The Organizational Gravity Podcast

Politics has a way of finding its way into the workplace, whether we invite it or not.Following the 2026 midterm elections, Kelly and I sat down to talk about something that’s become increasingly difficult: having conversations with people who don’t necessarily agree with us.We started with the election results, the political pendulum, and the effects that changes in government have on business. Tariffs, regulations, project costs, and now artificial intelligence all have consequences that extend well beyond Washington. Those consequences eventually find their way into our organizations, our teams, and our conversations.But somewhere along the way, the discussion became less about politics and more about people.Regardless of how we voted, we still have to come back to work together.And that means finding ways to understand each other without needing to agree on everything.For years, we’ve treated politics, religion, and other personal subjects as things that shouldn’t be discussed at work. There are understandable reasons for those boundaries. But people don’t arrive at work without their beliefs, experiences, fears, or concerns. Those things influence how we communicate, how we interpret decisions, and how we relate to the people around us.This is part of what I call human middleware: the often invisible work people do to manage relationships, bridge differences, interpret intentions, and keep organizations functioning.It’s also a form of Organizational Gravity. The more tension, uncertainty, and misunderstanding people carry, the more effort it takes to accomplish even ordinary things.We talk about why creating room for difficult conversations matters, and why understanding someone is not the same as agreeing with them. A workplace can allow people to speak openly without turning every disagreement into a debate. Sometimes the most valuable thing a leader can do is listen, recognize where someone is coming from, and leave the conversation there.What happens when AI enters the conversation?Our discussion eventually turns to artificial intelligence, regulation, and the uncomfortable questions surrounding the technology’s future.We explore Anthropic’s recent IPO filings, including warnings about the potential for catastrophic or existential risks from sufficiently advanced AI systems. That leads to a discussion about what responsible governance should look like, and whether the pace of technological advancement is getting ahead of our ability to understand its consequences.But there’s a more personal question underneath all of it.What happens to the relationships that make work meaningful when we begin automating more of the work itself?Kelly raises something I think deserves much more attention: the value of human connection isn’t always something we can measure.We spend an extraordinary amount of time working with other people. We develop friendships, build trust, disagree, collaborate, and occasionally drive each other crazy. Those relationships become part of our lives, not simply an output of our jobs.AI may change how we accomplish work. It may reduce the time required for certain activities. But that doesn’t eliminate our need to connect with one another.And maybe that’s something worth protecting as we build the next generation of organizations.A few things we’ve gotten wrong along the wayIn our regular look at Organizational Gravity, I also talk about something more personal: my own failure résumé.Earlier in my career, I spent a lot of time focused on execution. Deliver the project, hit the budget, get the result. That approach was often rewarded, but it came with a cost I didn’t fully appreciate until much later.Sometimes the relationships mattered more than the result I was pushing so hard to achieve.Kelly shares a very different experience. Early in her career, someone told her she would never get anywhere because she was too nice. Rather than accept that advice, she made kindness and understanding central to how she worked with others.It’s a useful contrast between two approaches to leadership, and a reminder that being effective doesn’t require being harsh.We also talk about the people who help us recognize our own behavior. I share some recent feedback from Audrey about trying so hard to communicate a message that I was actually making it harder for people to hear.Sometimes saying less accomplishes more.That brings us to Jefferson Fisher’s communication advice: allowing yourself a breath before responding to something difficult. Simple advice, and apparently something I still need a lot of practice with.Naturally, we somehow manage to connect that lesson to an electric fence, a jumping cholla cactus, and a couple of parenting decisions that probably deserve their own episode.The thought we’re leaving withWe don’t have to agree on politics. We don’t have to share the same approach to leadership, technology, or organizational change.But we do have to find ways to work together.The relationships, conversations, trust, and occasional uncomfortable feedback that make that possible are easy to overlook because they don’t fit neatly into a project plan or a performance dashboard.They are human middleware.And as technology makes more of our work easier to automate, I suspect that very human work will become even more important.Have the conversations. Be willing to listen. Know when to walk away from a debate. And above all, be kind to the people you’re working with.In this episode* 00:24 — The midterm elections and why politics affects business* 03:24 — Political extremes, organizational change, and the J-curve* 09:55 — Politics at work, psychological safety, and human middleware* 14:16 — Human connection and what AI can’t easily replace* 18:22 — AI regulation, Anthropic’s IPO filings, and technology risk* 21:45 — My failure résumé and lessons from a career focused on execution* 24:17 — Kelly’s experience with being told she was too nice to succeed* 28:59 — Leadership feedback, communication, and knowing when to stop pushing* 30:25 — Parenting lessons involving an electric fence and a cactus* 32:50 — Jefferson Fisher, difficult conversations, and the power of a pause* 35:03 — Why difficult conversations are still worth havingSources and further reading2026 midterm election results: The Associated Press confirmed that Democrats won control of both chambers in the November 3 elections. The newly elected Congress will take office in January 2027. Associated Press — Calling Control of Congress in the 2026 Midterms.AI risks and accountability: Anthropic’s November 2, 2026 amended SEC registration statement discusses the possible catastrophic or existential risks of advanced AI, alongside operational, financial, and regulatory risks. Anthropic — SEC Form S-1/A.State AI legislation: The National Conference of State Legislatures tracks proposed and enacted AI legislation, including requirements around transparency, oversight, responsible use, and discrimination. NCSL — Artificial Intelligence Legislation Database.Psychological safety: Amy Edmondson’s research examines the relationship between interpersonal trust, team learning, and the ability to speak up without fear of embarrassment or punishment. Edmondson (1999), Psychological Safety and Learning Behavior in Work Teams.The importance of human connection: The U.S. Surgeon General’s advisory examines the relationship between social connection, health, and well-being. U.S. Department of Health and Human Services — Social Connection.Communication and conflict: Jefferson Fisher’s The Next Conversation: Argue Less, Talk More explores practical strategies for responding thoughtfully, navigating disagreement, and improving conversations. Jefferson Fisher — The Next Conversation.Organizational Gravity explores the forces that shape how organizations function, the complexity that builds along the way, and the people who make it all work.If this conversation resonated with you, subscribe for free, share it with someone you work… Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

We keep saying AI will give us more time for the human side of work. More time to listen, build relationships, and have the conversations we keep putting off.I like that idea. I also wonder what makes us think we’ll use the time that way.We can fill it with more work. We can use it to take on another responsibility. And if we’re uncomfortable having a difficult conversation, we now have something that can write a very thoughtful message while we remain just as uncomfortable—and just as absent.This week, Colette Molteni joined Kelly and me to talk about empathy, efficiency, and the human work happening around our systems. I came into the conversation worried about what organizations might give up in pursuit of speed. Colette helped me sharpen the question: do we understand which effort we’re trying to remove, and what that effort contributes?The News BeatWhat leaves with the people?We opened with the corporate layoff announcements and the familiar explanation that organizations are shifting toward AI. My concern was the experience those people leave with. Someone may know the process, but they also know where it breaks, which customer needs another conversation, and what to do when the instructions stop being useful.Colette pushed that concern further. If the expected AI gains underdeliver, an organization may already have removed the people who could help recover the work. The people who remain are watching, too. Uncertainty about their own positions can affect their willingness to take risks, invest in relationships, or commit to the next change.An efficiency gain can look different once you account for what happens afterward.Kelly introduced an alternative: IKEA’s approach to reskilling. In 2023, Ingka Group reported that 8,500 call center coworkers had gained skills in areas including remote interior design, digital sales, relationship building, and complex customer inquiries as its Billie chatbot handled simpler questions. That is an older example, but a useful one for this discussion. It shows that adopting AI can include a deliberate decision about where people contribute next.Kelly also noted that people need opportunities to learn how their work is changing. That puts responsibility on both sides. Employees need to develop skills, and organizations need to decide whether they are willing to invest in the people who already understand the business.What is happening to empathy in management?The second story covered research in HR Dive that found managers scored below the general population on measures including empathy. The research draws on personality assessments spanning almost twenty years; it does not establish that AI has recently made managers less empathetic.That distinction matters because Colette immediately questioned the explanation. Are organizations promoting people who demonstrate less empathy? Is the experience of management wearing it down? What expectations and pressures are shaping the behavior?Those are different problems. They need different responses.Our preparation also included Gallup’s reporting on declining engagement, with managers accounting for much of the recent downturn. Gallup connects the discussion to the scope of managers’ responsibilities and notes their importance in supporting AI adoption.The person we expect to guide everyone else through change may be struggling with it themselves. Asking that person to demonstrate more empathy is reasonable. We also need to look at what we’re asking them to carry.The Gravity AuditThe work around the systemHuman Middleware is the work people do to connect what an organization’s systems, processes, and ways of working leave disconnected.I originally understood that in a very tangible way. A person takes information from one place and puts it somewhere else. Someone reconciles two systems. Someone knows how to get a request through because the official process doesn’t quite do the job.I wanted to fix those gaps. I still do.But the conversation with Colette brought us into the relationships around that work: the trust that makes someone willing to raise a concern, the explanation that helps another team understand a request, and the attention that lets a manager notice someone is having a hard time.Kelly and I had a current example. As we prepared to launch a system, we were still discovering conversations and background work. The system could accept more information and included an AI component, but people were still using back channels to get things done.We hadn’t accounted for everything.That is an uncomfortable admission when you’re the person talking about Human Middleware. It is also exactly why I keep testing the idea against actual work. A new system can make the visible process easier while leaving people responsible for the connections around it.Before counting that as an efficiency gain, I want to know what those people are still doing.Some of the labor is the pointColette gave us the example that became the center of the conversation.A neighbor had brought her homemade chicken noodle soup when she was sick. Some efficiency was involved—the soup had been prepared and frozen—but its meaning included the effort and attention behind it. Bringing a can of soup to the doorstep would have been a different gesture.Kelly connected that to a child making a gift. Its value is bound up in the child having made it for you. The finished object is only part of what you receive.That distinction gets harder at work, because we’ve trained ourselves to look for effort we can eliminate.Some effort is unnecessary. Some exists because a process is broken. Some helps us understand another person well enough to make a sound decision or offer useful support.We need to tell those apart.An AI tool can help a manager organize their thoughts before a conversation. It can suggest clearer wording. But the manager still needs to understand what happened, consider the person involved, and take responsibility for what they say.A warm message can be useful. The relationship still needs someone in it.The Conversation with ColetteConnection does not look the same to everyoneWe spent part of the episode talking about remote work, messages, and meeting people in person.Kelly described how her impression of someone had changed after meeting them. Over chat, she had read a particular tone into their messages. In person, she found someone she connected with. I recognized the value of those encounters, too.Colette offered an important qualification. She prefers text to phone calls in many situations, and receiving a message can feel like connection to her. The medium itself does not tell us whether someone cares.That challenged an assumption running through parts of our discussion. We can mistake our preferred way of connecting for the correct way.If empathy involves understanding another person, we have to leave room for their preferences. Someone who wants a message rather than a call is telling us how to reach them.Does AI make room for people?Kelly asked Colette whether AI gives leaders more time to connect with their teams or allows them to avoid difficult conversations.Colette’s answer depended on how the person uses it.Earlier that day, she had used AI to compare code between environments. Saving time on that task could leave her with more attention for a later conversation. She also described another possibility: using AI to summarize a conversation and send an email without absorbing what was discussed.The tool can support either pattern. The person still chooses how involved they want to be.This is where my concern about saved time keeps returning. I’m quite capable of finding another task to put in it. Saving ten hours can become an invitation to accept ten hours of new commitments.We should be specific when we say AI will create more time for relationships. What work is going away? What will we do with the capacity? Will people actually be more available to one another?Empathy under pressure Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

Who do we trust when the technology, companies, and rules are changing this fast?This week, Gabe and Kelly start with a deceptively simple question: who do you actually trust in AI?Is it easier to trust Google because it has been part of our lives for decades? Can newer companies like OpenAI and Anthropic build that same trust—or are they trying to manufacture it faster than trust normally develops?From there, the conversation gets bigger.We talk about the difference between trusting an AI company and trusting what the AI actually tells you; why hallucinations and sycophantic behavior change the relationship; and what happens when AI starts sounding, responding, and even speaking more like another person. If a particular voice feels more familiar, reassuring, or authoritative, does that change how willing we are to believe the answer?Then trust moves from AI into the workplace.Can you trust your manager while distrusting the company? What do leaders really mean when they ask employees to “trust the process” during change? And when we introduce AI into that process, are we automating work—or quietly moving trust from a person into a system?That becomes the Organizational Gravity question at the center of the episode:Does the organization work because it's trustworthy—or because trustworthy people keep making it work?That question matters even more as AI begins taking over pieces of Human Middleware: the people who connect systems, interpret ambiguity, know where the exceptions live, and keep work moving when the formal process is not enough.We also cover AI security, the 2026 Hugging Face incident, Anthropic’s new Public Benefit Fund, the enormous amount of money flowing through AI, and whether corporate visibility and public promises actually build trust—or just create presence.And finally, we turn the question back on leadership.Being trusted doesn’t automatically mean you’re right. Fixing something doesn’t automatically earn trust in the next change. And sometimes one of the clearest ways a leader can demonstrate trust is surprisingly simple:Let go.Not completely. Not irresponsibly. But enough to let someone else own the work, make decisions, and prove what they can do.Maybe trust isn’t something we can announce, market, automate, or demand.Maybe it has to accumulate.And maybe that’s exactly what makes this moment in AI so difficult.In this episode:* Why longevity and familiarity matter when we decide which technology companies to trust* Trust in an AI company vs. trust in an AI answer* Hallucinations, sycophancy, and the danger of confident language* Human-like AI, voice, anthropomorphism, and emotional trust* Why “trust the process” usually shows up during change* AI security and what the Hugging Face incident exposed* Whether public-benefit commitments build trust or simply build presence* Trust between employees, managers, leadership, and the larger organization* Human Middleware and the people quietly holding organizations together* Why being trusted is not proof that you’re right* Delegation, letting go, and trust as a leadership behaviorThe Gravity Audit:Where does trust actually live in your organization?If the person everyone relies on disappeared tomorrow, would the process still work?And if AI replaces part of what that person does, what exactly are you transferring to the machine—the task, the judgment, or the trust? Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

Episode DescriptionWe’ve spent a lot of time discussing Human Middleware: the invisible work people do to keep organizations running. But what happens when that invisible work falls disproportionately on women? And what happens when the people doing it aren’t recognized for their contributions?For this episode of the Organizational Gravity Podcast, Gabe and Kelly join Audrey Johnson and Kay Turner for a personal conversation about women in the workplace, leadership, motherhood, compensation, professional expectations, and AI's growing influence.These women have worked together across multiple organizations and bring years of experience in project management, operations, and organizational change.Rather than simply discussing workplace statistics, they share their own experiences, including the moments that shaped their careers, the obstacles they’ve encountered, and the opportunities they see ahead.Kelly Sheahan — The Hidden Expectations of Being DependableInvisible work and unequal expectationsKelly opens the conversation by describing how women often take on extra responsibilities without being asked whether they belong to them. She recalls an executive requesting a presentation at 4:59 PM, due by 8 AM the next morning.Her question: Why was she the person expected to take care of it?Navigating male-dominated industriesKelly discusses the differences between working in female-dominated and male-dominated industries, particularly the expectations surrounding assertiveness, communication, appearance, and professional behavior.She describes the fine line women often navigate between speaking their minds and being perceived as aggressive or difficult.Motherhood and returning to workKelly shares the challenges of returning from maternity leave, balancing professional responsibilities with motherhood, and keeping pace with rapidly changing technology.She also questions why maternity and paternity leave remain so inconsistent.Pay equity and professional recognitionKelly recounts discovering that a less-experienced employee reporting directly to her earned about the same salary, despite her additional management responsibilities.She questions how organizations evaluate experience, determine compensation, and account for subjective judgments in promotion decisions.AI as an opportunity to do more valuable workKelly sees AI as an opportunity to move beyond repetitive tasks and toward higher-value contributions. She also challenges the assumption that lower recognition of women’s AI use necessarily means women use it less.Audrey Johnson — Making Invisible Work VisibleRecognizing the value of work that happens behind the scenesAudrey challenges the idea that invisible work is somehow less valuable.Much of project management involves connecting people, solving problems, coordinating activities, and keeping work moving.Her central question is how women can make those contributions visible and demonstrate their value.Executive presence versus authenticityAudrey discusses feedback on her executive presence and reconsiders what the phrase actually means.Was she being encouraged to become a stronger leader, or being asked to behave in a way that matched someone else’s expectations?She argues that authenticity matters because maintaining a professional persona that isn’t genuinely yours is ultimately unsustainable.Visibility, promotion, and communicating business valueAudrey reflects on the importance of discussing accomplishments, communicating financial impact, and ensuring leadership understands the value of work being performed.She also acknowledges that promotion decisions often involve visibility and relationships, not simply performance.Motherhood and career decisionsAudrey shares how having a daughter influenced her willingness to travel as a project manager.She describes feeling less visible on her own projects when colleagues traveled more frequently and wondered whether those differences would affect her advancement.AI, learning, and career opportunityAudrey encourages women to become curious about AI, invest time in learning it, and use it to reduce repetitive work.She sees AI proficiency as a potential path toward better roles, greater responsibility, and improved compensation.Finding your voice in the roomAudrey discusses learning when to interrupt, redirect a conversation, and insist on finishing a thought.She argues that women should feel comfortable exercising authority in meetings rather than automatically yielding the floor.Kay Turner — Recognition, Opportunity, and the Cost of Speaking UpWhen other people receive credit for your workKay argues that much of women’s work isn’t invisible. Rather, organizations have become accustomed to expecting women to perform it.She describes how women can contribute substantially while someone else receives the recognition, and how that experience can undermine confidence.Assertiveness and unequal standardsKay reflects on being a direct, outspoken woman in corporate environments and the professional consequences of refusing to back down.She questions whether the same traits that can advance a man’s career sometimes limit a woman’s opportunities.The career opportunities women sacrificeKay shares a pivotal moment when a female executive recognized her potential and offered her a director position that required relocating.She declined because of her responsibilities as a mother and still wonders how her career might have developed had she accepted.A serious accident and unequal compensationOne of the episode’s most difficult stories comes when Kay describes a car accident that left her unable to work for three months.Despite managing a project for 14 months before the accident and returning to complete it, she received only 15% of the project bonus, while the colleague who temporarily replaced her received 85%.Kay discusses the humiliation, frustration, and lasting effects of that decision.Race, bias, and advancementKay shares her experiences navigating corporate America as a Black woman and discusses how personal relationships, unconscious bias, and expectations about who fits a leadership role can affect career progression.She challenges the assumption that promotions are always determined primarily by skill and performance.AI access and opportunityFor Kay, the question isn’t simply whether women are willing to use AI.It’s whether they have the access, exposure, training, and opportunities to develop meaningful capabilities with it.Leadership is a collective effortKay closes with a reminder that productive disagreement is not an attempt to undermine leadership.Teams succeed when people challenge one another constructively, recognize their shared responsibilities, and work toward collective success.The Gravity Beat — What Are We Actually Recognizing?Throughout the discussion, one question continues to surface:If organizations depend on invisible work, why do they struggle to recognize the people performing it?The conversation explores how performance expectations, leadership behaviors, career interruptions, and organizational structures can create additional burdens that aren’t always reflected in job descriptions or compensation.AI creates an interesting possibility. Some of the repetitive and administrative work that has historically occupied employees may become easier to automate.But removing the task doesn’t automatically correct the organizational assumptions that made the work invisible in the first place.And without equitable access to technology and career opportunities, organizations risk carrying those same problems into a new generation of work.A Personal Thank YouGabe closes by reflecting on the women who shaped his life and career.From his grandmother, who taught him to read, to his aunt, whose career helped shape his understanding of business, to Star, whose continued support makes so much of his professional and creative work possible.He also thanks Kelly, Audrey, and Kay for years of collaboration… Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe

AI is supposed to make work easier. So why are we using the time it saves to take on even more?This week, Gabe and Kelly explore an uncomfortable possibility: what if the biggest obstacle to eliminating unnecessary work isn’t the technology, but our own behavior?We revisit Human Middleware, the invisible work people do to keep disconnected systems, processes, and organizations running. But the conversation takes an unexpected turn when Gabe admits something he got wrong in his original thinking: even when technology removes the need for people to stand in the middle, we often choose to put ourselves right back there.We discuss the difference between getting work done and creating value, the instant gratification of AI systems that agree with us, and the growing responsibility of managing teams of AI agents without necessarily having the experience to question their outputs.We also introduce three lenses for understanding Human Middleware: structural, behavioral, and social. Some of that work should disappear. Some may be worth protecting.And perhaps the biggest question: if AI accelerates everything we do, are we also accelerating the organizational and personal habits we should have left behind?In this episode* 01:00 — AI makes us faster, but are we doing less?* 07:12 — The danger of an AI partner that always agrees* 11:04 — Why judgment matters more than producing another PowerPoint* 16:20 — Everyone becomes a founder: managing your own AI agents* 24:14 — The three forms of Human Middleware* 28:10 — Why human relationships and experience still matter* 34:27 — What Gabe got wrong about Human Middleware* 39:26 — Why removing tasks doesn’t necessarily improve work* 40:20 — The tool changed. We didn’t.Join the conversationWhat happens when you eliminate unnecessary work? Do you actually reclaim the time, or do you find something else to fill it?We’d like to hear where you see Human Middleware in your organization, which parts should disappear, and which parts represent something genuinely valuable.Subscribe to Organizational Gravity for more conversations, essays, and practical perspectives on organizational design, technology, AI, and the way we work. Get full access to Organizational Gravity at organizationalgravity.substack.com/subscribe