Built This Week

Clinical trials are one of the slowest and most expensive processes in modern medicine.It can take 10–15 years and up to $3 billion to bring a new drug to market — and many trials fail simply because they can’t enroll enough patients.In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Dr. Chadi Nabhan, Chief Medical Officer at RyghtAI, to explore how AI-powered digital twins of clinical trial sites can dramatically improve the speed and success of clinical trials.RyghtAI has built a platform that creates digital twins of thousands of clinical trial sites worldwide, allowing pharmaceutical companies to instantly identify the best locations and investigators for any given trial.Instead of relying on manual site selection or reputation-based decisions, AI analyzes historical trial performance, patient demographics, biomarker capabilities, and infrastructure to determine which sites are most likely to enroll patients successfully.The result: faster trials, better patient representation, and potentially life-saving therapies reaching the market sooner.In this episode we discuss:• Why 80% of clinical trials fall behind schedule • Why half of clinical trial sites enroll 0–1 patients • How AI parses 200-page trial protocols in seconds • The role of digital twins in predicting trial success • How AI improves patient diversity in clinical trials • Why biomarker data is becoming essential in modern medicine • How AI agents infer site capabilities from historical trial data • Why informed patients using AI tools may actually improve healthcare outcomesIf AI can dramatically improve the speed and efficiency of clinical trials, it could reshape how quickly new treatments reach patients worldwide.⏱️ TIMESTAMPS(0:00) Welcome to Built This Week (0:37) Introducing Dr. Chadi Nabhan from Ryght AI (1:12) What RyghtAI is building (2:14) The problem with clinical trial site selection (3:07) Digital twins for clinical trial sites (4:01) Manual vs AI-driven trial strategy simulation (5:15) Why clinical trials fail (6:03) The massive cost and time of drug development (6:51) How AI identifies the best trial sites (8:00) Ranking clinical trial sites using AI scoring (9:03) Diversity challenges in clinical trials (10:02) Using census data to improve patient representation (10:35) Biomarkers and genomic trial requirements (11:48) Predicting future trial success from past data (12:14) How AI accelerates trial matching (13:04) AI agents reading clinical trial protocols (14:20) Parsing 200-page protocols in seconds (15:00) AI identifying investigators and site contacts (15:57) Helping overlooked clinical sites get discovered (17:47) AI’s expanding role in healthcare innovation (18:00) Eight Sleep raises $50M at a $1.5B valuation (21:09) Apple releases a $599 MacBook (23:00) Dr. Nabhan’s upcoming book: AI and Cancer Care (23:33) Will AI replace Google for patient research? (25:30) The future of personalized AI healthcare (26:10) Final thoughts and wrap-up🔗 LINKSRyght AI https://ryght.aiDr. Chadi Nabhan https://chadinabhan.comBuilt This Week New episodes every Friday🎙️ HOSTSJordan Metzner https://linkedin.com/in/jordanmetzner https://x.com/mrjmetzSam Nadler https://linkedin.com/in/sam-nadler-1881b75 https://x.com/Gravino05

Construction has lagged behind every major industry in technology adoption.Manual data entry. Spreadsheets. Email-based procurement. Slow invoice approvals. Paper delivery tickets.That’s finally changing.In this episode of Built This Week, Sam Nadler and Jordan Metzner sit down with Eldar (Field Materials AI) to break down how AI is automating procurement for commercial and civil contractors — from reading quotes and invoices to verifying pricing, matching delivery tickets, and integrating directly with ERPs.Field Materials builds AI agents that eliminate manual data entry across the procure-to-pay cycle for electrical, mechanical, concrete, drywall, and other commercial subcontractors working on hospitals, data centers, and billion-dollar infrastructure projects.We also explore:• Why construction productivity has barely improved in decades • How AI agents read and process supplier quotes automatically • How foundational model improvements upgrade products overnight • Why procurement automation directly impacts margin • The data center boom forcing construction to modernize • The difference between “adding AI” and building AI-first software • Whether incumbents like SAP and Salesforce are at risk • Why we may be entering a golden era for construction technologyThis isn’t theoretical AI.This is production AI operating inside large-scale commercial construction projects today.⏱️ TIMESTAMPS(0:00) Entering the golden era of construction tech (0:24) Welcome to Built This Week (0:43) Introducing Field Materials AI (1:12) What Field Materials actually does (1:41) Scenario modeling demo (BOM shock analysis) (3:51) Pricing intelligence and risk modeling (4:53) How the company started (6:13) Automating quotes, invoices, and delivery tickets (7:23) Who uses Field Materials (commercial subs) (8:49) How procurement actually works today (manual chaos) (10:07) Cutting overhead and scaling without hiring (11:29) Reducing material waste and pricing errors (12:25) Accelerating invoice approval cycles (13:04) AI agents for different document types (14:01) How foundational model upgrades improve the product (15:09) Why construction underinvested in tech (15:52) The data center boom forcing modernization (16:49) AI + robotics + prefabrication (17:31) Anthropic partnerships and enterprise AI integration (18:39) The next wave: AI with “hands” in enterprise systems (19:49) Why incumbents risk building gimmicks (21:07) Salesforce, SAP, and retention vs innovation (24:12) COBOL, modernization, and disruption cycles (26:39) Why building real AI tools is still hard (27:03) Where to find Field Materials🔗 LINKSField Materials https://fieldmaterials.aiBuilt This Week New episodes every Friday🎙️ HOSTSJordan Metzner https://linkedin.com/in/jordanmetzner https://x.com/mrjmetzSam Nadler https://linkedin.com/in/sam-nadler-1881b75 https://x.com/Gravino05

DNA is just another language.In Episode 32 of Built This Week, we sit down with Dov Gertz, founder of Converge Bio, to explore how generative AI is transforming drug discovery.Every human can be represented as 3.2 billion nucleotides built from four letters: A, C, G, and T. If computers run on zeros and ones, we run on biological code.Converge Bio is training frontier foundation models on DNA, RNA, proteins, and small molecules — helping biotech and pharma companies design better drugs, faster and cheaper.We also demo a retro-inspired “Cell Defense Arena” game built for Converge to use at conferences.Then we pivot into AI infrastructure and agent workflows:The GPU bottleneck and pharma’s growing demand for compute Why molecular AI is 5 to 10 years behind text models How AI could reduce drug timelines from 10 years to 6 to 8 Why cancer and autoimmune diseases may benefit first The limits of FDA regulation in shortening approval cycles OpenClaw, multi-agent systems, and infinite AI teams Cloud versus on prem in the era of foundation modelsThe big takeaway:Chatbots are impressive. But AI applied to biology could extend human life.If you work in biotech, pharma, AI research, or frontier infrastructure — this episode is for you.New episodes every Friday.⏱ TIMESTAMPS(0:00) DNA as code: 3.2 billion nucleotides (0:32) Welcome to Episode 32 (1:00) Meet Dov Gertz and Converge Bio (2:02) Demo: Cell Defense Arena game (3:25) Converge Bio’s $33M raise and mission (4:05) Foundation models for molecular data (5:00) Turning DNA, RNA, and proteins into machine-readable text (6:02) How transformers apply to biology (7:03) 400x more DNA than text on the internet (8:02) Who Converge’s customers are (9:21) Faster, cheaper, better drug discovery (10:39) The three bottlenecks: data, architecture, compute (12:02) The future of personalized medicine (13:02) Which diseases benefit first: cancer, diabetes, autoimmune (14:00) Regulatory realities and clinical trial timelines (16:30) Will AI shorten drug approval cycles? (17:01) NVIDIA, GPUs, and scaling molecular AI (18:30) Pharma as a new AI infrastructure consumer (19:13) Hard pivot: OpenClaw and agentic AI (21:26) Managing teams of AI agents (22:20) Cloud versus on prem debate (25:02) Why developers must adapt weekly (29:26) Closing thoughts and where to find Converge Bio🔗 LINKSConverge Bio https://converge-bio.comBuilt This Week New episodes every Friday https://builtthisweek.comJordan Metzner https://x.com/mrjmetzSam Nadler https://x.com/Gravino05

Private equity due diligence used to take hundreds of hours. Now it takes seconds.In Episode 31 of Built This Week, we sit down with August Kiles, Head of Product at Emblem, to break down how AI is transforming investment funds — from venture capital to growth equity to private equity.Emblem is building what they call the “last platform investors will ever need” — a system that ingests entire data rooms, extracts financials, compares deals, generates reports in Word, Excel, and PowerPoint, and helps funds get to a “no” faster.We also demo a portfolio scenario simulation tool inspired by Emblem — showing how macro events like regulatory pressure or liquidity surges could impact a 30-company portfolio.Then we dive into the latest AI news:Amazon engineers pushing for Claude Code over internal toolsWhy Opus 4.6 is a step-function improvement for codingHow AI is changing software development workflowsElon Musk’s XAI reorg and what it signals about model competitionThe big takeaway:AI is not eliminating analysts. It’s increasing deal throughput and freeing them to focus on alpha.If you work in VC, private equity, family offices, or growth equity — this episode is for you.New episodes every Friday.⏱ TIMESTAMPS(0:00) Emblem’s mission: the last platform investors will ever need (0:25) Welcome to Episode 31 (0:55) Meet August Kiles from Emblem (1:28) Building a portfolio scenario simulation tool (2:05) Modeling regulatory pressure across a 30-company fund (3:00) Liquidity supernova scenario explained (4:00) What Emblem actually does for investment funds (5:00) AI-powered due diligence and data room indexing (6:00) From 100 hours of analysis to seconds (7:20) The old way vs the AI-powered way (8:30) Will AI reduce analyst headcount? (9:40) Getting to “no” faster in private equity (10:30) Where Emblem shines: seed vs private equity (12:00) Multi-agent model orchestration inside Emblem (13:00) How new models improved financial modeling (15:00) Amazon engineers pushing for Claude Code (17:30) Step-function improvements in Opus 4.6 (19:00) Coding workflows transformed by new models (21:30) Elon Musk’s XAI reorganization (23:00) Why model quality now matters more than IDE (25:00) Final thoughts and wrap-up🔗 LINKSEmblem https://emblem.peBuilt This Week New episodes every Friday https://builtthisweek.comJordan Metzner https://x.com/mrjmetzSam Nadler https://x.com/Gravino05

The biggest shift in AI isn’t a new model. It’s agents managing other agents.In Episode 30 of Built This Week, Sam Nadler and Jordan Metzner break down how they’re actually using the latest AI releases — including Claude Opus 4.6 and OpenAI Codex 5.3 — to build real software inside their own workflows.Jordan walks through a private, fully local AI system built with Claude Code that turns raw 23andMe data, blood work, medications, and personal health inputs into a unified health dashboard. The goal isn’t diagnostics — it’s creating a long-term, living record that surfaces insights doctors don’t easily connect.Sam then demos an AI-powered personal trainer built using the new Codex desktop Mac app and high-reasoning models. The system adapts workouts rep-by-rep, adjusts volume in real time, and highlights the tradeoffs between fast iteration tools and slower, deeper reasoning workflows.We close with the biggest AI platform launches of the week:Anthropic’s Opus 4.6 and Agent TeamsOpenAI Frontier and enterprise AI coworkersPerplexity’s Council Mode and LLM swarmsThe era of one chatbot at a time is over. The new skill is learning how to manage AI agents that manage other agents.No hype. No abstractions. Just what actually happens when builders use AI on themselves first.New episodes every Friday.TIMESTAMPS(0:00) The shift from single-agent AI to multi-agent systems (0:21) Welcome to Built This Week Episode 30 (1:00) Agenda and why this week matters (1:38) Why Jordan downloaded his 23andMe data (2:30) Turning unreadable DNA files into usable insights (3:50) Combining genetics, blood work, and medications (5:05) Drug response insights and hereditary signals (6:10) Generating doctor-ready reports for family (7:20) Why this system runs fully local (8:00) Building personal software instead of buying tools (8:40) Sam’s AI personal trainer built with Codex (9:50) Rep-by-rep workout feedback and fatigue detection (10:45) Designing AI interfaces for real-world use (11:40) Codex vs Claude Code: speed vs deep reasoning (12:20) Anthropic Opus 4.6 and Agent Teams (13:00) OpenAI Frontier and AI coworkers (13:25) Perplexity Council Mode and model swarms (14:05) Why multi-agent management is the real inflection (15:15) Becoming a manager of AI managers (16:00) How many agents one human can manage (17:00) AI’s impact on legacy software companies (18:15) Episode 30 wrap-up and what’s nextLINKSBuilt This Week New episodes every Friday https://builtthisweek.comJordan Metzner https://x.com/mrjmetzSam Nadler https://x.com/Gravino05