The Organizational Gravity Podcast

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 Beat

What 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 Audit

The work around the system

Human 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 point

Colette 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 Colette

Connection does not look the same to everyone

We 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

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 behavior

The 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?

Episode Description

We’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 Dependable

Invisible work and unequal expectations

Kelly 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 industries

Kelly 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 work

Kelly 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 recognition

Kelly 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 work

Kelly 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 Visible

Recognizing the value of work that happens behind the scenes

Audrey 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 authenticity

Audrey 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 value

Audrey 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 decisions

Audrey 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 opportunity

Audrey 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 room

Audrey 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 Up

When other people receive credit for your work

Kay 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 standards

Kay 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 sacrifice

Kay 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 compensation

One 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 advancement

Kay 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 opportunity

For 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 effort

Kay 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 You

Gabe 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…

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 conversation

What 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.

Would Your Hiring Process Screen Out Your Best Employee?

A résumé gives us the polished version: the roles, the accomplishments, the software, the results. It rarely tells us what went wrong, what remained unfinished, or how someone developed the judgment they bring to work.

In this episode of Organizational Gravity, Gabe and Kelly explore what gets lost when hiring becomes an exchange between optimized résumés and automated screening systems. They discuss transferable skills, the responsibility of hiring managers to define a role clearly, and the uncomfortable experience of writing a failure CV.

The conversation returns to human middleware—the people who keep work moving between systems, teams, and processes. Before handing their work to an agent, there are questions worth asking: Why was this process built this way? What experience shaped it? And what could this person contribute if they had more time?

In this episode:

* 00:15 — AI résumés, automated screening, and the first human contact

* 04:52 — Who checks which candidates the system rejects?

* 07:08 — Recognizing transferable experience

* 08:46 — When the hiring manager defines the wrong job

* 11:13 — The uncomfortable work of writing a failure CV

* 18:19 — Looking beyond polished accomplishments

* 20:13 — Vulnerability and sharing professional failures

* 24:08 — Human middleware and the judgment behind a process

* 27:49 — Interview credibility and first impressions

* 35:21 — Degrees, gender, and screening bias

* 38:36 — Recognizing skills gained in other industries

* 40:06 — Automation, hiring uncertainty, and time to think

A few clarifications belong alongside this conversation.

Automated screening can exclude qualified candidates, particularly when employers use overly restrictive requirements. That concern is documented in Harvard Business School and Accenture’s Hidden Workers research. But screening systems vary, and a missing keyword does not automatically mean rejection. A lack of response also does not establish that AI rejected someone or that an AI-written résumé would have changed the outcome. Read the research.

Our discussion of body language went further than the evidence supports. Eye direction is not a reliable way to determine whether someone is lying. Looking away, checking notes, or keeping a camera off does not establish dishonesty. A stronger approach is to ask consistent, job-related questions and evaluate the substance of the answers. We also used “imposter syndrome” when discussing possible misrepresentation; those are different things. Eye-direction study, structured-interview guidance.

The Fortune 100 or Fortune 500 did not collectively eliminate college-degree requirements. Some employers removed them for particular roles, and research found that those announcements often produced limited changes in who was actually hired. HBS and Burning Glass report.

Women earned the majority of U.S. bachelor’s and master’s degrees in the 2021–22 data cited here. That does not, by itself, tell us whether women are being hired at higher or lower rates. Research has documented gender and racial bias in particular hiring settings and AI screening experiments, but those findings should not be treated as a description of every employer or tool. NCES undergraduate data, graduate data, AI screening study.

Our comments about women being better multitaskers or project managers reflect personal impressions, not an established general advantage. The multitasking study linked here found no significant gender differences in the tasks it tested. The broader hiring question remains useful: are we recognizing the skills a person has demonstrated, including those developed outside a conventional career path? Read the study.

We also discuss whether uncertainty about AI is holding back hiring. That is a possibility raised in the conversation, not a demonstrated explanation. The August 2026 BLS release, published after the recording, reported hiring little changed over the month and year; it does not establish AI as the cause of hiring trends. BLS report.

A few smaller points: the “8%” reference was not tied to an identifiable source and should not be treated as a verified statistic. Professional résumé writers still exist. The Martha Stewart story is Gabe’s recollection and interpretation, rather than a verified quotation. And a failure CV is presented here as an exercise in reflection, not a tested strategy for getting through an application system. Professional résumé-writing association.

The “un-resume” discussion draws on Jasmine LeFlore’s TEDx talk, It’s Not Only the Resume, It’s also the Un-resume, which explores the experiences and unseen work behind conventional credentials. Watch the talk.

What experience made you better at your work but never made it onto your résumé?