboards, reports, and recommendations.”

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Enterprise AI is easy to demonstrate. The real test begins when a promising POC meets production costs, security requirements, data movement, latency, and internal adoption.

Shimon Ben-David, CTO at WEKA, joins Amir to discuss the gap between experimenting with generative AI and operating it at scale. They explore how classical AI differs from generative AI, why production exposes problems that demos hide, and how companies with limited AI maturity can start building useful internal capability.

Practical Takeaways

• A successful POC proves that an outcome is possible. It does not prove that the system will be affordable, secure, reliable, or fast at scale.
• Enterprise AI adoption reaches across infrastructure, engineering, data, security, and business teams. It cannot be owned by one group in isolation.
• Adding more GPUs will not fix slow data access, poor utilization, weak pipelines, or an experience users do not want to use.
• External support can help, but the person or firm involved needs to stay through implementation and production, not stop at recommendations.
• Companies that are behind should begin with proven use cases, build internal experience, and quickly stop experiments that fail to show value.

Key Moments

00:00 Why moving enterprise AI into production remains difficult
01:55 The difference between classical AI and generative AI adoption
07:05 How companies can use AI without having a formal AI strategy
11:35 Why successful POCs often struggle when they reach production
17:35 Competitive pressure, AI FOMO, and the need to calculate real ROI
22:00 Why AI adoption requires cross organizational change
33:10 Where a company with limited AI maturity should begin

One Line That Stuck

“The promise is there. It is possible. You just need to do it properly.”

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AI can generate code faster, but that does not make software delivery simple. It shifts the pressure to requirements, architecture, review, and technical judgment.

Goncalo Silva, CTO at Doist, explains how AI is changing the way teams behind Todoist and Twist build software. He shares why greater individual autonomy has led to more collaboration, why deep expertise still matters, and how faster execution is reshaping product delivery, project planning, and engineering hiring.

What Leaders Can Take From This

• Faster code generation makes strong planning and clear requirements more important, not less important.
• Designers, product leaders, and engineers can work from richer prototypes, but production systems still need experienced technical judgment.
• Engineering capacity does not have to move into other functions. Teams can use it to improve reliability, performance, quality, and the amount of valuable work they ship.
• Token counts are a weak measure of progress. Doist looks at team feedback and whether projects are staying on track.
• Engineering interviews need to test architecture, decision making, curiosity, and depth, not simply whether a candidate can produce working code.

Approximate Highlights

00:00 Meet GonCalo Silva and the products behind Doist
02:00 How broadly AI is being used across Doist
04:15 Why greater autonomy has brought teams closer together
09:45 Where nontechnical coding works, and where it creates risk
17:50 How AI compressed a major refactoring effort by 20 to 30 times
25:05 Measuring AI value without counting tokens
30:20 Why faster execution requires more up front planning
34:50 How Doist changed its engineering interview process

One Line That Stuck

“We are the bottleneck. Our attention span, our ability to memorize, our ability to understand, and deep expertise.”

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