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Jun 14, 2023 · 46m · we-live-to-build

People Logic Found a Signal in Our Tools We Never Knew to Look For

Matthew Schmidt · 27m spoken Sean Weisbrot · 14m spoken
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In this episode of the 'We Live to Build' podcast, host Sean Weisbrot and PeopleLogic CEO Matthew Schmidt explore how integrating artificial intelligence, automated workflows, and organizational network analysis can streamline enterprise operations while keeping essential human judgment at the center of workforce management.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Sean holds 36% of the talking time here. How this is scored →

Sean as informed peer 3.8 Guest teaching 3.1 Guest disagreement 0.9 Sean pushing back 0.9
05100:0015:0030:0045:003:00–7:21 · Sean as informed peer 4/10 Founding PeopleLogic and Lessons from Startup Exits Sean asks probing questions about the financial ramifications of operational errors on startup exits, estimating valuation losses based on founder equity.7:21–11:01 · Sean as informed peer 5/10 Streamlining Operations and Alleviating Overburdened HR Teams Sean articulates an operator's perspective on M&A due diligence and why prospective buyers target unaddressed HR inefficiencies. Matthew strongly validates this diagnosis.11:01–13:21 · Sean as informed peer 2/10 AI Revolution in Talent Acquisition and Recruitment Matthew educates Sean on the modern recruitment landscape, highlighting how synthetic applicant personas and generative AI headshots conflict with screening software.13:21–19:37 · Sean as informed peer 6/10 Manual Hiring Funnels versus AI-Driven Candidate Screening Sean walks through his custom manual hiring pipeline designed to filter for instructions-following candidates. Matthew contextualizes the scale threshold where startups must avoid AI.19:38–23:17 · Sean as informed peer 4/10 Empowering Frontline Managers with PeopleLogic Insights Sean challenges Matthew's frontline manager empowerment model, pushing that organizational strategy must operate top-down. Matthew clarifies the executive enablement framework.23:18–26:20 · Sean as informed peer 3/10 Leveraging AI Chatbots for HR Knowledge Dissemination Matthew reframes Sean's premise, clarifying that automation and integration workflows do not require AI, as AI's primary function is pattern detection across data silos.26:20–29:54 · Sean as informed peer 6/10 Internal Knowledge Bots and Operational Efficiency Case Studies Sean presents empirical data from a corporate study showing AI-assisted customer service reps reaching 6-month proficiency in 1 month, extrapolating the finding to engineering docs.29:55–36:44 · Sean as informed peer 4/10 Overcoming Automation Fears and Elevating HR Roles Sean highlights the difficulty of training AI on subjective corporate cultural values. Matthew agrees, noting today's models lack human intuition and recency bias correction.36:45–39:05 · Sean as informed peer 2/10 Managing Diversity, Equity, and Inclusion with AI Matthew explains why automated DEI enforcement in hiring is hazardous and points out that HR tech startups lack the massive compute budgets of foundation model providers.39:05–41:37 · Sean as informed peer 3/10 Building Knowledge Graphs and Organizational Network Analysis Matthew breaks down the architecture of knowledge graphs and organizational network analysis (ONA), explaining how PeopleLogic tracks interconnected people, deals, and projects.41:38–45:45 · Sean as informed peer 3/10 Optimistic Futurism and Preserving Human Free Will The conversation turns philosophical as Matthew shares an optimistic futurist outlook, while both agree that automating governance threatens human free will.3:00–7:21 · Guest teaching 2/10 Founding PeopleLogic and Lessons from Startup Exits Sean asks probing questions about the financial ramifications of operational errors on startup exits, estimating valuation losses based on founder equity.7:21–11:01 · Guest teaching 1/10 Streamlining Operations and Alleviating Overburdened HR Teams Sean articulates an operator's perspective on M&A due diligence and why prospective buyers target unaddressed HR inefficiencies. Matthew strongly validates this diagnosis.11:01–13:21 · Guest teaching 4/10 AI Revolution in Talent Acquisition and Recruitment Matthew educates Sean on the modern recruitment landscape, highlighting how synthetic applicant personas and generative AI headshots conflict with screening software.13:21–19:37 · Guest teaching 3/10 Manual Hiring Funnels versus AI-Driven Candidate Screening Sean walks through his custom manual hiring pipeline designed to filter for instructions-following candidates. Matthew contextualizes the scale threshold where startups must avoid AI.19:38–23:17 · Guest teaching 4/10 Empowering Frontline Managers with PeopleLogic Insights Sean challenges Matthew's frontline manager empowerment model, pushing that organizational strategy must operate top-down. Matthew clarifies the executive enablement framework.23:18–26:20 · Guest teaching 5/10 Leveraging AI Chatbots for HR Knowledge Dissemination Matthew reframes Sean's premise, clarifying that automation and integration workflows do not require AI, as AI's primary function is pattern detection across data silos.26:20–29:54 · Guest teaching 1/10 Internal Knowledge Bots and Operational Efficiency Case Studies Sean presents empirical data from a corporate study showing AI-assisted customer service reps reaching 6-month proficiency in 1 month, extrapolating the finding to engineering docs.29:55–36:44 · Guest teaching 3/10 Overcoming Automation Fears and Elevating HR Roles Sean highlights the difficulty of training AI on subjective corporate cultural values. Matthew agrees, noting today's models lack human intuition and recency bias correction.36:45–39:05 · Guest teaching 4/10 Managing Diversity, Equity, and Inclusion with AI Matthew explains why automated DEI enforcement in hiring is hazardous and points out that HR tech startups lack the massive compute budgets of foundation model providers.39:05–41:37 · Guest teaching 5/10 Building Knowledge Graphs and Organizational Network Analysis Matthew breaks down the architecture of knowledge graphs and organizational network analysis (ONA), explaining how PeopleLogic tracks interconnected people, deals, and projects.41:38–45:45 · Guest teaching 2/10 Optimistic Futurism and Preserving Human Free Will The conversation turns philosophical as Matthew shares an optimistic futurist outlook, while both agree that automating governance threatens human free will.3:00–7:21 · Guest disagreement 1/10 Founding PeopleLogic and Lessons from Startup Exits Sean asks probing questions about the financial ramifications of operational errors on startup exits, estimating valuation losses based on founder equity.7:21–11:01 · Guest disagreement 0/10 Streamlining Operations and Alleviating Overburdened HR Teams Sean articulates an operator's perspective on M&A due diligence and why prospective buyers target unaddressed HR inefficiencies. Matthew strongly validates this diagnosis.11:01–13:21 · Guest disagreement 1/10 AI Revolution in Talent Acquisition and Recruitment Matthew educates Sean on the modern recruitment landscape, highlighting how synthetic applicant personas and generative AI headshots conflict with screening software.13:21–19:37 · Guest disagreement 1/10 Manual Hiring Funnels versus AI-Driven Candidate Screening Sean walks through his custom manual hiring pipeline designed to filter for instructions-following candidates. Matthew contextualizes the scale threshold where startups must avoid AI.19:38–23:17 · Guest disagreement 1/10 Empowering Frontline Managers with PeopleLogic Insights Sean challenges Matthew's frontline manager empowerment model, pushing that organizational strategy must operate top-down. Matthew clarifies the executive enablement framework.23:18–26:20 · Guest disagreement 2/10 Leveraging AI Chatbots for HR Knowledge Dissemination Matthew reframes Sean's premise, clarifying that automation and integration workflows do not require AI, as AI's primary function is pattern detection across data silos.26:20–29:54 · Guest disagreement 0/10 Internal Knowledge Bots and Operational Efficiency Case Studies Sean presents empirical data from a corporate study showing AI-assisted customer service reps reaching 6-month proficiency in 1 month, extrapolating the finding to engineering docs.29:55–36:44 · Guest disagreement 1/10 Overcoming Automation Fears and Elevating HR Roles Sean highlights the difficulty of training AI on subjective corporate cultural values. Matthew agrees, noting today's models lack human intuition and recency bias correction.36:45–39:05 · Guest disagreement 1/10 Managing Diversity, Equity, and Inclusion with AI Matthew explains why automated DEI enforcement in hiring is hazardous and points out that HR tech startups lack the massive compute budgets of foundation model providers.39:05–41:37 · Guest disagreement 1/10 Building Knowledge Graphs and Organizational Network Analysis Matthew breaks down the architecture of knowledge graphs and organizational network analysis (ONA), explaining how PeopleLogic tracks interconnected people, deals, and projects.41:38–45:45 · Guest disagreement 1/10 Optimistic Futurism and Preserving Human Free Will The conversation turns philosophical as Matthew shares an optimistic futurist outlook, while both agree that automating governance threatens human free will.3:00–7:21 · Sean pushing back 1/10 Founding PeopleLogic and Lessons from Startup Exits Sean asks probing questions about the financial ramifications of operational errors on startup exits, estimating valuation losses based on founder equity.7:21–11:01 · Sean pushing back 0/10 Streamlining Operations and Alleviating Overburdened HR Teams Sean articulates an operator's perspective on M&A due diligence and why prospective buyers target unaddressed HR inefficiencies. Matthew strongly validates this diagnosis.11:01–13:21 · Sean pushing back 0/10 AI Revolution in Talent Acquisition and Recruitment Matthew educates Sean on the modern recruitment landscape, highlighting how synthetic applicant personas and generative AI headshots conflict with screening software.13:21–19:37 · Sean pushing back 1/10 Manual Hiring Funnels versus AI-Driven Candidate Screening Sean walks through his custom manual hiring pipeline designed to filter for instructions-following candidates. Matthew contextualizes the scale threshold where startups must avoid AI.19:38–23:17 · Sean pushing back 4/10 Empowering Frontline Managers with PeopleLogic Insights Sean challenges Matthew's frontline manager empowerment model, pushing that organizational strategy must operate top-down. Matthew clarifies the executive enablement framework.23:18–26:20 · Sean pushing back 1/10 Leveraging AI Chatbots for HR Knowledge Dissemination Matthew reframes Sean's premise, clarifying that automation and integration workflows do not require AI, as AI's primary function is pattern detection across data silos.26:20–29:54 · Sean pushing back 0/10 Internal Knowledge Bots and Operational Efficiency Case Studies Sean presents empirical data from a corporate study showing AI-assisted customer service reps reaching 6-month proficiency in 1 month, extrapolating the finding to engineering docs.29:55–36:44 · Sean pushing back 2/10 Overcoming Automation Fears and Elevating HR Roles Sean highlights the difficulty of training AI on subjective corporate cultural values. Matthew agrees, noting today's models lack human intuition and recency bias correction.36:45–39:05 · Sean pushing back 0/10 Managing Diversity, Equity, and Inclusion with AI Matthew explains why automated DEI enforcement in hiring is hazardous and points out that HR tech startups lack the massive compute budgets of foundation model providers.39:05–41:37 · Sean pushing back 0/10 Building Knowledge Graphs and Organizational Network Analysis Matthew breaks down the architecture of knowledge graphs and organizational network analysis (ONA), explaining how PeopleLogic tracks interconnected people, deals, and projects.41:38–45:45 · Sean pushing back 1/10 Optimistic Futurism and Preserving Human Free Will The conversation turns philosophical as Matthew shares an optimistic futurist outlook, while both agree that automating governance threatens human free will.

speaking balance: gold is Sean, purple is the guest (3 minute bins)

0:00 · Sean 64.5% · guest 35.5%0:00 · Sean 64.5% · guest 35.5%3:00 · Sean 10.5% · guest 89.5%3:00 · Sean 10.5% · guest 89.5%6:00 · Sean 77.9% · guest 22.1%6:00 · Sean 77.9% · guest 22.1%9:00 · Sean 33.9% · guest 66.1%9:00 · Sean 33.9% · guest 66.1%12:00 · Sean 54.6% · guest 45.4%12:00 · Sean 54.6% · guest 45.4%15:00 · Sean 86% · guest 14%15:00 · Sean 86% · guest 14%18:00 · Sean 6.4% · guest 93.6%18:00 · Sean 6.4% · guest 93.6%21:00 · Sean 32.4% · guest 67.6%21:00 · Sean 32.4% · guest 67.6%24:00 · Sean 36.9% · guest 63.1%24:00 · Sean 36.9% · guest 63.1%27:00 · Sean 54.9% · guest 45.1%27:00 · Sean 54.9% · guest 45.1%30:00 · Sean 21.2% · guest 78.8%30:00 · Sean 21.2% · guest 78.8%33:00 · Sean 22.7% · guest 77.3%33:00 · Sean 22.7% · guest 77.3%36:00 · Sean 8% · guest 92%36:00 · Sean 8% · guest 92%39:00 · Sean 16.9% · guest 83.1%39:00 · Sean 16.9% · guest 83.1%42:00 · Sean 12.6% · guest 87.4%42:00 · Sean 12.6% · guest 87.4%45:00 · Sean 31.5% · guest 68.5%45:00 · Sean 31.5% · guest 68.5%
Sharpest disagreement ▶ 23:49 Decoupling AI from basic automation

Matthew directly counters Sean's underlying assumption, emphasizing that effective automations and integrations do not require artificial intelligence.

 Hardest push from Sean ▶ 21:23 Challenging bottom-up management delegation

Sean questions Matthew's premise of pushing responsibility down to frontline managers, insisting that organizational change must be driven top-down.

 Biggest teaching moment ▶ 39:09 Knowledge graphs vs organizational network analysis

Matthew defines organizational knowledge graphs for Sean, explaining how mapping cross-functional interactions and work artifacts reveals invisible operational bottlenecks.

 Sean holds their own ▶ 26:20 Citing enterprise support bot performance metrics

Sean cites a research study demonstrating rapid onboarding acceleration via internal Q&A bots, expanding the case into technical documentation and engineering workflows.
the scores for every segment, with the reasoning behind each
ChapterTopicSean as informed peerGuest teachingGuest disagreementSean pushing backWhy
Founding PeopleLogic and Lessons from Startup Exits 4211 Sean asks probing questions about the financial ramifications of operational errors on startup exits, estimating valuation losses based on founder equity.
Streamlining Operations and Alleviating Overburdened HR Teams 5100 Sean articulates an operator's perspective on M&A due diligence and why prospective buyers target unaddressed HR inefficiencies. Matthew strongly validates this diagnosis.
AI Revolution in Talent Acquisition and Recruitment 2410 Matthew educates Sean on the modern recruitment landscape, highlighting how synthetic applicant personas and generative AI headshots conflict with screening software.
Manual Hiring Funnels versus AI-Driven Candidate Screening 6311 Sean walks through his custom manual hiring pipeline designed to filter for instructions-following candidates. Matthew contextualizes the scale threshold where startups must avoid AI.
Empowering Frontline Managers with PeopleLogic Insights 4414 Sean challenges Matthew's frontline manager empowerment model, pushing that organizational strategy must operate top-down. Matthew clarifies the executive enablement framework.
Leveraging AI Chatbots for HR Knowledge Dissemination 3521 Matthew reframes Sean's premise, clarifying that automation and integration workflows do not require AI, as AI's primary function is pattern detection across data silos.
Internal Knowledge Bots and Operational Efficiency Case Studies 6100 Sean presents empirical data from a corporate study showing AI-assisted customer service reps reaching 6-month proficiency in 1 month, extrapolating the finding to engineering docs.
Overcoming Automation Fears and Elevating HR Roles 4312 Sean highlights the difficulty of training AI on subjective corporate cultural values. Matthew agrees, noting today's models lack human intuition and recency bias correction.
Managing Diversity, Equity, and Inclusion with AI 2410 Matthew explains why automated DEI enforcement in hiring is hazardous and points out that HR tech startups lack the massive compute budgets of foundation model providers.
Building Knowledge Graphs and Organizational Network Analysis 3510 Matthew breaks down the architecture of knowledge graphs and organizational network analysis (ONA), explaining how PeopleLogic tracks interconnected people, deals, and projects.
Optimistic Futurism and Preserving Human Free Will 3211 The conversation turns philosophical as Matthew shares an optimistic futurist outlook, while both agree that automating governance threatens human free will.

Statements from this episode (9)

What-if
Schmidt: People management missteps delayed prior startup exit by two-plus years
“I would say those missteps probably increased our time to exit by at least two years, but possibly more.”
Matthew Schmidt Jun 14, 2023 ▶ 5:25
Insight
Weisbrot: Candidate creativity is useless without the ability to follow instructions
“We want them to follow instructions first, because if you can't follow instructions, then your creativity doesn't matter. Right? Because there's a hierarchy. At some point, you're going to have to listen to what someone else says.”
Sean Weisbrot Jun 14, 2023 ▶ 15:19
Insight
Schmidt: Early-stage startups should avoid using AI for hiring
“A startup probably shouldn't use AI in the hiring process, right? The choice and the impact of the individuals at that early stage is too important.”
Matthew Schmidt Jun 14, 2023 ▶ 17:34
Assertion Partly supported
Weisbrot: AI brought support agents to six-month proficiency in one month
“People who had been around in the company for one month were performing as well as, if not better than people who weren't trained that way, who had been at the company for six months.”
Sean Weisbrot Jun 14, 2023 ▶ 27:03
Assertion Supported
Schmidt: Confirm uses ONA to streamline 360 performance reviews
“Companies like confirm that are using ONA to kind of Turn the three 60 process on its head and to save you a bunch of time.”
Matthew Schmidt Jun 14, 2023 ▶ 33:03
Prediction Not checkable as stated
Schmidt: Replacing managers with AI in reviews will create more problems
“But I think, you know, offloading that from the person who's most familiar with it is you know, probably going to create more problems in its work.”
Matthew Schmidt Jun 14, 2023 ▶ 34:06
Opinion
Schmidt: Using AI to infer candidate demographics is a massive mistake
“It should not be making decisions, particularly in the hiring process based on those types of things, right? It should not say, oh, this text feels like it was written by someone who identifies as a female, right? Like if it starts to introduce those sorts of …”
Matthew Schmidt Jun 14, 2023 ▶ 37:38
Assertion Supported
Schmidt: Most HR Tech Companies Lack Resources to Train Custom AI
“And the truth is that most companies who are in the HR tech space don't have the resources that someone like open AI has to train their content. And to train the AI. They just seem to, they don't have a billion dollars from Microsoft.”
Matthew Schmidt Jun 14, 2023 ▶ 38:38
Opinion
Schmidt: Giving up decision-making to AI takes away human essence
“What makes us human is free will and choice and I think to give the, to give that up to anyone, frankly, right, and whether it's AI or not, is taking away what makes us human.”
Matthew Schmidt Jun 14, 2023 ▶ 44:47
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