Executives face five pressing questions on AI governance before any board meeting forces the issue. Are we set up properly inside the organization? Who is responsible for AI governance? Who is accountable when something goes wrong? How are risks being assessed? Could anyone describe this to the board clearly? Most teams cannot. Grant Thornton's 2026 AI Impact Survey showed 78% of executives lacked strong confidence they could pass an independent AI governance audit within 90 days.

Most executive teams can tell you how many people in their organization have used a generative AI tool this month such as Claude, Copilot or ChatGPT. But only few know who is accountable in the event one of those people entered confidential client data into it.

And that is where the problem lies.

AI adoption has out-paced AI governance almost everywhere. IBM asked 2,000 technology executives about this in June. Seventy-seven percent of executives acknowledged that AI adoption is outpacing current governance capabilities.

Tools spread quickly because everyone wants to measure efficiency & productivity gains. The hype is real. Governance was considered to be something that would slow things down, so oversight was delayed.

The boards are catching up and they want answers. Deloitte's Global Boardroom Program polled nearly 700 board members and executives across 56 countries. The share of boards where AI isn't on the agenda at all dropped to 31%, down from 45%. But two-thirds still describe their own AI knowledge as limited or non-existent. So the questions are coming from people who can't evaluate the answers yet. So how will they know what good looks like?

Here are the five questions I think every executive should be prepared to provide their board a real answer to.

The Executive AI Governance Readiness Check

1. Readiness. Is the data, the policy, and the shared understanding actually there?

2. Ownership. Who holds this, by name, with a budget?

3. Accountability. Who answers when it goes wrong?

4. Risk. Are we looking past data privacy?

5. Board readiness. Could we explain any of this today, specifically?

1. Are we organizationally ready for AI?

This isn’t a question about tools, but about organizational maturity.

What does AI readiness look like? Your data is clean enough to trust, your policies exist somewhere other than a wiki page nobody reads, and your people know the difference between being allowed to use AI and what specific scenarios they are allowed to use AI for. Most companies have none of that in place. They rollout the AI tool and maybe hold a prompt hackathon to create hype in the hopes that will result in an AI-ready team.

The further a company is from real deployment, the worse this gets. In Grant Thornton’s survey, only 7% of organizations still piloting AI were very confident they could pass an independent governance audit within 90 days. In organizations where AI was fully integrated, 74% expressed confidence. Only a fifth of companies had a tested plan for what to do when AI fails. This is astounding for a technology now sitting inside client deliverables and hiring decisions.

The honest test: pull ten employees from different departments and ask them what they’re allowed to put into an AI tool. If you get ten different answers, then your organization is not ready. You’ve just adopted another tool that will serve as a very expensive email writer.

2. Who owns AI governance?

Ask this question in most companies and you’ll get silence, looks of confusion, a random guess, or all of the above.

IT will say it’s a legal issue. Legal will say it’s a business issue. The business unit says someone above them must be handling it. The reality is that there is no real owner. And governance without an owner is a recipe for disaster.

So here’s my answer. One named executive, sitting at C-suite level, chairing a cross-functional council that includes legal, IT, risk, and the business units actually deploying the tools. The council builds consensus. The named executive carries the decision. Title matters less than authority: this works with a Chief AI Officer, a COO, or a General Counsel.

Most companies currently show oversight without an owner. Thomson Reuters Institute data indicated 44% of firms publicly claim an AI strategy and 40% report board or committee oversight, yet fewer than a third maintain a dedicated team or resources beneath it. IBM found that two-thirds of technology leaders say they’re being held accountable for AI systems they don’t fully control.

That is not proper governance, and it exposes one person to bearing the full responsibility on their shoulders.

3. Where does accountability sit?

Ownership and accountability sound like the same question. They are not.

Ownership is about who runs the governance program. Accountability is about who answers for what happens when AI gets something wrong, whether through biased hiring suggestions, fabricated figures in client reports or unchecked decisions.

None of these are hypothetical cases anymore. Organizations in IBM’s 2026 study reported an average of 54 AI agent incidents over a single year, each requiring human intervention. Seventeen percent were high severity and took more than 4 hours to contain. Of those, 37% led to data exposure or a security breach, and 17% raised compliance problems.

Fifty four incidents yearly equals roughly one per week. Someone needs to answer for each incident, or no one does.

If an AI system produces a bad outcome tomorrow, can you point to the person who signed off on the process that let it happen? If the honest answer is "it depends" or "we'd have to look into it", that's your answer right there.

4. How are we assessing AI risk?

Most risk conversations about AI are really just conversations about data privacy. That’s one risk among several, and treating it as the whole picture is how the other ones go unnoticed.

The fuller list includes:

• Model risk. Is the output actually reliable?

• Operational risk. What happens when the system fails silently instead of loudly?

• Reputational risk. What does this look like publicly if it goes wrong?

• Regulatory risk. Are you positioned for rules that don’t exist yet but are clearly coming?

If you’re building this from scratch, the NIST AI Risk Management Framework and ISO/IEC 42001 are where most people begin, and the EU AI Act sets the outer boundary of what regulators will eventually want to see.

5. Could we confidently explain our approach to the board?

This is the question that actually matters, because it’s the one that gets asked whether you’re ready or not.

When a board member inquires specifically how AI risk is managed, replies such as “we have encouraged teams to experiment” fall short.

“Here’s our governance structure, here’s who owns it, here’s how we assess risk, here’s what we’ve caught and fixed” is the answer that keeps a board comfortable and keeps a leadership team credible.

And the exposure behind the question is now financial. Article 99 of the EU AI Act sets fines of up to €35 million or 7% of worldwide annual turnover for prohibited AI practices, and up to €15 million or 3% for breaches of most other obligations, including the high-risk system requirements. Those have been enforceable since August 2025. Your board has read those numbers too, or someone has read them to your board.

If you can’t say that with confidence right now, that tells you exactly where the work is.

Organizations that settle these questions early will govern AI more effectively than they adopt it. And governance is turning into one of the central leadership skills of the decade.

Every organization has two structures running at once. One is on the slide: reporting lines, titles, dotted-line relationships. The other is the one that actually decides things, and it rarely matches the first.

You can tell the difference by watching what happens before a meeting, not during it. The real decision is usually made in a hallway conversation, a text thread, or a five-minute call two days earlier. What happens in the room is often just the room finding out. If you’ve ever walked into a “discussion” that was clearly already settled, you’ve seen this firsthand. And the uncomfortable part is that nobody tells you when it happens. You have to notice.

Here’s what I look for now, and what I wish someone had handed me a decade earlier:

Who gets asked for their read before the decision, not after. Formal authority tells you who signs off. It doesn’t tell you whose objection actually stops something. Those are frequently different people, and the second group is the one worth mapping.

Who gets copied on the thread that matters, not the one that’s performative. Every initiative has a real distribution list and a decorative one. Learn to tell them apart quickly. It’s usually obvious once you know to look.

Whose “no” is expensive to override. Some people can be outvoted cheaply. Others can’t, regardless of title, because overriding them costs political capital nobody wants to spend. That’s real power, and it’s almost never printed on a business card.

None of this is about becoming cynical or transactional. It’s about accuracy. Reading the room correctly is a technical skill, the same as reading a balance sheet. Much like financial fluency, most of us were never actually taught it. We picked it up slowly, expensively, usually after getting it wrong at least once in a meeting that mattered.

Where AI actually helps here

This isn’t a place AI replaces your judgment. Reading a room is still a human skill. But it’s a genuinely useful way to pressure-test your read before you walk in, especially when you’re new to a room or the stakes are high enough that a blind spot would be expensive.

Try this prompt with whatever generative AI tool you use. It’s built around CLEAR, the framework used in Her Executive Ascent’s leadership programs: Context, Length (of response), Examples, Actions, Role.

Context: I’m walking into a meeting about [decision or initiative]. Here is a list of who’s in the room, by role and level only, no names: [list roles and levels, plus what you know about each one’s incentives, history with this topic, or recent friction].

Length: Three short sections, one per action above, no more than two sentences each.

Examples: A blocker might look like someone who wasn’t consulted and has quietly pushed back on similar initiatives before. A surprise objection might come from a function that seems unrelated but has exposure I’m not accounting for.

Actions: Identify who is likely to support this and why, who has enough informal influence to slow or block it even without formal authority, and what objection is most likely to come from a direction I’m not expecting.

Role: Act as an experienced organizational strategist helping me prepare for a meeting. Ask me clarifying questions, one at a time, until you have enough information to provide a practical response.

Every line does one job:

• Context hands over the situation without a single name attached,

• Length of response tells it exactly how much to hand back so you’re not wading through paragraphs to find the one useful line,

• Examples anchor what “blocker” and “surprise objection” actually mean instead of leaving the model to guess,

• Actions is the specific ask, and

• Role sets the lens it’s answering from.

The value isn’t the output on the first pass. It’s that a good model will surface something you hadn’t thought to check yourself. Use it to catch what your own position in the room might be hiding from you, not as a replacement for your own read. And role and level only is the right call regardless of the tool. There’s no version of this exercise that requires the AI to know who anyone actually is. Never share identifying, private, or confidential information with any AI tool. This is the exact kind of gap AI Fluent: Foundations for Executive Women was built to close.

P.S. It’s a self-paced course with lifetime access, and you’ll practice the framework directly with an AI Foundations Coach built for this course, not just read about it.

In this episode of Leadership Recalibrated™, Dinah traces her path from the bleeding edge of mobile security to founding Code Like a Girl, and talks candidly about the parts of corporate leadership she never got good at, and why she's not sorry about it.

This is a conversation about the standards that get invented after the fact, the cost of caring too much in a system that doesn't reward it, and what happens when you stop trying to win a game you never agreed to play.

About Our Guest:

Dinah Davis spent 20 years in tech, from writing cryptographic protocols at BlackBerry to leading engineering teams as a VP, most recently at Arctic Wolf. She holds a master's in cryptography and multiple patents. In 2016, she founded Code Like a Girl, a publication amplifying women's voices in tech, which she now runs full-time. She writes about careers, leadership, and the parts of the industry nobody puts in the recruiting brochure.

Connect with Dinah: https://www.linkedin.com/in/dinahdavis/

Subscribe to Code Like A Girl: https://substack.com/@codelikeagirl

Subscribe to She Writes AI: https://substack.com/@shewritesai

• Responsible and Ethical AI Use — Where AI governance actually breaks down (fairness, transparency, accountability) and what to ask before it does.

• The AI Strategy Blueprint — A full blueprint for where AI creates value, what capabilities it requires, and how to phase the rollout.

Read more

• The Value-Fit-Risk Workbook — The working version of VFR — score a live AI decision instead of just reading the theory.

• The AI Decision Matrix and Due Diligence Framework — Compare build, acquire, or partner options, and run technical, business, and strategic due diligence before committing.

• The Executive AI Messaging Matrix — Draft tailored AI messaging …

Read more

• The CLEAR Framework for AI Prompts — A five-part structure (Context, Length, Examples, Action, Role) for writing prompts that get useful answers instead of generic ones.

• The Value-Fit-Risk Framework — How to evaluate any AI initiative on real value, organizational fit, and actual risk — read this before the workbook below.

Read more

Lisa R. Triggs did that too. For years.

In this episode of Leadership Recalibrated™, I sit down with Lisa, known as The Menopause Queen, who spent twenty years navigating every stage of menopause — from perimenopause at 38 to full menopause at 47 — experiencing a vast array of symptoms including depression, insomnia, extreme fatigue, and a disconnection from herself so deep that she reached the lowest point of her life. What she found in that darkness, and what she built her way back with, is the reason she is here today.

This is not a conversation about managing symptoms. It is a conversation about what happens when you stop letting menopause control you and start making choices that change everything. Lisa managed her journey using visualization, meditation, journaling, and a mindset shift that she now teaches through her bestselling book, The Menopause Mindshift: How I Unleashed My Inner Queen, And You Can Too, and through her coaching practice, podcast, and speaking work.

What she has to say is going to reframe the way you think about this chapter of life entirely.

We get into why the story we have been told about menopause is the thing making it harder, how the hormonal changes of midlife bring buried things back to the surface and what to do when that happens, why the strength that women in leadership wear as armor is often a mask for the vulnerability underneath, what it looks like to grieve who you used to be while building someone even better, and the one rule Lisa wants retired from the old playbook about what women are capable of at this stage of life.

This one is for every woman who has ever felt like she was disappearing inside her own body and needed someone to tell her that the other side of this is worth fighting for.

About Our Guest

The Menopause Queen, Lisa R. Triggs is a certified menopause coach, motivational speaker, best selling author and podcast host.  With a deep love for personal growth, Lisa has spent nearly 20 years navigating the rollercoaster of menopause; an experience that ultimately transformed her life.

In her bestselling book, The Menopause Mindshift: How I Unleashed My Inner Queen, And You Can Too!, Lisa shares her story to inspire other women to take back control, prioritize their well-being, and step into their power.

Today, she stands as a voice of hope, showing women that menopause doesn’t have to be a struggle—it can be a powerful awakening. Her mission is to help others awaken their inner Queen, embrace self-love, and redefine what it means to thrive in this life phase.

Connect with Lisa

Website: https://themenopausemindshift.com

LinkedIn: https://www.linkedin.com/in/lisartriggs

Buy her book: https://www.amazon.ca/Menopause-Mindshift-Unleashed-Inner-Queen-ebook/dp/B0DJG47FS8

The numbers are sobering. 86% of jobs most exposed to AI automation are held by women. Women make up less than 25% of the workforce actually building AI. And the World Economic Forum estimates it will take 123 years to close the gender parity gap worldwide. In 2026, women are still making 83 to 86 cents for every dollar a man earns for the same work.

But today's guest is not here to talk about the problem. She is here to talk about what we do about it.

In this episode of Leadership Recalibrated™, I sit down with Miri Rodriguez, Founder and CEO of Empressa.ai, the groundbreaking AI platform built on women's lived experiences, trained on women's knowledge, and designed to close the very gaps the technology industry has spent decades widening. After 13 years at Microsoft, where she became one of the company's most recognized storytellers and published the award-winning bestseller Brand Storytelling, Miri left one of the most powerful platforms in the world to build something the industry had never seen before.

We get into why AI is either the most powerful tool women have ever had access to or the fastest way to permanently hard-code today's gaps into tomorrow's opportunities at scale, what it actually means for an AI to be built on women's lived experiences and why that changes everything about how it responds, the Empressa Imperial Council and the model that pays women royalties for the knowledge they contribute, how to start closing the gap today, and the rule Miri wants every woman to rewrite before she walks into her next room.

This one will fire you up. And it is supposed to.

About Our Guest

Miri Rodriguez is the Founder and CEO of Empressa.ai, a groundbreaking AI company reshaping the future of work for women. With 13 years at Microsoft, where she evolved from communications and marketing into one of the company's most recognized storytellers, Miri built and led work that changes how organizations connect with people. That body of work became her bestselling book, Brand Storytelling, published by Kogan Page, translated into four languages, and recognized with multiple awards worldwide.

Now she is channeling everything she built at Microsoft into something the market has never seen before. Empressa is an AI platform built on women's lived experiences, trained on women's knowledge, and designed to close the very gaps the technology industry has spent decades widening.

She is also the author of the book The Women of Microsoft, an advisor to Ragan's Center for AI Strategy, and one of the clearest, most urgent voices on what it is going to take for women to stop adapting to systems that were never built for them and start building the ones that are.

Connect with Miri

LinkedIn: https://linkedin.com/mirirod

Website: https://empressa.ai

That gap has a name. And it has a cost.

In this episode of Leadership Recalibrated™, I sit down with Heather Gawel, founder of Momentum Product Co. and former VP of Product and Design, to talk about what AI adoption actually looks like inside organizations right now versus how leadership is talking about it. Heather has spent the better part of two decades leading product teams managing portfolios of over $100 million and earlier this year left the corporate seat to go deeper — building an enablement practice specifically around the AI gap she kept running into everywhere she turned.

What she is seeing is not what most leaders think is happening on their teams.

There is a bell curve playing out inside most organizations right now. At the top, urgency is high. In the middle, leadership is building slide decks and attending demos. And at the IC level, people are on their own time trying to piece together a skill set their organization demanded but never equipped them for.

This is a conversation for anyone in leadership who has announced an AI strategy without building the conditions for their people to actually execute on it. And for anyone on the ground who is tired of being told to learn AI without being given the time or support to do it.

About Our Guest

Heather Gawel is the founder of Momentum Product Co. and a practitioner at the intersection of AI, product, and execution. She started her career in mechanical engineering, moved into product management, and spent the better part of two decades leading product teams across hardware, software, and B2B SaaS — most recently as VP of Product and Design at a San Francisco-based scale-up managing a portfolio of over $100 million.

Earlier this year Heather made the move from the corporate seat to consulting, co-founding Momentum Product Co. to fill the AI enablement gap she kept running into — first inside her own organizations and then in every discovery call she took when launching the business. Through Momentum, she runs hands-on AI workshops and enablement sessions for product managers, founders, and cross-functional teams across Waterloo, Toronto, and the US.

What makes her perspective different is straightforward — these are not frameworks she read about. These are workflows she uses every day. She is passionate about creating on-ramps for people who want to build with AI, especially women who are new to the AI community.

Connect with Heather:

Website: momentumproductco.com

LinkedIn: linkedin.com/in/heathergawel

Build With AI: Tech Worker Edition

Heather is running an in-person, hands-on Build with AI workshop in partnership with Communitech on Thursday June 18th in Kitchener, Ontario.

• Register here: luma.com/08m5ihzd

• Use promo code RECALIBRATED at checkout to get $50 off of registration.

In this episode of Leadership Recalibrated™, I sit down with Supriya Verma, Founder of Sustainability X Magazine and author of the newly released book Bold Women, Sustainable Futures: Leadership Lessons in an Age of Resistance. Supriya has spent a decade at the intersection of sustainability, women's leadership, and systems change — building a platform that brings together the conversations most people keep in separate rooms, and shining a light on the women who are leading through some of the most complex and consequential challenges of our time.

This book arrived at exactly the right moment. As DEI commitments are being rolled back, climate accountability is being deprioritized, and a generation of young women is entering a workforce that feels more hostile than the one that came before it, Supriya chose not to be quiet about it. And that decision changed everything about what this book became.

This is a conversation for anyone who has felt defeated by how quickly progress can be reversed. And for anyone who needs a reminder that the women who came before us faced resistance too and kept going anyway.

About Our Guest

Supriya Verma is the Founder of Sustainability X Magazine, a platform she launched in 2016 to bring together environment, economy, society, and politics through dialogue and storytelling. What began as a digital sustainability magazine evolved into a broader space for visibility and recognition, most notably through the Global 50 Women in Sustainability Awards, now celebrating its fifth annual cycle as Sustainability X marks its 10th anniversary.

Over a decade of listening to extraordinary women across climate, governance, finance, advocacy, science, policy, and social justice, Supriya began to see patterns that demanded more than celebration. They demanded documentation. The result is her debut book, Bold Women, Sustainable Futures: Leadership Lessons in an Age of Resistance, published in March 2026 during Women's Month and available worldwide wherever books are sold.

She is one of the most urgent and necessary voices on what bold, sustainable, and human-centered leadership actually looks like in a world that is actively resisting it.

Get Involved:

• The SustainabilityX® Magazine - sustainabilityx.co

• Buy the book on Amazon, Indigo, Friesenpress & 50,000+ global retailers.

• Subscribe to the newsletter for book and tour updates

For the past two years, the headlines have been relentless: Women are slower to adopt AI. Women are falling behind. Women are resistant to change.

What those headlines missed, and what a recent study from Chief x The Harris Poll makes clear, is the difference between chasing a trend and actually leading one.

Because here’s what was really happening while those headlines ran: AI was being thrust onto workforces everywhere with little training, no strategic roadmap, and almost no vision for what responsible implementation actually looks like. Companies moved fast leaving their people behind. And we’re already seeing the consequences.

Three numbers that reframe everything:

• 80% of senior women leaders are actively shaping AI strategy at their organizations

• 83% say caution in AI adoption is a sign of good leadership — not resistance to change

• 87% have already witnessed the fallout of companies that went “AI only” and left their people underutilized

“Women are making sure the humans keeping pace with AI don’t get left behind in the process.”

— Alison Moore, CEO of Chief

This is what real strategic leadership looks like. Not the loudest voice in the room rushing to implement the newest tool. The steady, clear-eyed leader asking: what does this mean for our people, our culture, and our long-term business outcomes?

To the women leading the way in your organizations — you were never behind. You were building the framework everyone else will eventually need.

I shared this with the women in the Her Executive Ascent cohort this week because it’s a power move worth naming out loud. When the narrative shifts, we shift with it — and right now, the data is finally catching up to what we already knew.

The reflection question for this week:

Where in your organization do you have influence over how AI is being introduced? You don’t have to have “AI” in your title to shape this. You just have to show up with intention.

Read the full story here → Fast Company — “New study finds 80% of women leaders are shaping AI strategy”

TL;DR: The Canadian government has known about AI-generated non-consensual sexual deepfakes, including of minors, for months. I’ve written multiple government offices, asked eight specific questions, and received zero substantive answers. Grok, X’s AI chatbot, generates up to 6,700 non-consensual sexual images per hour. Canada banned Huawei because it potentially posed a risk. There is no explanation for why the threshold is different when the technology is causing harm everyday where victims include women and children instead of corporate infrastructure. I’m not anti-AI. I run a business that depends on it.

I’m asking for what every other industry already has: regulation that exists before the harm scales, not after.

The ask is simple: answer the questions, commit to a date, build the framework.

Since I started writing to our government officials, the situation has gotten worse, not better.

A Canadian man walked free because the law doesn’t cover deepfakes.

In March 2026, a Nova Scotia man who used AI to create nude deepfake images of five of his high school classmates was acquitted of intimate images charges. Not because what he did wasn’t harmful. Because the Criminal Code didn’t cover it. The presiding judge wrote in her decision: “The law does not reflect the technology that exists today... It is not within the proper jurisdiction of this Court to extend the reach of the legislation.”

Justice Minister Fraser called deepfake nudes “heinous” in response to the acquittal. Bill C-16, the legislation that would have prevented it, was still sitting in committee.

CBC News — N.S. man acquitted of intimate images charges, March 11, 2026

CBC News — Federal justice minister calls deepfake nudes ‘heinous,’ March 16, 2026

This is not a hypothetical failure. It happened. A judge was legally obligated to acquit a man who made nude images of five women without their knowledge or consent because Parliament had not done its job. Those five women are living with that outcome right now.

Dozens more Canadian women were victimized just days ago.

As of May 29, CBC News reported that multiple women across Canada had their social media photos taken and manipulated by AI into violent and sexual deepfakes. Ottawa police have charged two men following a multi-jurisdictional investigation. The women described feeling confused, violated, and terrified.

CBC News — AI deepfakes of dozens of Canadian women in violent and sexual images shared online, May 29, 2026

The law that would make this unambiguously criminal has not yet passed. Those women had no clear legal recourse at the moment the harm happened.

Grok is still generating nonconsensual sexual images despite a pledge to stop.

In January 2026, X announced it would no longer allow users to edit images of real people into revealing clothing. In April 2026, NBC News confirmed Grok was still generating nonconsensual sexualized images of real people, with users finding prompt techniques to bypass the restrictions.

NBC News — Musk’s Grok AI chatbot is still making sexual deepfakes, despite X’s promise to stop it, April 14, 2026

This is what platform self-regulation looks like in practice. A pledge, a bypass, and the harm continues. This is precisely why I have argued — across four letters and six government offices — that the only effective intervention point is upstream regulatory authority, not voluntary corporate commitments that dissolve under user pressure.

Parliament amended Bill C-16 but it still hasn’t passed, and the Globe says the delay is unconscionable.

On May 11, the same day Minister Solomon was in Vancouver announcing a data centre partnership, a House of Commons committee amended Bill C-16 to cover “nearly nude” images, directly in response to the Grok controversy. The amendment also requires platforms to remove reported images within 48 hours. These are meaningful improvements.

But the bill has not passed. It still needs to clear the Senate. There is no guaranteed timeline. And the Globe and Mail’s editorial board published a piece last week calling on Ottawa to move urgently, noting that real Canadians are being acquitted and real women are being harmed while Parliament deliberates.

Global News — Bill to criminalize AI sexual deepfakes will include ‘nearly nude’ images, May 11, 2026

The Globe and Mail — Ottawa must move urgently to ban nude deepfakes, May 27, 2026

Alberta is moving provincially because the federal government hasn’t.

In March 2026, Alberta announced it was changing provincial law to allow victims of deepfakes to sue for damages. A province is filling the gap because Parliament has not.

CBC News — Alberta to change legislation allowing lawsuits over sharing deepfake intimate images, March 24, 2026

This is what regulatory failure looks like at scale: a patchwork of provincial civil remedies substituting for the federal criminal and regulatory framework that should exist and doesn’t.

The Privacy Commissioner launched an investigation, but it isn’t binding.

In January 2026, Canada’s Privacy Commissioner expanded his investigation into X Corp. to assess whether Grok obtained valid consent from Canadians for the use of their personal information to create deepfakes.

Office of the Privacy Commissioner — Privacy Commissioner expands investigation into X, January 15, 2026

An investigation that may conclude in two years is not a regulatory response to harm happening at 6,700 images per hour. It is the appearance of oversight in the absence of the infrastructure to actually provide it. This is exactly the gap I have described across every letter I have sent: Canada has no body with the mandate and tools to respond in real time. The Privacy Commissioner’s investigation is evidence of that gap, not a solution to it.

What the courts have said, since the government won’t.

The Nova Scotia judge who acquitted the man who made nude deepfakes of five women wrote this in her decision:

“The statute must be revised to respond to such concerns.”

That is a judge telling Parliament to do its job. The Globe and Mail’s editorial board echoed it last week. Legal experts at Dalhousie University have said Canada is “far behind.” Two men have now been criminally charged in connection with deepfake abuse of dozens of Canadian women. A bill that would help is stalled in the Senate with no guaranteed timeline.

The government has had every signal it needs. The question that remains — the one that has gone unanswered across four months and six offices — is whether it will act before the next acquittal, the next victim, the next case where a judge is legally obligated to let someone walk free because Parliament chose not to keep pace with the technology it is simultaneously investing $926 million to build.

The Globe and Mail — Federal move to criminalize non-consensual sexual deepfakes is long overdue, experts say

Let me start by telling you who I am, because I suspect the government has found it convenient not to ask.

I am not a technophobe. I am not anti-AI. I use artificial intelligence tools every day to run my business. I believe Canada’s investment in sovereign AI infrastructure is strategically important. I support innovation.

I’m writing this because I understand what AI can do and because that understanding makes the government’s failure to build regulatory guardrails around it not just frustrating, but dangerous.

What I’m asking for is what every responsible industry already has: innovation with regulation. The two are not opposites. They are, in every sector that functions well — pharmaceuticals, aviation, financial services, food safety — inseparable. AI is the first transformative technology in a generation to be deployed at massive scale without that framework. And Canadians are paying the price.

The current example: Grok

My letters to the government have focused on Grok, X’s AI chatbot, because it represents a documented, ongoing, measurable harm.

But here is what I need you to understand:

Grok is not the issue. Grok is the current example of the issue.

The actual problem is structural. Canada has no regulatory framework that can respond effectively when any AI system — from any company, of any political affiliation — pivots toward configurations that enable serious harm. An AI system that today operates within acceptable parameters can change its content policies, update its safety filters, or be acquired by new ownership overnight. Without a standing regulatory body with authority to act, there is no mechanism to respond until after harm has already scaled.

Non-consensual sexual imagery is the harm that has surfaced first and most visibly, but it is not the only harm AI systems can enable at scale. The absence of a regulatory framework is a vulnerability across the entire threat surface: fraud, manipulation, surveillance, radicalization, child exploitation. Every month without that framework is another month in which any AI system, from any source, can cause harm at scale with no meaningful regulatory response available.

We’ve seen this movie before

What is happening with AI is not unprecedented. It is the continuation of a pattern that Canadian governments have allowed to persist for two decades.

Social media platforms scaled to billions of users between 2006 and 2016. During that decade, governments around the world watched as these platforms enabled: mass harassment of women and minorities, the radicalization of young men through algorithmic amplification, foreign interference in democratic elections, the spread of health misinformation that cost lives, the exploitation of children through unmoderated content, and the systematic extraction of personal data without meaningful consent.

Canada’s response to twenty years of documented social media harm? We still do not have comprehensive social media regulation. Bill C-63, which would have established baseline platform accountability standards, died twice under Liberal governments before it could pass.

The costs of regulatory lag are not distributed equally. They are borne disproportionately by the people with the least power to protect themselves: women, children, minorities, the elderly, the digitally isolated. Canada's failure to regulate social media for twenty years did not harm Mark Zuckerberg. It harmed the women who were harassed off platforms, the teenagers whose data was sold, and the children who were exploited. AI will follow the same logic.

Four months. Six offices. Zero answers.

Here is the complete record of my correspondence with the Government of Canada.

• Jan 14, 2026: My first email to Minister Solomon raising Grok deepfake concerns.

• Jan 15, 2026: Minister Solomon called deepfakes “violence,” cited Bill C-16, said new legislation “is coming.” No timeline.

• Jan 24–25, 2026: My second email, a list of policy questions, international comparisons, legal expert evidence that C-16 is inadequate.

• Feb 2026: PM Office (Executive Correspondence Officer) acknowledged receipt. Said PM “will leave your remarks to be considered by the Ministers”.

• Feb 2026: Minister Miller’s office (Dept. of Canadian Heritage with no AI mandate) described sexual imagery of minors as a “disinformation” issue. Directed me to the Democratic Institutions website.

• May 5, 2026: Minister Fraser cited Bill C-16 (inadequate per legal experts). Forwarded to Minister Miller, who had already replied in February.

One response mischaracterized non-consensual sexual imagery of minors as a “disinformation” problem. This is either a failure of correspondence management or a deliberate recategorization of the harm. Either explanation is unacceptable.

30 days for data centres. 20 years for citizen protection.

On January 15, the same day Minister Solomon replied to my first email calling deepfake abuse “violence”, the government launched a call for proposals for sovereign AI data centres. Submission deadline: 30 days.

By May 11, Minister Solomon was at a press event in Vancouver announcing a data centre cluster partnership with Telus. The same minister. Months, not years. The government is spending $926 million over five years to build Canada’s AI capacity.

The answer, I suspect, is not one of capacity. It is one of priority. And the priority being revealed by this record is that building Canada’s AI economy takes precedence over protecting Canadians from AI harm. That is a choice. It should be named as one, and Canadians should be able to evaluate it.

Eight questions. Asked four times. Still unanswered.

I’ll reproduce them here, for the public record:

• Canada banned TikTok and Huawei on precautionary grounds. Grok is generating up to 6,700 non-consensual sexualized images per hour, including of minors, right now. What is the principled distinction?

• Is concern about Elon Musk’s political or economic retaliation influencing Canada’s refusal to act? If not, what specific legal barrier prevents Canada from taking the same action asv Indonesia, Malaysia, and the Philippines?

• Legal experts confirm Bill C-16 will not address most Grok-generated images. Bill C-63 has died twice. What is Canada’s actual plan to prevent, not merely criminalize after the fact, the creation of non-consensual sexual deepfakes?

• Minister Solomon called deepfake sexual abuse “violence” and simultaneously stated Canada is not considering a ban on X. How does the government reconcile these positions?

• What volume of documented harm would be sufficient to trigger immediate government action? If 6,700 non-consensual sexualized images per hour — including of minors — is insufficient, what is?

• Will the government commit to a specific date for introducing new AI and online harms legislation? Not “we are working on it.” A date.

• The government continues to actively use X, a platform it acknowledges enables “violence”, while Liberal MP Will Greaves has suspended his account calling it “sexual exploitation”. Why has the government as a whole not done the same?

• Grok is the current example, not the whole problem. What is Canada’s plan for the next AI system, from any company, that pivots to enabling serious harm? What regulatory body has the authority to respond in real time?

What I’m actually asking Canada to build

Criticizing inaction is necessary but insufficient. Here is what responsible AI governance actually looks like, not as a departure from innovation, but as the foundation without which innovation becomes a liability.

A standing AI regulatory body with real-time authority. Not a commission that produces reports. A body with the mandate to respond when an AI system causes harm at scale — to require changes, impose penalties, and if necessary restrict access — within days, not years.

Platform liability that reflects what AI actually is. When a platform’s own AI system generates non-consensual sexual imagery, the platform is not a distributor. It is the producer. Liability frameworks need to reflect this.

Mandatory safety standards before deployment, not remediation after harm. Aviation does not wait for crashes to set safety standards. The current approach to AI — deploy, watch for harm, legislate after the fact — is a choice to allow the first wave of victims to serve as the proof of concept for regulation.

Timelines that match the pace of technology. A regulatory approach that allows legislation to die twice across multiple parliaments, with no binding consequence and no emergency fallback, is structurally incapable of governing a sector that iterates monthly.

Prime Minister Carney said at Davos it is time to take the sign out of the window. To stop performing values and start enacting them.

Canada has a sign in the window that says it protects citizens from technology-enabled harm. Twenty years of social media regulation that never materialized. Online harms legislation that has died twice. Eight questions across four months and six government offices that have not received a single direct answer. A 30-day timeline for data centres and no timeline for citizen protection.

The women and children of Canada, and every Canadian who depends on a digital economy that is both innovative and safe, are asking you to mean it, or take it down.

Postscript: the data centres

While Parliament deliberates over whether to protect Canadians from AI-generated sexual violence, hyperscale AI data centres are being planned and built across Canada with no regulatory framework governing their community impact, their environmental footprint, their energy consumption, or the say that residents have in whether they are built at all.

A single facility exceeding 100 megawatts — the minimum threshold for the federal Sovereign Compute Infrastructure Program — draws more electricity than approximately 80,000 Canadian homes. These facilities require massive water cooling systems, generate sustained noise, and place significant pressure on local power grids and municipal infrastructure. The communities they are going into have no formal mechanism to weigh in, no guaranteed environmental review process calibrated to this scale, and no regulatory body whose mandate includes cumulative impact when multiple hyperscale facilities land in the same region.

The federal government is subsidizing this buildout. It is not regulating it.

Government of Canada — AI Sovereign Compute Infrastructure Program

There is a version of this investment that includes mandatory community consultation before site approval, environmental and grid impact assessments, water use reporting, and binding community benefit agreements so that the municipalities hosting this infrastructure share in its returns. None of that framework exists. The money is flowing anyway.

I support Canada’s investment in AI infrastructure. But economic development and community protection are not supposed to be mutually exclusive. And right now, one is moving at thirty-day procurement speed while the other has no timeline at all.

The pattern is the same. The costs, as always, will not be distributed equally.

Sources

January 15, 2026 — Call for Proposals: Large-Scale Sovereign AI Data Centres The official ISED page (now closed for submissions): https://ised-isde.canada.ca/site/ised/en/enabling-large-scale-sovereign-ai-data-centres

February 15, 2026 — Proposals Close / Engagement Begins Same ISED page above confirms the January 15–February 15 window and that the government “is engaging with proponents of promising projects.”

April 14, 2026 - Musk’s Grok AI chatbot is still making sexual deepfakes, despite X’s promise to stop it

https://www.nbcnews.com/tech/tech-news/musks-ai-chatbot-grok-xai-making-sexual-deepfakes-imagine-rcna265855

April 15, 2026 — AI Sovereign Compute Infrastructure Program (SCIP) Launched Official Government of Canada press release: https://www.canada.ca/en/innovation-science-economic-development/news/2026/04/canada-launches-national-initiative-to-build-large-scale-ai-supercomputing-capacity.html

Official SCIP program page (applications open, ~$890 million available): https://ised-isde.canada.ca/site/ised/en/ai-sovereign-compute-infrastructure-program

May 11, 2026 — Minister Solomon Announces Telus Data Centre Partnership (Vancouver/Kamloops) Official Government of Canada press release: https://www.canada.ca/en/innovation-science-economic-development/news/2026/05/government-of-canada-and-telus-advance-work-to-build-sovereign-ai-infrastructure.html

CBC News coverage (includes video of Solomon at the press event): https://www.cbc.ca/news/canada/british-columbia/b-c-ai-data-centre-plan-vancouver-kamloops-9.7195426

That is not a dramatic oversimplification. That is what Jessica Caresse White walks into every single time.

In this episode of Leadership Recalibrated™, I sit down with Jessica, CEO & Managing Director of her own transformation consulting practice and a woman who built her career inside some of the most demanding organizations in the world — starting with McKinsey, where she learned what it means to be obsessive about the details everyone else dismisses, and continuing through Cisco, Hitachi, and beyond.

What makes Jessica different from every other consultant in the room is that she has never been interested in transforming the business alone. She is in the business of transforming hearts and minds. And she has spent her entire career proving that you cannot have one without the other.

Jessica does not sugarcoat what transformation actually requires — the people decisions, the AI reality, and the corporate habits that quietly sabotage even the best-laid plans. She has seen it all from the inside and she is not holding back.

This is a conversation for anyone leading a team through change right now who suspects the problem is not the roadmap. It is the people. And what happens when no one is paying attention to them.

About Our Guest

Jessica Caresse is a visionary leader, transformational coach, and the driving force behind J.Caresse & Company, a consulting firm that not only transforms businesses but also transforms hearts & minds.

With a wealth of experience across industries, Jessica has built a career on the principles of integrity, innovation, and unwavering determination.

Her work at industry giants like Cisco Systems, Hitachi Solutions, and McKinsey & Company was marked by her ability to turn challenges into opportunities and lead teams through complex, high-stakes projects.

She is a master at streamlining operations, driving strategic change, and creating environments where both businesses and people can thrive.

Connect with Jessica:

• Website: www.jcaresse.com

• LinkedIn: https://www.linkedin.com/in/jessica-caresse

• Connecting The Dots Podcast: https://www.jcaresse.com/podcast

• Book a call: https://calendly.com/jcaresse/connect-with-jessica-caresse

And the data backs it up.

In this episode of Leadership Recalibrated, I sit down with Alexandra Tillo, Senior Talent Marketing Consultant at Indeed Canada, who has spent nearly a decade sitting at the intersection of employers and job seekers translating labour market research into insights that the people making hiring decisions need to hear. What she is seeing right now when it comes to women in the Canadian workforce is equal parts progress and friction. And the friction is persistent.

This is a conversation for every woman navigating the job market right now, for every leader who wants to understand what their hiring process is actually communicating, and for anyone who believes the only way to change outcomes is to change the system creating them.

About Our Guest

Alexandra Tillo is a Senior Talent Marketing Consultant at Indeed Canada, where she has spent nearly a decade working at the intersection of employers and job seekers. In her current role, she serves as Indeed Canada's market-facing voice — speaking at industry conferences, executive briefings, and events, and translating labour market data and talent trends into actionable insights for HR and talent acquisition leaders across the country.

Before stepping into thought leadership, Alexandra built her career on the front lines of enterprise sales managing national accounts at Indeed with some of Canada's largest employers including Walmart, McDonald's, and Gordon Food Service, and earlier at Oracle where she led bilingual account management across enterprise and mid-market segments in Eastern Canada.

Alexandra brings a global perspective to the Canadian hiring market and a clear-eyed ability to connect what the data says with what employers and job seekers are actually experiencing on the ground.

She is one of the most informed voices on what the labour market data says about women in the workforce right now. And in this episode, she is not holding back.

Connect with Alexandra

• LinkedIn: https://www.linkedin.com/in/alexandra-tillo/

• Indeed: https://www.indeed.com/

• Indeed article: https://www.indeed.com/career-advice/news/women-flexible-work-benefits-gap

That is not a you problem. That is what happens when high-achieving women are handed a leadership playbook that was never written for them.

In this episode of Leadership Recalibrated™, I sit down with Lynsey Mulder — keynote speaker, certified coach, and former SVP at a Fortune 100 company — who spent over two decades inside corporate America before leaving to do something about the leadership advice gap that keeps so many women stuck.

Lynsey does not do recycled. She does not do watered down. She comes with frameworks, data, and the kind of honest conversation that most leadership training skips entirely.

We get into the invisible work tax that women absorb without even realizing it is happening, why only 20% of people actually know their superpower and what it costs when you are not one of them, the BANK framework and how cracking your own communication code changes everything about how you lead and how you are heard, why no is not a problem to manage but a strategy to master, and what it actually takes to stop conforming to a mold that was never built for you and start leading in a way that is fully, unapologetically yours.

This is a conversation for women who are done surviving the room and ready to own it.

About Our Guest

Lynsey Mulder spent over two decades rising through corporate America to reach the level of Senior Vice President at a Fortune 100 company, and what she learned along the way is that most leadership advice handed to women is either recycled, watered down, or written by people who've never actually been in the room. So she left to change that.

Today, Lynsey runs multiple businesses. Her favorite being working with high-achieving women, business owners, and executives who are ready to lead on their own terms. She is a keynote speaker, certified coach, podcast host, and the person your leadership team calls when they need real talk, not a motivational poster. Her frameworks help leaders get clear on what they stand for, how they communicate, and what they refuse to compromise, so they can stop surviving the room and start owning it.

Connect With Lynsey

Website: https://lynseymulder.com

Lead With Spark Podcast: https://lynseymulder.com/podcast

So women do. And then they get penalized for it.

In this episode, I dig into the research and I came with receipts. Most importantly, I share some positive stats we don't hear about.

If you are a first-time manager trying to figure out how to lead with AI, or a senior leader who's done waiting for the system to catch up, this one is for you.

Her Executive Ascent Leadership Programs: https://community.herexecutiveascent.com/pages/program-offerings

The Research

• LeanIn

  • https://leanin.org/research/ai-women-gender-gap-data

  • https://leanin.org/education/what-is-attribution-bias

• HBS

  • https://d3.harvard.edu/the-gender-divide-in-generative-ai-a-global-challenge

  • https://www.hbs.edu/faculty/Pages/item.aspx?num=66548

• HK / Peking University

  • https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5255039

• Monday Girl

  • https://itbrief.ca/story/canadian-women-report-greater-anxiety-over-ai-at-work

• UNESCO

  • https://www.unesco.org/ethics-ai/en/articles/tackling-gender-bias-and-harms-artificial-intelligence-ai

• Dev Barometer:

  • https://www.bairesdev.com/blog/dev-barometer-q1-2026-ai-accountability

• Deloitte:

  • https://ceo-na.com/technology/women-and-generative-ai-the-adoption-gap-is-closing-fast-but-a-trust-gap-persists/

• UN Global Compact

  • https://unglobalcompact.org/compactjournal/silicon-ceiling-how-ai-risks-rewiring-workplace-inequality

• Nature

  • https://news.stanford.edu/stories/2025/10/ai-llms-age-bias-older-working-women-research

• ILO

  • https://www.weforum.org/stories/2026/03/ai-gender-parity-womens-history-month-jobs/

Joana Castro has a different theory about why that is. And the answer isn't another supplement, another diet, or another wellness trend.

Joana is a food therapy expert and founder of My Own True Balance, who sits at a rare intersection of nuclear medicine, biomedical engineering, and traditional Chinese medicine.

After spending years inside the corporate world while quietly rebuilding her own health — following years of being dismissed by the medical system — she built a practice that merges Eastern and Western science in a way that most people in either field haven't managed to do.

In this episode, we talk about why the way most women have been eating their entire lives has been working against them, what traditional Chinese medicine understands about the female body that Western medicine is only beginning to catch up to, and why real healing was never supposed to be fast. It was supposed to be steady.

This is a conversation about food as medicine, about learning to understand your own body's makeup, and about what it looks like to stop chasing fixes and start owning your energy.

About Our Guest

Joana Castro is a creative polymath and food therapy expert bridging science, TCM, and holistic health. With a background in nuclear medicine, biomedical engineering, and TCM food therapy; she turned personal health struggles into a mission: helping people to achieve balance through food, so they can truly own their energy. 

As Founder of My Own True Balance, Joana helps busy professionals reclaim vitality through food therapy. She specializes in female health (menstrual & menopause), burnout recovery, energy/focus, and digestion—fueled by science, ancient wisdom, and real results.

Connect with Joana

• Linkedin Profile: linkedin.com/in/joanadscastro/

• Website: https://myowntruebalance.com

• Food Therapy Session: https://myowntruebalance.com/food-therapy-consultation

• Use promo code: TAKE10 to receive a 10% discount, valid on your first purchase, on the My Own True Balance website.

But what if that instinct is exactly what's keeping you stuck?

In this episode of Leadership Recalibrated, I sit down with Saeideh Fard, CFO at Introhive and one of the most intentional network builders I have had the pleasure of meeting. Saei has spent 25 years in executive finance roles across some of North America's most ambitious growth-stage technology companies. And she credits a significant part of that success not to technical expertise alone, but to a concept she calls the Power of Three.

The idea is simple. The impact is anything but.

This is a conversation for anyone who has been playing small, waiting to be discovered, or solving everything alone when the network they need is already two steps away.

About Our Guest

Saeideh Fard — known as Saei — serves as CFO at Introhive, a B2B SaaS organization bringing client intelligence, data and automation solutions that drive growth in North America and Europe's top legal, accounting, and consulting firms. 

Previously, Saei was the SVP, Finance at PointClickCare, the leading Electronic Health Record technology partner to North America's senior care industry; CFO at Tulip Retail, a SaaS technology leader in retail store associate mobility and a Kleiner Perkins and Salesforce Ventures portfolio company. Earlier, she served as Group CFO at Traveledge and Kensington Tours, overseeing teams across luxury leisure, corporate, and technology divisions across Canada, US and Bermuda. Her C-Suite career spans transformation roles focusing in high organic growth and M&A environments. 

Saei started her career as a CPA with PwC. She holds both Canadian and US CPA designations, and is an alumni of the Queens (Smith) School of Business. Her championed causes include helping women, youth and immigrants achieve their leadership potential.

Connect with Saei on LinkedIn at https://www.linkedin.com/in/saeidehfard

Learn more about Introhive at introhive.com

In this episode of Leadership Recalibrated, I sit down with Nana Obeng, Strategic Operations Partner and Fractional COO, to talk about the critical — and often painful — mindset shift every scaling founder eventually has to make: stepping out of the role of operator and into the role of architect.

Nana breaks down why high-achieving women are particularly susceptible to becoming the bottleneck in their own businesses, what the warning signs look like before they become a crisis, and how to build the kind of decision infrastructure that lets your business grow without everything running through you.

They also get into the role of AI in modern leadership — how to use it to accelerate your thinking and pressure test decisions without letting it replace the executive judgment that no algorithm can replicate.

If you're leading an established, revenue-generating business and starting to feel the friction of your own growth, this conversation will meet you exactly where you are.

About Our Guest

Nana Obeng is a Strategic Operations Partner and Fractional COO for women CEOs leading established, revenue-generating businesses navigating the shift from steady growth to structured scale. She helps founders strengthen how their companies operate so growth becomes intentional, disciplined, and sustainable.

With over 15 years of experience across engineering, corporate strategy, and entrepreneurship, she brings systems thinking and executive-level judgment to complex growth decisions. After starting her career in structured corporate environments, she realized she wanted to see more direct impact where strategy translated quickly into results.

After earning her MBA and co-founding a consumer brand, she saw a consistent pattern. Ambitious women were building exceptional businesses while also carrying significant responsibilities outside of work. What they needed was not more tools. They needed the right operational partner.

Today, she partners with women CEOs leading established, revenue-generating businesses to reduce operational noise and scale with clarity, financial discipline, and operational leverage.

Connect with Nana

• Website: https://www.highpointconsulting.online/

• LinkedIn: https://www.linkedin.com/in/nana-obeng/

• Instagram: https://www.instagram.com/hipt_consulting/

The first wave hit on the morning of March 31st. Thousands of Oracle employees woke up to find an email where their career used to be, with more waves expected to follow until the total reaches what analysts estimate could be as many as 30,000 people. Oracle sent it at 6 a.m. No manager called first. No HR conversation. Just a form letter from something called "Oracle Leadership" and a severed VPN connection before the coffee finished brewing.

The company isn’t struggling. It posted a 27% jump in net income last quarter. What it is doing is spending money it doesn’t have on infrastructure it can’t yet afford, and handing the bill to the people who showed up every day and built the thing. Oracle took on $58 billion in new debt in two months. Its stock has dropped by half since September. The people being cut in waves are the margin call.

This is the AI gold rush, and like every gold rush in history, the people doing the actual digging aren’t getting rich.

A Race That May Already Be Over

Before we even get to the human cost, it’s worth asking whether Oracle’s bet makes any sense at all.

Amazon, Microsoft, and Google have been building cloud and AI infrastructure for over a decade. They have the land, the power contracts, the cooling systems, the supplier relationships, and the engineering talent that took years to accumulate. They have the customers locked in through dependency, habit, and switching costs that make leaving genuinely painful. They are not waiting for Oracle to catch up.

Jumping into the AI infrastructure race now, at this scale, funded by debt, is a little like Microsoft deciding in 2012 that it was going to dominate the smartphone market. The logic looked reasonable on paper. Mobile was the future. Microsoft was a technology giant with vast resources. And yet by the time Windows Phone launched seriously, the game was already over. Apple and Android had the apps, the developers, the ecosystems, and crucially the users. Microsoft had PowerPoint and a burning desire to be relevant.

The outcome was predictable to almost everyone except the people who had already committed to the strategy. I know this more personally than I would like. I spent nine months writing a technical book on Windows Phone development, only to have it be obsolete by the time it went to print. The market had already moved on before the ink dried. Oracle’s board apparently did not get that particular memo.

Oracle is attempting to do what Microsoft failed to do, in a market where the incumbents are far more deeply entrenched than Apple and Android were, while carrying debt that would make any CFO’s hands shake. The companies that will win the AI infrastructure race are almost certainly already winning it. The companies taking on $58 billion in debt to chase them are not disrupting the market. They are funding it, while their employees pay the entry fee and their shareholders take the loss.

The people being cut in waves didn’t lose their jobs to AI. They lost their jobs to a late bet on a table where the cards are largely already dealt.

Nobody Is Teaching Anyone Anything

Here’s the part that should embarrass every tech executive who has ever stood on a stage and talked about human-AI collaboration: companies pouring hundreds of billions into AI infrastructure are spending almost nothing on helping their workers actually use it.

What gets called “training” is usually a one-hour session, maybe a free Coursera subscription, and a vague directive to get comfortable with the tools. That’s not preparation. That’s paperwork. It exists so the company can say it did something, not because anyone seriously expects it to change how people work.

The result is predictable. Adoption is uneven. Mistakes get blamed on employees. People who can’t figure it out alone get managed out. The productivity gains executives promised investors don’t materialize, because productivity doesn’t come from buying a tool, it comes from knowing how to use one. Nobody is teaching that part. It costs money and takes time and doesn’t show up in a press release.

Larry Ellison signed off on this. The board approved it. The executives who drafted that 6 a.m. email went back to their morning as planned.

None of them will struggle to find their next job, fund their retirement, or explain a gap on their resume. They cut the people who built the thing, offered no path forward, and will spend the rest of the quarter talking about innovation.

The Bill Nobody Wants to Read

Oracle’s planned data center buildout is estimated at $156 billion. That number gets reported as a sign of ambition. It is also an environmental commitment that nobody asked voters, communities, or future generations to weigh in on.

A large AI data center drinks as much water as a small city, sometimes more. The energy demands are not modest, and they are growing faster than the renewable capacity being built to meet them. Microsoft, Google, and Oracle have all quietly softened climate commitments over the past two years as AI infrastructure demands accelerated. The press releases about sustainability are still there. The targets behind them have been revised downward, delayed, or quietly dropped.

Greta Thunberg has spent the better part of a decade skipping school, missing her teenage years, and telling rooms full of world leaders and billionaires that they are failing her generation. She said it at the UN. She said it at Davos, both in 2019 and 2020. She said it to their faces, on camera, with the whole world watching. She has been saying the same thing ever since, increasingly to people who have stopped pretending to listen.

When the most recognizable climate activist on the planet can’t get a boardroom to take climate change seriously, the idea that a sustainability footnote in an annual report is going to move the needle on $156 billion worth of data centers strains credibility to its breaking point.

This is the part of the AI story that gets the least coverage, possibly because the people writing about AI are mostly excited about it, and possibly because the timescales involved are inconvenient for quarterly earnings calls. But the water doesn’t care about the earnings call. Neither does the grid.

If the infrastructure race continues at its current pace, the environmental cost won’t show up as a line item in Oracle’s next annual report. It will show up somewhere else, for someone else, later. That is precisely the kind of externality that corporations are structurally designed to ignore.

Which raises the question: where is today’s Erin Brockovich? Because what we need right now is not another white paper on sustainable computing. We need someone in a parking lot outside a data center in Arizona, talking to the families whose well ran dry, with a paralegal badge and absolutely no patience for corporate lawyers. The contaminated water that drove Brockovich’s case was invisible until someone connected the dots between what a company was dumping and what was happening to the people living nearby. The same dots exist here. They just haven’t been connected loudly enough yet.

Productivity Tools for What, Exactly?

This is the question nobody in enterprise tech seems willing to sit with. We are building enormous, expensive, power-hungry systems to make knowledge work faster, cheaper, and more automated. Fine. But faster toward what? Cheaper for whom? Automated in whose interest?

If the answer is that AI will drive economic growth, that growth needs to land somewhere other than the balance sheets of companies that just fired a fifth of their staff. If the answer is that AI will solve big problems, someone needs to explain why the companies building it are simultaneously walking back climate commitments and laying off the people who might have worked on those problems.

AI is no good to us if the planet can’t sustain running it. A tool that accelerates productivity while accelerating ecological breakdown is not solving the problem. It is adding processing power to it.

The people Oracle cut loose in waves this week weren’t standing in the way of progress. Many of them were probably the most capable people in the room for figuring out what these tools could actually do. They just had the bad luck of working in divisions that didn’t have a data center attached.

What Accountability Looks Like

Outrage fades. It always does. The thing that changes corporate behaviour, historically, is sustained pressure from multiple directions at once: workers who organize instead of just venting, consumers who redirect spending and stay redirected, legislators who update laws written before any of this existed, and institutional investors who stop treating worker welfare as someone else’s problem.

The deeper fix is structural. Workers need a seat at the table when decisions like this get made, not just the legal right to receive a form letter afterward. Some countries built that into how corporations are governed. Most didn’t. The AI moment is a reasonable time to revisit that choice.

Because none of this is inevitable. Not the 6 a.m. emails. Not the six-week severance. Not the abandoned climate targets. Not the fiction that we’re all racing toward something good.

Every single one of these decisions had a human being behind it, with a name, a title, and a bonus structure that rewarded exactly this outcome.

The technology is not the problem. The technology is being used as cover for the problem, and we keep allowing it.

P.S. Yes, I used AI to help structure and articulate this. My original draft was mostly expletives. Claude cleaned it up, kept the anger intact, and had the audacity to do it without drinking a reservoir dry or laying anyone off. Make of that what you will.