The Tech Trek

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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AI is not just changing how engineers write code. It is changing who gets close enough to shape the work.

In this episode of The Tech Trek, Robert Stewart, CTO at Arbital Health, joins Amir to talk about how AI is bringing actuarial subject matter experts closer to product and engineering teams, especially in healthcare and risk based contracts. Robert shares how his team is pairing technically minded SMEs with software engineers, using AI tools in development, and rethinking technical hiring now that AI assisted coding is part of the job.

Practical Takeaways

• AI can reduce the distance between domain experts and engineering when the SMEs can clearly describe requirements, acceptance criteria, and edge cases.
• Pairing a subject matter expert with an experienced engineer can be more powerful than traditional pair programming because each person brings a different kind of judgment.
• Better written requirements matter more in an AI assisted workflow because tools can work directly from detailed tickets and context.
• Technical interviews may need to test how candidates use AI, not whether they can avoid it.
• Hiring teams need stronger signals around identity, environment fit, prompting skill, and how candidates respond to AI output.

Timestamped Highlights

00:00 Robert Stewart on Arbital Health, value based care, and the role of actuarial expertise in healthcare infrastructure.
03:06 Why actuarial knowledge is hard to transfer into engineering teams through normal handoffs.
04:40 How AI helps subject matter experts move closer to product and engineering work.
06:08 Why engineering fundamentals still matter, even when AI makes code easier to create.
09:55 How Arbital Health is using Cursor, Claude Code, and human review in a regulated environment.
14:52 Why more detailed Jira tickets are becoming more valuable in AI assisted development.
17:10 How AI is changing technical interviews from “you may use AI” to “you must use AI.”
22:16 What suspicious candidates, remote interviews, and fake profiles are forcing hiring teams to rethink.

One Line That Stuck

“You can judge an expert by the type of questions they ask.”

Pro Tips

• Ask candidates to share their screen during AI assisted technical interviews.
• Watch how they prompt, not just what they produce.
• Look for whether they catch strange or weak AI output.
• Use a rubric, but also evaluate whether the candidate fits the way your team actually works.
• For AI generated code, add stronger human review, especially in regulated environments.

Subscribe to The Tech Trek for more conversations on how technical teams are adapting around AI, data, product, platform, hiring, and engineering execution.