The Signal Lab

Javier Valencia applied to more than 200 jobs on LinkedIn. Every interview he landed came through someone he knew. Javier had spent years leading recruiting teams and implementing systems like Workday and iCIMS. He knew why employers built the application process the way they did. Then he had to get through it himself.The people who helped him get interviews were mostly peers or people who had worked for him. They could tell a hiring manager what they had seen him do and why he was worth talking to. What could those people see that his application couldn’t show?Why this mattersJavier has been responsible for filling a role that drew a thousand applicants. He can explain why a recruiter in that position asks more screening questions and why reviewing every application is hard to imagine. He also knows what it’s like to apply for a job he believes he can do and never hear back. Those experiences lead him and host Ian Davidson to disagree about when a hiring decision actually happens. If the software ranks the applicants and the recruiter reviews only the top 25, who decided which people got a chance?About Javier and IanJavier began recruiting in the U.S. Army and went on to recruit for technical schools and the collision repair industry, lead recruiting teams, and implement applicant tracking systems. He and Ian first knew each other in high school. They reconnected while Javier was searching for work. Ian is the founder and CEO of Signol Labs, where he works with employers, workforce leaders, and technology teams on the systems that connect learning and work. Connect with Javier · Connect with IanWhat you’ll hearJavier describes what his application experience at Meta felt like when it pointed him toward other roles instead of leaving him to guess. He walks through how he would audit a recruiting workflow and makes a provocative case for retiring some job postings in favor of direct outreach. The conversation gets personal when he talks about what he wants for his kids and what he learned watching technicians earn qualifications they could take to their next employer.Chapters00:00 — When does a hiring decision happen? 02:00 — From Army recruiting to leading recruiting teams 06:00 — Designing a workflow for candidates, recruiters, and hiring managers 09:00 — What useful feedback felt like at Meta 12:00 — Where Javier’s interviews came from 16:00 — What happens when one role draws a thousand applicants 20:00 — Who gets seen when the recruiter reviews the top 25? 25:00 — What Javier would audit when he returns to recruiting 27:00 — The case for posting fewer jobs 30:00 — What proof of capability could travel with a worker? 36:00 — What happens when nobody is there to vouch for you?Continue the conversationIf you’re designing the profile at the center of a talent marketplace, I’d like to hear what you’re trying to help employers see. Talk with Signol Labs. If someone comes to mind who could help us understand another part of this system, send a guest suggestion to [email protected] The Signal Lab and share this episode with someone who decides how applicants get reviewed. Keep listening for the signal.

What happens when the systems we use to connect people to jobs no longer reflect how the work gets done?Employers are finding expertise through freelance, fractional, and contract arrangements that don’t fit neatly into a conventional job posting. Workers are building careers across projects and clients. Thousands of specialized marketplaces help connect them—but finding the right destination creates another challenge for everyone involved.Human Cloud co-founders Matthew Mottola and Tony Buffum join Ian Davidson to explore what employers’ use of flexible talent reveals about how work is changing. Drawing on their experiences at Microsoft, Stanley Black & Decker, and Upwork, they explain why companies adopted these approaches and what still makes employers hesitate.The conversation turns on a different starting question: “What does success look like?” When employers begin with an outcome rather than a job description, they have to think more specifically about the capabilities needed to deliver it. Ian asks whether that experience offers a preview of how more organizations may design their teams as AI changes the work.Ian closes by examining why adding more marketplaces doesn’t necessarily create a stronger workforce system. Whether the opportunity is a permanent position or a six-week contract, employers need better ways to describe the capabilities required, and workers need profiles that carry evidence of what they can do across systems.This is the third episode in Act 3, “This Is Not a System,” our continuing investigation into why the workforce system does not function like a system. Next, Army Special Forces veteran Steve Geib and his wife, Sytease, help us examine what happens when individuals must connect the pieces themselves.EPISODE CHAPTERS00:00 - When full-time jobs aren’t the whole picture 03:00 - How Tony and Matt found a shared mission 04:00 - Microsoft’s freelance experiment 06:00 - Why traditional hiring wasn’t fast enough 08:00 - Employer adoption and thousands of specialized marketplaces 11:00 - Decision fatigue and our fictional sponsor, FullTimeJobs.com 12:00 - How AI changes the way employers organize work 16:00 - Starting with “What does success look like?” 18:00 - How evidence builds trust 19:00 - What replaces familiar resume signals? 21:00 - Looking beneath the credential 23:00 - Talent marketplaces as a form of certification 24:00 - Portable profiles and inconsistent data 26:00 - Can workers take their reputation with them? 28:00 - Credentials, accomplishments, and evidence that travels 30:00 - What state marketplaces miss by designing only for full-time work 32:00 - Feedback loops and evidence beyond claims 34:00 - Building shared infrastructure as work changes Explore Human Cloud: https://humancloud.com/Explore Signal Mapping: https://signollabs.com/blog-signal-mapping/Learn about Signol Labs: https://signollabs.com/Working to connect human capability with opportunity? Contact Ian at [email protected] or https://signollabs.com/contact-us/.Subscribe to The Signal Lab and share this conversation with someone building your region’s workforce.

America wants more ships, more chips, more advanced manufacturing, and a workforce ready for AI. Who is responsible for preparing the people who will do that work?We call it the workforce system. But employers, colleges, training providers, workforce boards, and government agencies each own a piece. Who connects them? Who makes sure a training program leads to a job? And who helps workers navigate the distance between the two?Paul Fain, editor at Workshift and writer of The Job newsletter, joins Ian Davidson to examine what the workforce system actually is, how it is funded, and why so much responsibility falls to states and regions. Paul reports across the boundaries between education, workforce policy, and employer demand. That perspective helps reveal what happens when institutions pursue related goals without the coordination to make them work together.The conversation starts with the Workforce Innovation and Opportunity Act, or WIOA, the main federal workforce framework. Paul describes a system smaller than many people realize, with funding divided among programs and administered through states and local workforce boards. The question quickly becomes larger: if employers need qualified workers, who should pay to prepare them, and what should employers commit to in return?AI makes the coordination problem more urgent. Near the end, Paul describes the possibility of labor-market disruption on the scale of industrialization. Ian's conclusion: no one is coming to assemble this for us. States, employers, educators, and workforce organizations will have to build the capacity to respond together.This is the second episode in Act 3, our continuing investigation into why the workforce system does not function like a system. Next, Human Cloud co-founders Matthew Mottola and Tony Buffum help us examine work spreading across projects, clients, and platforms.EPISODE CHAPTERS 00:00 - A workforce system built around figuring it out alone 03:00 - What is the workforce system? 06:00 - How workforce funding reaches people 09:00 - Who should pay to prepare workers? 13:00 - Connecting education to jobs that pay off 15:00 - Oklahoma and employer-funded training ]18:00 - What coordinated infrastructure requires 20:00 - Industrial policy and local responsibility 21:00 - Why states are being asked to do more 23:00 - State talent marketplaces and connected data 28:00 - National ambitions meet regional economies 34:00 - AI disruption and support beyond tuition 38:00 - No one is coming to assemble this for usRead Paul's reporting: https://workshift.org/ Connect with Paul: https://www.linkedin.com/in/paulfain-thejob/ Explore Signal Mapping: https://signollabs.com/blog-signal-mapping/ Learn about Signol Labs: https://signollabs.com/ Working to connect training and employer demand? Contact Ian at [email protected] or https://signollabs.com/contact-us/. Subscribe to The Signal Lab and share this conversation with someone building your region's workforce.

How do we prepare workers for disruption if we cannot see how work is changing?Artificial intelligence is already changing how employers operate, organize work, and value different capabilities. Yet much of the workforce ecosystem still relies on job postings to understand employer demand. Those postings can be useful, but they are advertisements. They do not reliably tell us which opportunities are real, who was hired, what work that person performs, what the job pays, or whether it lasts.Economist Julia Lane has spent decades working on this problem. She is a professor at New York University, and author of Democratizing Our Data.Julia explains why technological change reaches workers through employer decisions. Companies choose whether to adopt AI, how to redesign their operations, and which workers and capabilities they need. Understanding the future of work therefore requires better information about what employers actually do - not only what they advertise.This is a conversation about data, but its consequences are deeply human. Bad information wastes employer resources, directs training dollars toward uncertain outcomes, and leaves workers making consequential choices without a clear view of opportunity. Better information could change the narrative from inevitable disruption to informed adaptation.EPISODE CHAPTERS00:00 - The Planes That Never Returned: How selection bias distorts what we think we know. 01:10 - Why We Cannot Prepare for Change We Cannot See: The limits of job-posting data. 05:00 - Learning at Work: Why on-the-job training matters across a working life. 06:40 - Labor Supply, Employer Demand, and Technology: Where economic disruption enters the workforce. 08:20 - The Missing Longitudinal View: Why surveys struggle to capture labor-market churn. 10:35 - The Employment Data We Already Collect: What unemployment-insurance wage records reveal. 13:10 - Data Built for Yesterday's Questions: Moving from surveys to administrative records. 14:15 - Industries of Ideas: Tracking how technology shocks move through local economies. 16:10 - What Early AI Adoption May Be Showing: Initial findings from Ohio employers. 17:10 - What JEDx Could Add: Better work locations and faster reporting. 18:25 - What Better Data Means for Learners: Local jobs, earnings growth, and durable pathways. 20:00 - Why Job Postings Are Not Enough: Representation, documentation, and validation. 21:35 - Building the Missing Feedback Loop: Connecting training to actual worker outcomes. 23:05 - A More Optimistic Future of Work: How better signals could help employers and workers adapt.Learn more about Julia Lane and Industries of Ideas: https://industriesofideas.ai/ Connect with Julia: https://www.linkedin.com/in/julia-lane/ Explore Signal Mapping: https://signollabs.com/blog-signal-mapping/ Learn more about Signol Labs: https://signollabs.com/ Connect with Ian Davidson: https://www.linkedin.com/in/ianwdavidson/ Recommend a future guest: [email protected]

What do employers actually want?What skills matter? What credentials will they trust? Can better data improve hiring? After three months investigating how hiring decisions actually get made, Ian Davidson thinks we're starting with the wrong question.The better question is: Which decision are you trying to change?Hiring isn't one decision. Job boards decide which opportunities people see. Applicant tracking systems decide who gets surfaced. Recruiters decide who gets a conversation. Hiring managers decide who gets the job.Together, those decisions allocate one of hiring's scarcest resources: human attention.And the decisions affecting the most candidates often happen when the system knows the least about them.Episode 10 brings together insights from Allison Danielsen, Matt Plummer, Andrew Cunsolo, Jordan Franklin, Matt Charney, Brad Robinson, and James Harris.The investigation began as a search for better signals. It became the Hiring Decision Architecture: a framework for identifying which hiring decision we're trying to improve, what information could make it better, and where to intervene.EPISODE CHAPTERS00:00 — What Do Employers Actually Want? Why skills and credentials may be the wrong starting point.05:00 — What Hiring Managers Really Evaluate Motivation, potential, work ethic, learning, and what resumes miss.12:00 — Skills-Based Hiring and the Replacement Problem What replaces degree and experience requirements?14:00 — Application Overload, ATSs and AI Hiring What happens when employers receive 1,000 applications?16:00 — Hiring Is an Allocation of Human Attention Why consequential decisions happen with limited candidate information.18:00 — The “Cake Lady” and the Candidates Systems Miss A great hire who never would have survived the conventional process.21:00 — Better Candidate and Opportunity Profiles Why resumes and job descriptions both leave out critical context.26:00 — The Hiring Decision Architecture Start with the decision, then identify the information needed to improve it.28:00 — Talent Marketplaces and Workforce Intelligence What if matching systems understood people's needs and constraints?33:00 — Fix the System Before the Hiring Manager Improve the decisions determining who reaches a human.37:00 — AI, Machine-Readable Data and Verifiable Skills Technology can use richer information without overwhelming recruiters.40:00 — Better Talent Marketplace Matching Matching capability, potential, motivation, needs, and opportunity.42:00 — Learning and Employment Records + Trusted Career Profile How portable, verifiable data could improve hiring decisions.45:00 — The Demand-Side Case for Better Workforce Data Employers aren't asking for LERs. They're asking for help with 1,200 applicants.48:00 — Which Hiring Decisions Should We Fix? Stop starting with data. Start with the decision.Research supported by the U.S. Chamber of Commerce Foundation and T3 Innovation Network.Explore the Hiring Decision Architecture: https://signollabs.com/hiring-decision-architecture/Signol Labs: https://signollabs.com/Subscribe to The Signal Lab wherever you listen to podcasts.