Eye On A.I.

Construction is one of the least digitized industries in the world, and not because it resists technology. It resists bad technology.
In this episode of Eye on AI, Craig Smith sits down with Olek Paraska, CTO of Togal AI, to break down why construction productivity has barely improved in 50 years and why pre-construction is the real bottleneck holding the industry back.
Olek explains how most estimating and takeoff work is still done manually, why automating this phase can unlock massive efficiency gains, and how AI works best in construction when it acts as a perception and reasoning layer rather than a replacement for human judgment.
The conversation explores computer vision, agentic AI, human-in-the-loop systems, and why respecting real-world constraints is essential for AI to deliver real ROI. It also looks ahead to a future where floor plans, materials, costs, and constructability can be reasoned about together, long before construction begins.
This episode is a deep dive into how AI can finally move construction forward by solving the right problems, in the right order.

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(00:00) Why Construction Is Desperate for Better AI
(01:06) Olek's Path From Software to Construction
(02:17) Why Construction Productivity Has Stalled for Decades
(04:33) The Pre-Construction Bottleneck No One Talks About
(06:17) How Takeoffs Are Still Done Manually
(09:15) Why Construction Rejects Bad Technology
(11:18) How Togal Found the Right Problem to Solve
(12:14) From Computer Vision to Reasoning AI
(17:44) What Agentic AI Looks Like in Pre-Construction
(20:59) Turning Floor Plans Into Materials and Costs
(28:18) The Real ROI of AI for Contractors
(47:11) The Long-Term Vision for AI in Construction

In this episode of the Eye on AI Podcast, Craig Smith sits down with Steve Brown, founder of CureWise, to explore how agentic AI is reshaping healthcare from the patient's perspective.
Steve shares the deeply personal story behind CureWise, born out of his own experience with a rare cancer diagnosis that was repeatedly missed by traditional medical pathways. The conversation dives into why modern healthcare struggles with complex, edge-case conditions, how fragmented medical data and time-constrained systems fail patients, and where AI can meaningfully help without replacing clinicians.
The discussion goes deep into multi-agent AI systems, reliability through consensus, large context windows, and how AI can surface better questions rather than premature answers. Steve explains why patient education is the real unlock for better outcomes, how precision medicine depends on individualized data and genetics, and why empowering patients leads to stronger collaboration with doctors.
This episode offers a grounded, practical look at AI's role in healthcare, not as a diagnostic shortcut, but as a tool for clarity, context, and better decision-making in some of the most critical moments of car
 
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(00:00) Using Multi-Agent AI to Analyze Medical Records
(04:35) Steve Brown's Tech Background and Return to Healthcare
(08:25) How a Rare Cancer Diagnosis Was Initially Missed
(13:55) Why Modern Medicine Struggles With Complex Cases
(18:29) Multi-Agent Consensus and AI Reliability in Healthcare
(24:12) Large Context Windows, RAG, and Medical Data Organization
(28:24) Why CureWise Focuses on Patient Education, Not Diagnosis
(33:10) Precision Medicine, Genetics, and Personalized Treatment
(47:45) Why CureWise Launches Direct-to-Patient First
(53:19) The Future of AI-Driven Precision Medicine

AI is getting smarter, but now it needs better  judgment.
In this episode of the Eye on AI Podcast, we speak with Robbie Goldfarb, former Meta product leader and co-founder of Forum AI, about why treating AI as a truth engine is one of the most dangerous assumptions in modern artificial intelligence.
Robbie brings first-hand experience from Meta's trust and safety and AI teams, where he worked on misinformation, elections, youth safety, and AI governance. He explains why large language models shouldn't be treated as arbiters of truth, why subjective domains like politics, health, and mental health pose serious risks, and why more data does not solve the alignment problem.
The conversation breaks down how AI systems are evaluated today, how engagement incentives create sycophantic and biased models, and why trust is becoming the biggest barrier to real AI adoption. Robbie also shares how Forum AI is building expert-driven AI evaluation systems that scale human judgment instead of crowd labels, and why transparency about who trains AI matters more than ever.
This episode explores AI safety, AI trust, model evaluation, expert judgment, mental health risks, misinformation, and the future of responsible AI deployment.
If you are building, deploying, regulating, or relying on AI systems, this conversation will fundamentally change how you think about intelligence, truth, and responsibility.

Want to know more about Forum AI?
Website: https://www.byforum.com/
X: https://x.com/TheForumAI
LinkedIn: https://www.linkedin.com/company/byforum/
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(00:00) Why Treating AI as a "Truth Engine" Is Dangerous
(02:47) What Forum AI Does and Why Expert Judgment Matters
(06:32) How Expert Thinking Is Extracted and Structured
(09:40) Bias, Training Data, and the Myth of Objectivity in AI
(14:04) Evaluating AI Through Consequences, Not Just Accuracy
(18:48) Who Decides "Ground Truth" in Subjective Domains
(24:27) How AI Models Are Actually Evaluated in Practice
(28:24) Why Quality of Experts Beats Scale in AI Evaluation
(36:33) Trust as the Biggest Bottleneck to AI Adoption
(45:01) What "Good Judgment" Means for AI Systems
(49:58) The Risks of Engagement-Driven AI Incentives
(54:51) Transparency, Accountability, and the Future of AI

In this episode of Eye on AI, Craig Smith sits down with Jarrod Johnson, Chief Customer Officer at TaskUs, to unpack how agentic AI is changing customer service from conversations to real action. 
 
They explore what agentic AI actually is, why chatbots were only the first step, and how enterprises are deploying AI systems that resolve issues, execute tasks, and work alongside human teams at scale. 
 
The conversation covers real-world use cases, the economics of AI-driven support, why many enterprise AI pilots fail, and how human roles evolve when AI takes on routine work. 
 
A grounded look at where customer experience, enterprise AI, and the future of support are heading.



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(00:00) Jarrod Johnson and the Evolution of TaskUs
(03:58) Why AI Became Core to Customer Service
(06:07) Humans, AI, and the New Support Model
(07:16) What Agentic AI Actually Is
(11:38) TaskUs as an AI Systems Integrator
(14:59) How Agentic AI Resolves Customer Issues
(19:52) Workforce Impact and the Human Role
(23:26) Why Most Enterprise AI Pilots Fail
(30:32) Real Client Case Study: Healthcare Impact
(36:34) Why Customer Service Still Feels Broken
(38:49) The End of IVR Menus and Legacy Systems
(42:25) AI Safety, Compliance, and Governance
(49:38) Training Humans for AI and RLHF Work
(54:34) The Future of Agentic AI in Enterprise

Inference is now the biggest challenge in enterprise AI.

In this episode of Eye on AI, Craig Smith speaks with Nick Pandher, VP of Product at Cirrascale, about why AI is shifting from model training to inference at scale. As AI moves into production, enterprises are prioritizing performance, latency, reliability, and cost efficiency over raw compute.

The conversation covers the rise of inference-first infrastructure, the limits of hyperscalers, the emergence of neoclouds, and how agentic AI is driving always-on inference workloads. Nick also explains how inference-optimized hardware and serverless AI platforms are shaping the future of enterprise AI deployment.
 
If you are deploying AI in production, this episode explains why inference is the real frontier.
 

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(00:00) Preview
(00:50) Introduction to Cirrascale and AI inference
(03:04) What makes Cirrascale a neocloud
(04:42) Why AI shifted from training to inference
(06:58) Private inference and enterprise security needs
(08:13) Hyperscalers vs neoclouds for AI workloads
(10:22) Performance metrics that matter in inference
(13:29) Hardware choices and inference accelerators
(20:04) Real enterprise AI use cases and automation
(23:59) Hybrid AI, regulated industries, and compliance
(26:43) Proof of value before AI pilots
(31:18) White-glove AI infrastructure vs self-serve cloud
(33:32) Qualcomm partnership and inference-first AI
(41:52) Edge-to-cloud inference and agentic workflows
(49:20) Why AI pilots fail and how enterprises succeed