Google’s Stitch is worth paying attention to, even if you’re not a designer.

Launched at Google I/O in May 2025 and now significantly redesigned, Stitch bills itself as an “AI-native infinite canvas” for UI design. The new version introduces what Google is calling “vibe design”: instead of starting with wireframes or pixel-level specs, you start by describing what you want the product to feel like. Your business objective. The emotion you want the user to have. What’s currently inspiring you. The AI handles iteration from there.

You can now speak directly to the canvas, ask for real-time design critiques, request variations by voice, and generate interactive prototypes without touching a single tool panel. Stitch will even auto-generate the next logical screens based on user flows you didn’t explicitly specify.

There are rough edges. This is still a Google Labs experiment. But Figma’s stock dropped when the update landed. That tells you something about how the market reads the stakes.

What’s actually happening here

Vibe coding was about abstracting away technical execution. You describe the outcome you want; the model writes the code. Stitch does something adjacent but distinct: it’s abstracting away design literacy, or the gap between having a clear idea and being able to express it visually.

That gap has always been costly. The person with the sharpest instinct for what a product should feel like is often not the person trained to render it. Founders, strategists, product managers, researchers: these are people who understand the intended experience but lack the vocabulary to express it in Figma. Stitch appears designed specifically for them, not as a shortcut for designers, but as an on-ramp for people who have been locked out of the design conversation entirely.

This is a meaningful shift. Not because it makes design “easier” in some trivial sense, but because it changes who gets to participate in shaping what a product looks and feels like before anyone writes a line of production code.

The broader pattern

The bet most AI labs made in 2024 was that software development would be the first domain where AI created real capability leverage. That bet looks correct. But coding was always the most legible example of a broader dynamic: AI peeling away the technical skill layers that have historically separated someone with a clear idea from someone who can execute it.

Design is now following. Writing is arguably already there, though the quality questions are still live. Strategy and analysis are further behind, but the direction is consistent.

One analyst noted that without deterministic constraints — design standards, corporate patterns, contextual requirements — tools like Stitch carry real risk of producing outputs that are technically polished but strategically incoherent. That’s a fair concern. The question isn’t whether the canvas can produce something beautiful. It’s whether the person guiding it knows what they actually want.

That, as it turns out, is the part that was always the hard part.

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Stitch is currently free in Google Labs beta. Worth trying even if design isn’t your domain — the experience of describing intent and watching a canvas respond is clarifying in ways that are difficult to anticipate until you do it.

Sources: Google Blog | The Register | The Deep View | AI Business

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Earlier this year, Jack Dorsey announced that Block would cut 4,000 employees, roughly 40% of its workforce. His explanation was simple: AI tools now allow fewer people to do more. Investors loved it. The stock jumped 24%.

The framing was clean. The narrative was legible. It had all the structural markers of a bold, forward-looking strategic move. We’ve seen this pattern before.

I wrote recently about “meaning-shaped” outputs: content that feels like it deserves your attention but on closer inspection has no actual relationship to the thing it claims to be about. LinkedIn comments that look thoughtful but say nothing. Reports with all the hallmarks of rigor but no genuine analysis underneath.

What’s happening in corporate America right now is meaning-shaped transformation. Companies are producing the shape of AI-driven change: the layoff announcements, the efficiency narratives, the investor-friendly framing. But without necessarily having done the underlying work that would make those narratives true. (Or perhaps, betting they won’t have to put in that work.)

The Numbers Behind the Narrative

Block’s story is instructive not because it’s unique, but because it’s so legible from multiple angles at once.

One angle: Block tripled its headcount between 2019 and 2022, from under 4,000 employees to over 12,000. Its stock dropped roughly 40% since early 2025. It had been under sustained pressure to cut costs. An analyst at Financial Technology Partners told Bloomberg this was “more about the business being bloated for so long than it is about AI.”

Another angle: Dorsey says Block built an internal AI tool called Goose, and that recent model improvements convinced him the company could operate effectively at half its size. He claims the company is targeting $2 million in gross profit per employee, up from $500,000 historically. That’s a real number attached to a real thesis about productivity.

Both of these things can be true at once. That’s exactly what makes “AI washing” so slippery.

In the weeks after the announcement, the picture got murkier. Laid-off and current employees began speaking publicly, with many describing the cuts as Dorsey “posturing for the market”, a way of winning back investor confidence after heavy investments in cryptocurrency, according to reporting in The Guardian. Worth noting in that context: Block spent $68.1 million on a company-wide event in September 2025 — featuring Jay-Z, Anderson .Paak, and 8,000 employees flown to Oakland — five months before cutting nearly half the workforce. That sequencing tells its own story.

The term “AI washing” has been circulating for months, and the evidence base is growing. A Forrester report from January found that many companies announcing AI-related layoffs don’t have mature AI systems ready to replace the roles they’re cutting. Goldman Sachs economists estimate AI is currently eliminating only 5,000 to 10,000 jobs per month across all U.S. sectors, a number that makes Block’s 4,000 cuts look oddly attributed to the technology alone.

Perhaps most telling: when New York State gave employers the option to cite “technological innovation or automation” in their legally required layoff notices, not a single one of the 160 companies filing (including several that publicly blamed AI) checked the box.

In Dorsey’s Own Words

In a subsequent interview with WIRED, Dorsey was given the chance to answer the AI-washing charge directly. Asked point-blank, were you AI-washing the layoffsb, he didn’t say no. He said: “The most important thing for me and the company is that we stay well ahead of the technology trends that are impacting us.” That’s a pivot, not a rebuttal.

What he offered instead was a genuine and genuinely ambitious vision of what he’s building toward. He said something really shifted in December in the sophistication of AI tools, specifically naming Anthropic’s Opus 4.6 and OpenAI’s Codex 5.3 as the trigger, and that it “presented an option to dramatically change how any company is structured.” He described the goal not as a leaner version of Block but something more radical: “I want the company itself to feel like a mini AGI.” No management hierarchy. An intelligence layer on top that employees and customers alike can query, build on, customize.

Take him at his word and it’s a serious idea. The kind of organizational redesign he’s describing is genuinely new thinking, not just efficiency-speak. The question is whether what Block actually did is that, or a precursor to it, or something else wearing its clothing.

He predicts that “every company that’s not building itself as intelligence is going to face something existential, and it’s going to happen over the next year or two.” Maybe. But betting that your prediction will prove true doesn’t retroactively make the method of getting there coherent. The vision and the execution are two different things, and the gap between them is exactly where AI washing operates. Yet, what is the actual downside of losing such a bet?

The Incentive Structure Is the Story

Why would companies frame layoffs as AI-driven when the reality is more complicated? Because it works.

Molly Kinder at the Brookings Institution puts it plainly: blaming AI is “a very investor-friendly message,” far more palatable than admitting the business was overstaffed or struggling. Block’s stock surging 24% on the announcement is the proof of concept. Markets reward the AI transformation narrative. They punish admissions of past mismanagement.

Bloomberg's opinion desk framed it well: this is a familiar Silicon Valley pattern of reframing mistakes or inconvenient decisions as vision. "Move fast and break things." A weak governance structure becomes protecting the founder's vision. A cost-cutting decision becomes leading the industry into the future. The substance changes; the move is always the same.

Deutsche Bank analysts have predicted that “AI redundancy washing will be a significant feature of 2026.” Nearly 60% of U.S. hiring managers surveyed by Resume.org said they emphasize AI’s role in job cuts because it’s perceived more favorably than citing financial constraints.

This is not a conspiracy but rather incentive structure. Incentive structures produce predictable behavior.

Wharton’s Peter Cappelli summarized the dynamic with characteristic directness: “The headline is, ‘It’s because of AI,’ but if you read what they actually say, they say, ‘We expect that AI will cover this work.’ Hadn’t done it. They’re just hoping.”

The Data Gap: Investment vs. Return

Step back from the layoff headlines and look at the broader picture, and something stark comes into view.

PwC’s 2026 Global CEO Survey of 4,454 executives across 95 countries found that 56% have seen neither revenue growth nor cost savings from their AI investments. Only 12%, one in eight, reported achieving both. Separately, Deloitte found that 84% of companies have not redesigned roles around AI capabilities, even though 36% expect significant job automation within a year.

Read those numbers together: the vast majority of organizations haven’t restructured their work around AI, haven’t seen financial returns from AI, and yet a growing number are attributing layoffs to AI.

That’s the gap. That’s where AI washing lives.

It’s the same gap the meaning-shaped piece approached from a different direction. Organizations are producing more outputs than ever — more reports, more strategic plans, more transformation narratives — while the quality of their actual decision-making lags behind. The volume of plausible-looking material starts to mask the absence of genuine understanding underneath.

What’s Actually Happening

None of this is to say AI isn’t changing work. It clearly is, in some contexts, for some roles, in ways that are genuinely significant. Anthropic’s Dario Amodei has written at length about the potential for what he calls “unusually painful” disruption. An MIT + Oak Ridge National Laboratory study found AI can already perform work equivalent to 11.7% of the U.S. labor market1. These aren’t trivial findings.

But there’s a meaningful difference between a company that is actually transforming how work gets done (redesigning roles, investing in retraining, evolving its operational model) and one that is using “AI” as a more acceptable frame for cost-cutting decisions that were coming regardless. The first is a real management challenge that deserves serious attention. The second is a category error being actively incentivized by capital markets.

The inability to tell the difference between those two things is itself the problem. Not just for workers trying to understand their job security, but for leaders trying to make sound technology investments, investors trying to allocate capital wisely, and policymakers trying to respond appropriately. Orientation and discernment, in other words. That’s what’s actually scarce here.

The Same Bottleneck

The scarce resource is the capacity to perceive clearly what’s actually happening, to distinguish between the shape of change and change itself.

When Dorsey tells the market that AI justifies cutting 40% of his workforce, the question isn’t whether AI is powerful. It is. The question is whether this specific claim, at this specific company, reflects genuine organizational transformation or a narrative optimized for investor reception. Or, something else.2

Answering that question requires someone who understands both the technology and the business well enough to actually evaluate it. That judgment doesn’t come from reading the press release.3

Discernment, not information, is the scarce resource.

The Broader Moment of 2026

This is all unfolding in a week when the Anthropic-Pentagon standoff reminded us that even the companies building AI are still figuring out the terrain — and that the relationship between AI capabilities, institutional power, and public accountability is being negotiated in real time, sometimes (usually) clumsily. The map-makers don’t have complete maps either.

Meanwhile, employee anxiety about AI and job loss has jumped from 28% in 2024 to 40% in 2026, according to Mercer’s global survey of 12,000 people worldwide. The IMF’s managing director says AI is “hitting the labor market like a tsunami.” Deutsche Bank warns most of the wave is manufactured narrative.

We are in a moment where the story about AI’s impact is running well ahead of the reality of AI’s impact, and the gap between the two is generating real consequences for real people. Four thousand of them at Block found out this week which side of that gap they were on.

That gap is a dis/orientation problem. And disorientation problems don’t get solved by producing more meaning-shaped discourse about them. They get solved by doing the slower, less marketable work of actually understanding the terrain.

The trouble is, doing work is being outpaced by the need to look the part.

This is part of the AI Moment (or Bubble) that we are still glimpsing, hoping to fathom4, no less, make meaningful choices about. The pressure to perform certainty is felt clearly. The tension between the urgency of performance and the lack of means to form coherence will be a defining challenge (or perhaps simply a burden) that will endure through the rest of this year.

Of course, the fallout isn’t falling evenly. Those writing the layoff press releases and the people named in them are not experiencing the same year. That asymmetry5 of who absorbs the gap between AI’s promise and its current reality is worth its own examination. Some economists would call it a K-shaped economy. I think that framing deserves a closer look, and will center on that in forthcoming posts; in addition to unpacking Big Tech Bets from the perspective of founders, investors, and those potentially buying their new products.

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Every AI startup getting funded right now is making a bet, whether the pitch deck says so or not. VC investments typically take five to eight years to exit. That means your product needs to still matter in a world where Anthropic, OpenAI, and Google have had five to eight more years to build. You’re not only betting on your own execution. You’re betting against theirs1.

Ethan Mollick put this cleanly in a recent post: almost every AI VC investment is essentially a bet against the vision the big labs have laid out. He’s right. And I’d take it further, because I’m watching this tension from both sides of the table.

I’ve been a founder, and I’ve evaluated vendors for enterprise purchasing decisions. I mentor startups through accelerator programs, and I advise companies on their technology strategy through . That range of vantage points means I keep seeing the same collision from different angles. On one side: the founding team with a sharp, specific tool, priced well, built for a real workflow. On the other: the enterprise buyer who already has a relationship with AWS or Azure or Oracle whose default move will always be to check what the platform offers before even looking at a startup.

This is the competitive landscape that doesn’t get enough honest discussion in startup spaces. Moats, runway, product-market fit: the standard vocabulary is all still relevant, but it now has a gravitational body that it didn’t before. The big labs aren’t just competitors. They’re the tidal force that shapes the entire market.

Here’s what I mean concretely. A startup might build a genuinely excellent AI agent for a specific compliance workflow. Solves a real problem, customers love it, price is right. But then AWS announces a general-purpose agent framework at re:Invent, and suddenly every enterprise buyer’s procurement team is asking, “Why don’t we just use the thing that plugs into our existing stack?” The startup’s product might be better. It almost certainly is, for that use case. But “better” is not the only variable in an enterprise purchasing decision, and often it’s not even the most important one.

I’ve seen this inertia from the inside. For over 18 months at one company I worked with, the software team was making the case2 to switch from one familiar vendor to a clearly superior alternative. The c-suite wouldn’t move. The delay held back prototyping and development — and then leadership used that same slowdown as justification for canceling the initiative later. The decision not to decide became the decision. That’s the loop procurement inertia creates, and it’s grimly common.

But that’s a story about inertia within an organization that had already committed to buying something. There’s a separate problem that doesn’t get discussed enough: buyers who haven’t committed to buying anything yet. I ran into a clear version of this in a recent conversation with a compliance director at a major financial exchange. When I asked about their timeline for deploying particular AI agents internally, the answer was revealing: still roadmap-stage. Not deployed. Not even piloting, really. Still in the “we’re evaluating the landscape” phase.

That’s the other side of Mollick’s observation that deserves attention. It’s more than startups are betting against the labs. It’s that the enterprise buyers everyone is building for may not be ready to place their bet yet, either. The market timing question cuts both ways. You can have the right product and still be early in a way that burns through your runway before the buyers show up3.

So what do you do with this?

If you’re a startup founder in AI right now, I think the honest answer is: you need to be very specific about which part of the big labs’ roadmap you think they’ll deprioritize or do badly. Not just “we’re more focused.” That’s table stakes. You need a real theory about why the hyperscaler will leave this gap open long enough for you to build a business in it. You will need to pressure-test that theory with actual buyers, not just other founders.

If you’re a company trying to make AI buying decisions, the honest answer is different but equally uncomfortable. The default will always be to wait for your existing vendor to offer something “good enough.” Sometimes that’s the right call. Sometimes it means you’re buying a mediocre general solution eighteen months from now when you could have had a sharp specific one today. The trick is knowing which situation you’re in, and most organizations don’t have a reliable process for figuring that out.

This is a lot of what I spend my time on at Bridgecraft: helping companies develop a real point of view on their technology landscape, rather than letting vendor relationships and procurement inertia make the decision by default. It’s not a solved problem. But it’s a solvable one, if you’re willing to do the work of actually understanding the terrain instead of just reading the map that your existing vendors hand you4.

The AI investment landscape isn’t going to get less complicated. The bets aren’t going to get easier. But the quality of your orientation to the landscape you’re operating in, and awareness of which paths afford the best chance of reaching desirable destinations? That’s where enduring advantage can be found.

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At 5:01 PM Eastern today, a deadline set by Defense Secretary Pete Hegseth expires. Anthropic, the AI company behind Claude, must either accept the Pentagon’s demand for unrestricted “all lawful use” access to its AI model on classified military networks or face contract termination and designation as a supply chain risk.

Anthropic CEO Dario Amodei rejected the Pentagon’s final offer last night, stating the company “cannot in good conscience accede to their request.”

This is the most significant confrontation between a private AI company and the U.S. government to date. And the way it’s unfolding tells us more about the state of AI governance than either side probably intends.

What’s actually at stake

The dispute is narrow in scope but enormous in precedent.

Anthropic has two red lines. It does not want Claude used for mass surveillance of Americans. It does not want Claude used in fully autonomous weapons without human involvement. The company has maintained these restrictions since it signed a contract worth up to $200 million with the Pentagon last summer, making Claude the first AI model deployed on the military’s classified networks.

The Pentagon’s position is that the end user, not the vendor, should determine how a licensed technology is used. As a senior Pentagon official told CNN: “You can’t lead tactical ops by exception. Legality is the Pentagon’s responsibility as the end user.”

Pentagon spokesperson Sean Parnell has insisted the military has “no interest in using AI to conduct mass surveillance of Americans (which is illegal) nor do we want to use AI to develop autonomous weapons that operate without human involvement.” But the Pentagon has consistently refused to put those assurances into contractual language. Anthropic says the latest proposed contract revisions, which the Pentagon framed as a compromise, were “paired with legalese that would allow those safeguards to be disregarded at will.”

This is not a dispute about what the Pentagon currently plans to do. It’s a dispute about who gets to set boundaries on what AI can be used for in national security contexts, and whether those boundaries can be contractually binding.

The epistemology of “all lawful use”

The Pentagon’s demand that AI tools be available for “all lawful purposes” sounds like a reasonable standard. It implies that legality itself is a sufficient guardrail. But this framing deserves more scrutiny than it’s getting.

Mass surveillance of Americans is not clearly illegal. It exists in a legislative gap. The legal frameworks governing domestic surveillance (FISA, Executive Order 12333, the Fourth Amendment) were built for wiretaps and phone records, not for AI systems capable of processing and cross-referencing vast datasets of behavioral, biometric, and communications data in real time. As a source familiar with Anthropic’s position told CNN, “there are no laws or regulations yet that cover how AI could be used in mass surveillance.”

Fully autonomous weapons systems are not prohibited by U.S. statute either. Department of Defense Directive 3000.09, which requires “appropriate levels of human judgment” in the use of force, is a policy document, not a law. It can be revised or rescinded by any defense secretary.

So “all lawful use” is not a safety framework. It’s the absence of one. It defines the acceptable boundary of AI deployment as “whatever isn’t currently prohibited” in domains where almost nothing is currently prohibited.

Amodei’s position is fundamentally epistemic. He is making a knowledge claim: that some capabilities are outside what today’s technology can safely and reliably support. In a statement Thursday, he wrote that Anthropic “believes deeply in the existential importance of using AI to defend the United States and other democracies.” But the company’s position is that current AI systems are not reliable enough for certain applications, regardless of their legality.

This distinction matters. The Pentagon is arguing from authority (”legality is our responsibility”). Anthropic is arguing from capability (”the technology isn’t ready for this”). These are fundamentally different kinds of claims, and they require different institutional mechanisms to adjudicate.

We do not currently have those mechanisms.

A Korean War statute for a 2026 problem

If Anthropic does not comply by 5:01 PM today, the Pentagon has threatened two actions: terminating the contract and designating Anthropic a supply chain risk, and potentially invoking the Defense Production Act to compel Anthropic’s cooperation.

The DPA is a Korean War-era statute signed by President Truman in 1950. It gives the executive branch broad authority to direct private industry in the name of national defense. It was designed for steel mills, tank factories, and industrial supply chains.

Legal scholars are divided on whether it can be used this way.

Alan Rozenshtein, writing in Lawfare, published the most thorough legal analysis to date. He identifies two possible demands the government might make under the DPA: requiring Anthropic to provide Claude without its contractual usage restrictions (a “same product, different terms” argument), or compelling Anthropic to retrain Claude to strip safety guardrails from the model itself. The first is legally contested. The second, Rozenshtein argues, would more clearly constitute demanding a new product, which sits on much weaker legal ground.

As Charlie Bullock of the Institute for Law & AI told the Associated Press, neither side’s legal argument is “a slam dunk.” If neither backs down, the most likely outcome is litigation between Anthropic and the federal government, testing the application of a 75-year-old industrial production statute to AI safety policy for the first time.

Rozenshtein’s conclusion cuts to the heart of the problem: “this fight is happening because Congress hasn’t set substantive rules for military AI.” He argues that if Congress had legislated guidelines on autonomous weapons and surveillance, Anthropic would likely be comfortable selling to the military without restrictions, and the DPA threat would never have arisen. The DPA itself is scheduled for reauthorization by September 30, 2026. Depending on how this dispute unfolds, its renewal could become a legislative flashpoint for AI governance.

The contradiction that reveals everything

Amodei identified perhaps the sharpest analytical point in his Thursday statement: the Pentagon’s two threatened actions are inherently contradictory. A supply chain risk designation labels Anthropic as too dangerous to work with. A DPA invocation labels Claude as too essential to lose.

You cannot be both a threat and a necessity. As former DOJ-DOD liaison Katie Sweeten told CNN: “I would assume we don’t want to utilize the technology that is the supply chain risk, right? So I don’t know how you square that.”

This contradiction reveals that the real dispute isn’t about legality or even capability. It’s about control. The Pentagon wants to establish the principle that no private company can set terms of service that constrain government use of a licensed technology. The Center for American Progress described the supply chain risk designation as potentially “existential for Anthropic” and the DPA invocation as “the quasi-nationalization of a frontier lab.”

Gregory Allen, a senior advisor at the Center for Strategic and International Studies, offered important context on Bloomberg Radio. The actual users of Claude within the Defense Department, he said, “love Anthropic, love Claude” and report that the company’s usage restrictions “have never been triggered.” The dispute is not being driven by an operational problem. It’s being driven by a principle.

The race to the bottom

The Anthropic standoff does not exist in isolation. It’s unfolding alongside several parallel developments that, taken together, paint a concerning picture.

xAI’s Grok is already on classified networks. Elon Musk’s xAI has signed a Pentagon contract under the exact “all lawful use” terms that Anthropic is refusing. This is the same model that has generated approximately 3 million deepfake images, including an estimated 23,000 depicting minors, and whose offices were raided by French prosecutors. The Pentagon has confirmed that Grok is “on board with being used in a classified setting,” though officials acknowledge it is not viewed as being as advanced as Claude.

The Pentagon’s AI strategy explicitly omits ethical AI language. A January 2026 strategy memorandum from the Department of War directed all Defense Department AI contracts to incorporate “any lawful use” language within 180 days. The same memorandum bans models with DEI-related “ideological tuning” and replaces “responsible AI” with “hard-nosed realism.”

OpenAI and Google are being pressured next. An open letter published Thursday night by tech workers from Anthropic’s top rivals urged their companies to hold the same red lines. The letter states: “The Pentagon is negotiating with Google and OpenAI to try to get them to agree to what Anthropic has refused. They’re trying to divide each company with fear that the other will give in.”

Bipartisan congressional concern is growing. Retired Air Force General Jack Shanahan, who led the Pentagon’s original AI initiatives, wrote that “painting a bullseye on Anthropic garners spicy headlines, but everyone loses in the end.” Both Republican and Democratic lawmakers have raised questions about the Pentagon’s approach.

The dynamic is clear: if any one company caves, the pressure on the rest becomes overwhelming. This is the classic collective action problem in governance, and it is playing out in real time with technology that could reshape the relationship between state power and individual rights.

The voluntary safety framework problem

There is an uncomfortable irony in the timing of this standoff. On the same day Hegseth issued his ultimatum, Anthropic published a major revision to its Responsible Scaling Policy. The company replaced its binding commitment to pause training if model capabilities outstripped safety controls with a nonbinding “Frontier Safety Roadmap.” The new framework describes its safety goals as “public goals that we will openly grade our progress towards” rather than hard commitments.

Anthropic says the change is unrelated to the Pentagon dispute. Chief Science Officer Jared Kaplan told TIME that the company spent nearly a year deliberating. The core reasoning: voluntary commitments don’t work when competitors ignore them. Anthropic argued that “if one AI developer paused development to implement safety measures while others moved forward training and deploying AI systems without strong mitigations, that could result in a world that is less safe.”

Chris Painter, director of policy at METR (a nonprofit focused on evaluating AI models), reviewed an early draft of the new policy and offered a mixed assessment. He praised the emphasis on transparent reporting. But he warned: “This is more evidence that society is not prepared for the potential catastrophic risks posed by AI.”

The practical implication for anyone relying on AI vendor safety commitments is sobering. If the most safety-focused AI company in the world has concluded that binding self-regulation is competitively untenable, the market alone will not produce adequate governance. The question of who sets the rules, and whether those rules can be enforced, becomes not a theoretical debate but an operational necessity.

What this means going forward

Whatever happens at 5:01 PM today, several things are already clear.

First, AI governance cannot be resolved through contract disputes, social media ultimatums, and Korean War-era production statutes. These are institutional tools built for a different century, and they are producing outcomes that nobody designed or intended. Lawfare’s argument that Congress should set the rules for military AI is compelling precisely because the alternative, which is what we’re watching right now, is governance by improvisation.

Second, the “all lawful use” standard will cascade beyond the Pentagon. If this framing becomes the default for government AI procurement, it will shape expectations for enterprise contracts more broadly. Every organization with an AI vendor relationship should be thinking about what happens when your vendor’s relationship with other clients, including governments, changes the terms of what your tools can do.

Third, voluntary safety commitments are structurally fragile. Anthropic’s own policy shift this week demonstrates that even companies founded on safety principles will adjust those principles under competitive pressure. Organizations relying on vendor self-regulation for AI governance need their own frameworks.

Fourth, the supply chain risk designation, if invoked, would have cascading effects well beyond the $200 million Pentagon contract. It would require every company doing defense work to prove it doesn’t use Anthropic products. For a company with a $14 billion revenue run rate and a potential IPO on the horizon, the enterprise implications could be severe.

And fifth, this is not about one company. The open letter from OpenAI and Google employees makes clear that the same pressure is being applied across the industry. Today’s outcome will shape whether any AI company can maintain red lines on military use of its technology

The institutional mismatch is the story

I want to close with what I think is the real takeaway, because it’s easy to get lost in the drama of ultimatums and deadlines.

The mismatch between our institutional infrastructure and the technology it governs is the defining challenge of this decade. A defense secretary is using a 1950 statute to govern 2026 AI capabilities. A Pentagon CTO is calling a CEO a “liar” with a “God-complex” on X. Contract negotiations that should be resolved through clear legislative frameworks are instead proceeding through ad hoc threats, public pressure campaigns, and legal theories that have never been tested in court.

This is not an AI problem. It is an institutional design problem. And it is a problem that the anticipatory governance community, the futures studies community, and the technology policy community have been warning about for years.

The question has never been whether AI would be used in national security. Of course it will be. The question is whether we have the governance infrastructure to make those decisions wisely, transparently, and with appropriate accountability.

Today, at 5:01 PM, we get one answer to that question. It is unlikely to be a reassuring one.

This post represents the author’s analysis and does not constitute legal advice.

In my latest article on From Here to There, I discuss:

1. Reframing the role of a CEO (or Chief Decider)

2. What comprises a quality decision

3. What decision makers Must Do

4. Consequences of poor decision making strategies

5. How mismanagement inhibits precious creative flow and buy-in

It all leads up to the take home message of:

“Take special care of your decision making so the actual creative work can proceed as smoothly as possible.”

Read the full article at From Here to There: Strategy and Mentorship for Innovators

From Here to There: Strategy and Mentorship for Innovators

The Cost of Indecision in Leadership: On the invisible tax leaders impose when they hesitate or defer without clarity

This post is part of a recurring series focused on Vision at Altitude: Strategy, Systems, and Leading from the Top — the first pillar of the From Here to There game plan. How do those at the helm set direction in times of rapid change? set direction in times of rapid change? We explore the mindsets, frameworks, and stra…

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a year ago · 2 likes · Jesse Parent

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Background & Problem Space

I spend a lot of time in spaces where folks are talking about the future, and one thing that is coming up more and more, subtly but increasingly overtly, is about how people in leadership positions or acting as providers of vision and direction are having to:

1. pivot,

2. capitulate,

3. reverse course,

4. change their minds,

5. express or mitigate despair,

6. wade through uncertainty,

or any number of adjacent, in-between, or otherwise non-ideal ways of presenting oneself as a confident individual about the future.

The appearance of superlative amounts of confidence during this era generally cluster within these categories:

1. Single-dimension targeting*

2. Morally and intellectually dishonest or disingenuous extremes

3. Nihilism & apathy outright

This is to say: focusing only on one goal or solution at the expense of taking in a broader view; creating an heavily polarized Good or Evil view of self or others, or associate solutions or approaches; or broadly giving up and seeing it all as pointless. These are the areas where most folks right now (maybe in general, but I’m really talking about 2025, here, and now), are displaying extremely cavalier attitudes and undaunted or unflinching takes on things.

Alternatives We Can Enact

What I want to say in this piece is that the following is also a path one can walk:

1. You can be uncertain, frustrated, apathetic

2. Coalitions and spaces and community can still exist

3. But it may require context, courage, and choosing it to be so.

I don’t think the United States, in particular, has many models, examples, or legitimate heroes for how to handle situations like this that are not diluted, white-washed, or otherwise sterilized. I can mention a number of folks in the 1960s, but I fear I may loose attention in doing so.

What I want to focus on directly is challenging because none of the historical examples we may think of have dealt with the kind of change we are seeing right now; the waves we have to navigate are different.

So I want to speak to a small but important component to that process, which is:

Be prepared to allow for others to go through their vision, future, or worldview existential crisis as you collaborate and find ways of enduring and building with them.

Let’s unpack this briefly, while you’ve generously offered your attention — this is one of the most challenging things to actually look in the face in this moment that we’re in, and I appreciate you giving me the time to talk about this with you.

I am intentionally not attempting to label or prescribe a specific outcome, belief, or judgement here. I am instead trying to speak to a process of understanding what is going on, and offering a small bit of advice about how to best handle it.

As I noted above, this is a time where many of the folks we look up to, and the visions or solutions the have espoused, are undergoing varying degrees of change, uncertainty, instability, or potentially collapse.

It is natural and understandable to both look for our heroes, leaders, or figures to be confident and strong and offer a solution. But I am offering that we may:

1. reasonably guard against, and prepare for, the compulsion to seek security in those appearances,

2. as well as understand there are alternatives

That is, alternatives to abandoning our loftier beliefs or faith-in-humanity (or community in general), in by taking respite in a a more apparent stability, or more apparent “confidence.”

In other words, I am seeking to encourage and normalize that, in reality, the wisest, strongest, most viable, and most secure approach to this moment will actually warrant some pause, some reconsideration.

It may entail embracing some of the “I don’t know” of it all, and still putting forth meaning and fostering relationships in spite of not knowing.

Furthermore, it will be useful (maybe necessary) to accompany others as they transition through the stages of belief change, vision change, or reconsidering what to actually do right now.

About those with very clear, isolated goals

I think it is noteworthy to say, if there is a group, or a figure, that is clear about what they want to take away from this moment, it may also lend to the appearance of confidence; the old adage of “the best way to predict the future is to make it.” It is reasonable and probably wise to consider that there are factions attempting to pursue very specific targets right now (* Point 1 on single-targets, earlier) - but this can be differentiated from those who actually have a clear and holistic picture about what is going on right now.

As an old political science professor used to say, “The hardest time to understand is the one we are currently in.” To expand upon that, looking backwards you, you can see which of the potential pathways became more viable or eventually materialized - but history can seem linear and uncomplicated if there is not proper contextualizing for all the different ways things could have gone.

Implementation: What do we do?

So beyond identifying what may be happening, my suggestion and prescriptive stance is as follows.

Internal Work

Finding peace in times of uncertainty may sound unserious, but there are meaningful and sober ways to do so. There is such a strong ebb and flow to 2025, almost everyone I know is pulled and pushed with the waves and tides. I even have sympathy for the Influencers who are simply repeating their sales pitch and the virtues of their product or program in spite of these challenges; I’m now in that space increasingly, myself.

Whatever face people are putting on right now, even if they are front-running or band-wagoning on whatever appears to be the Winning Team (in their view), people are scared and uncertain. So what do we do about it, at the internal level?

• Find what regulates you, both individually and with others. What are your consumption limits and quotas? What are you doing that is away from all of the chaos and stress? What are your indulgence, your downtime — the carefree space we need to recharge? What about your constructive outlets or things to invest in?

These are suggestions, but you will need to invest in knowing yourself well enough to have a reasonable barometer of what kinds of stress and burnout you are accumulating, and how to care for it.

Even though the focus on this post is interpersonal, it seems critical to emphasize the internal work. The world is fierce and demanding, and you’ll have to tend to yourself first before others - just like on the airplane safety videos.

External Work: Accompanying the Variable Beliefs and Visions of Others

Community will be critical in this area, and whether you are simply looking to join one or tasked with managing and caring for others, this will come up.

Some people have been in the game for a long time and staring at these problems for decades. Some folks only now are understanding what “AI is about”, or dealing with what politics has showed itself as capable of doing.

What I want to offer is that, collectively, we are all in the same boat. Nobody really knows. Nobody really knows what is going on or what is going to happen, and “that’s ok” — which I say not to normalize it or dismiss it, but rather to say we are in a period of that much change. I am also not suggesting everyone is simply going to get along, with a little compassion and understanding.

My message here is more so that the times are different, and it would be wise to consider carefully the trajectory of someone’s background, belief transitions, worldviews, and sense of the future.

We are going to need to collaborate, forge coalitions, alliances, and new ways of working together. Surely, there will be challenges and incompatibilities and lack of alignment at hand. But as we find new ways of doing things ahead, it is viable to understand how our changing times can mask or distort potential allies.

Or, how people simply dealing with the world as it is, will have varying degrees of contribution and availability as you attempt to forge your own way ahead, reconsider or solidify your own beliefs, and your particular plan going forward.

The new name of the game is a kind of extended, distributed co-regulatory path-finding practice. Alongside some valued colleagues and collaborators, I am attempting to discern, demarcate, and otherwise write more about this, as I find it to be a critical matter and even skillset that is substantially underrepresented.

We are not prepared for this moment in most of our formally available training. But there are things to know and learn and do, and ways to support and grow and cultivate that which we will require to sustain, endure, and flourish in the months and years ahead.

At the very least, there are better and worse ways of handling all of this, and there are those of us working to sort out what they are. If you are working in this space, or want to be doing more in this space, and resonate with this message, please reach out.

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Recommended Reading

I don’t truly know a book I’d recommend about this, given the lack of actual context for the influence of AI (and its impact on surveillance, information distortion or control, and the unknown and to-be-built digital ways of relating to each other), and the general World War era political pressures — both. (No less other factors, which I will unironically bypass for brevity here.)

But I would suggest looking into:

• Those who are Steel Manning a particular view and its trajectory, so as to demarcate where things may go if that line of reasoning holds out. (Recall “The hardest time to understand is the one we’re in”, from earlier.) Due to the innumerable approaches, visions, and agendas at hand, those who identify clearly where one of them is going can be of great use to your own triangulations and extrapolations.

• Quality takes on emotional literacy, and perhaps even things like faith transitions, or people moving in and out of cults.

  • I don’t mean to sound hokey or histrionic, but, we are indeed in a period where people’s views are fought for, no less the Attention Era in general. Also, one of my most respected in-the-thick-of-it people in the building of AI has commented about the nature of Silicon Valley and tech startup spaces as similar to the Axial Age or Late Antiquity, where the vying for believers and quest for dominance of alignment or devotion to a vision.

• Good literature about finding meaning or leadership or forging community in ties of uncertainty. Particularly if it goes beyond “uncertain markets”, but actual macro-level uncertainties.

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What would you suggest?

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From Here To There: Substack, LinkedIn

It’s Sunday, and this week has been full of hard work, lots of writing, conducting interviews for a new JOPRO project, meeting friends and supporting colleagues, and having the big questions about “Where Are We Going?”

Many hours this week have been spent on the macro planning of where is the world in 2025, and what can we actually feel good about doing, contributing to, and building ahead. What’s more, I am fresh off of a conversation where I had a tough love moment of deliberation with a good friend whether or not they are invested in their current vocation/industry as either:

1. they feel they should be, themselves, to do well; or,

2. they need to be, in order to make the requisite changes to greater viability and sustainable success.

Then I see this from Greg Isenberg:

Check your pulse

He writes:

treat sunday night like a career dashboard, check your pulse at 8 PM:

A) if your stomach knots because tomorrow means meetings you would pay to skip, you need an escape plan

B) if you feel a quiet buzz like the night before a field trip because Monday means fresh momentum, you are exactly where you belong.

that "dread versus electricity" gap is probably the most honest KPI in work

How apt, how fitting, and also, how challenging, especially right now. I would go on to say that not only is it a great diagnostic moment, but its worth reflecting the broader inertia (or sense of direction) at play:

It's a remarkably challenging time to build durable visions and beliefs about where to go and what to do that is of substance in the world today.

Trust, buy-in, vision and relation to momentum

There is a kind of macro-, maybe even meta-leadership that goes on here. Are you conducting your affairs and business in a way that lends trust to others? Or maybe not so much?

We are in a world full of horror stories such as Kevin Xue recently sharing a difficult turn of events regarding PayPal backing out last second on a job offer, after relocating and quitting his old job.

So when we do our Sunday Scaries Sunday Evening KPI check in, it seems critical to make some sober inquiries and evaluations:

1. What are you feeling right now?

2. What is the current ceiling (or capacity) of the situation you are in?

   1. How is it affected by the momentum level you currently are maintaining, and

   2. How easily can that momentum level change?

3. What is the delta (or relative distance/difficulty) to aligning a good to high momentum state with a vision of the future that is both sober and exciting?

Easier said than done, in some ways, but if you earnestly show up to these questions - and use the 8 PM check in that Isenberg notes above as a starting point, you may begin to discern some answers, or action steps towards finding answers.

Are you in a community that doesn’t regularly support your vision, you sharing your situation, or effective co-regulation? Is the town you are in not fit with the resources your vocation or your desired career path (or changing paths) would benefit from? Is the vision you have for your product or company actually fitting a real market, or the markets that are emerging over the next five years? This can feel like opening a very unpleasant can of worms, especially on a Sunday night.

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Community & resilience

Letting the check in be tied to investigating these kinds of questions can serve you well; even if it brings up scary and unpleasant things that maybe you would do better to think about after some sleep and in the light of the week ahead.

Ideally, you have some trusted folks to talk it over with, too.

The key here, though, is to not dismiss the Sunday Scaries and what their diagnostics reveal as merely a typical Sunday night phenomenon.

Rather, give space to think about them during the week ahead, especially if you are finding bigger, challenging problems looming in the distance. Herein also lies one of our super-powers as human beings, and something I think will be critical to be intentional about going forward: helping others to sort these challenges out, as well.

Having community and support around finding those answers and those things to do is vital. Try to help others find their best spaces to be in, and investing in the means to understand what limits folks and allows them to flourish can have huge multipliers down the road.

For the people closest to you, for personal or business ventures, investing in their capacity to manage these spaces, and holding space for their challenges, can go a long way. It may even be a major factor in whether ventures, projects, communities, or relations fall apart, in general.

Conclusion and Why It Matters

To recap,

1. Sunday Check in, what’s the raw feeling?

2. Seek context for both the Relative momentum & inertia, and Absolute momentum & inertia of the situation you are in

3. Make note to investigate in the week ahead; give the feelings legitimacy

4. Seek and support community:

   1. showing up for others this way can have many unforeseen benefits;

   2. realizing when others cannot show up for you this way has its own insights, as well.

Alignment is a word we hear thrown around in many different contexts these days. In this context, what I am offering some conversation about is aligning your own self with a path towards the future, and seeing how making that a valued part of your own culture and communities can be a centering, co-regulating, and ultimately beneficial practice.

We receive an abundance of advice on dismissing feelings, and I would say an even greater pressuring against having serious and meaningful conversations about the future. For folks that are attempting to build things in the world, this is indeed an almost sacred space, whether or not it is treated with due diligence and reverence.

Granted, my advice indicates a somewhat ideal pathway or manner of resolving conflict - but I mention it briefly here as a reference point; if these things are not tenable, why not? I specifically mention the interplay of momentum and inertia and their relative impacts on the context of the situation you are in, as well as the value of positive and supportive community capable of discernment and resilience.

These things require cultivation and tending to, but they are worthy investments.

Finally, I have long since realized that I (personally) need to keep the momentum moving forward, otherwise I get bogged down in The Horrors of the world. All of this contributes to the ability to do that, and I invite you to consider the role you play in maintaining the the ability for others to traverse meaningful pathways, and reach meaningful destinations.

Both for yourself personally (and professionally), as well as for those close to you and the teams you are leading or contributing to, there is a healthy sense of agency and related skillsets that continue to be essential for surviving and thriving in our world.

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So thank you for taking the time to read about it here, and be a part of the various conversations I’m aiming to offer around it. As always, your comments and insights are appreciated. Be well, and may your Sunday Scaries be more than a passing, routinized practice of self-dismissal.

Cheers to the communities that care and support us as we strive against all the chaos to find and develop meaning in the world, and with each other.

-

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Thanks for reading! I'm Jes, a data scientist, strategist & founder, and interdisciplinary researcher & exploring how we lead, learn, and innovate in complex times. On this Substack, I write across a few core themes:

• Leadership & Innovation Strategy

• Data Science, Technology & Responsible AI

• Mentorship & Early Career Growth

• Projects Updates & Dispatches

• Reflections & Reviews

Subscribe to follow along, and reach out if something resonates—I’m always up for a conversation around mapping & building the future and the cultivation required to get there.

For more updates,
From Here To There: Substack, LinkedIn
Jes Parent: Substack, LinkedIn, Bluesky, Twitter/X
JOPRO - Substack, LinkedIn, Bluesky

From Here to There: Strategy and Mentorship for Innovators

Iteration Beats Stagnation, Momentum as a Better Beacon Than "Progress"

This post is part of a recurring series focused on Execution In the Arena: Project Leadership and Tactical Applications — the second pillar of the From Here to There game plan. How do you actually work with your people to move forward? Here, we discuss tactics, strategy, and specific aspects of the operational and functional core of reaching your destin…

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a year ago · 3 likes · Jesse Parent and JOPRO

My longtime mentor and Director of Orthogonal Research and Education Laboratory, Dr. Bradly Alicea, would often make a distinct remark to us in various project meetings, particularly when someone was stuck or perhaps felt uneasy about the lack of visible outputs. Momentum can be more important - or perhaps a better target, beacon, or barometer - than [visible] progress. Or put another way, iteration beats stagnation. Of course, the nature of your organization, project, deadlines, and what you have to answer to for success to be realized, will condition how this principle is applied.

Ahead, we will discuss how this principle can be used as a lens to comprehend situations, both as a manager and as a member of a team.

1. Background: The Problem Space of Arriving at a Destination

There is a fundamental tension between people’s time, attention, and ability to pursue a goal or strive for something that does not yet exist. This is an imperative and often under-emphasized aspect of all manner of innovation, research, exploration, or situations wherein what you are trying to achieve is not a matter of duplicating or reproducing something else. There is a complexity at hand, and to maintain the focus of the destination when the path to it is unclear, requires skills, literacy, and ability to communicate. The role of a “leader” or manager in this capacity, formal or otherwise, is almost as an ambassador, between the Destination and the Project Team. Diplomacy is at hand, for how the Team can create, produce, or otherwise “travel” towards the Destination, outcome, or deliverable. What’s more, the “Destination” may not be exactly where its location was determined at the very outset of the endeavor.

In essence, the role here of the manager is to not let the Destination seem too far away while indicating what incremental steps to take. Or, how to identify what steps can reasonably be endeavored for, and are worthwhile stepping on, in order to either reach the Destination, or perhaps a safe and tenable waypoint.

2. The Challenge: Everything looks perfect from far away

One of the primary challenges in this space is that a high quality final product may seem both inspiring and daunting. Even, a “minimum viable product” (MVP) itself may seem like settling, or simply not as interesting, or in lesser cases, potentially even a distraction or a bridge to nowhere.

In innovation work, it’s often not the most brilliant idea that wins — it’s the team that keeps moving.

Core idea: what looks like "progress" from afar can actually mask inertia, while smaller, imperfect steps forward build momentum and clarity.

Continue reading the full post at
From Here To There:
Strategy & Mentorship for Innovators

👉Iteration Beats Stagnation, Momentum as a Better Beacon Than "Progress"

Thanks for reading! I'm Jes, a data scientist, strategist & founder, and interdisciplinary researcher & exploring how we lead, learn, and innovate in complex times. On this Substack, I write across a few core themes:

• Leadership & Innovation Strategy

• Data Science, AI & Ethics

• Mentorship & Early Career Growth

• Projects & Dispatches

• Reflections & Media

🔗 Explore the different sections here
🧭 You can also find my work with JOPRO, From Here to There, and Data x Direction

Subscribe to follow along, and reach out if something resonates—I’m always up for a conversation around mapping & building the future and the cultivation required to get there.

For more updates,
From Here To There: Substack, LinkedIn
Data x Direction: Substack, LinkedIn
Jes Parent: Substack, LinkedIn, Bluesky, Twitter/X
JOPRO - Substack, LinkedIn, Bluesky

From Here to There: Strategy and Mentorship for Innovators

Success: not at what cost, but to what end?

For a somewhat philosophical framing to this series, I will discuss a foundational problem space when it comes to mentoring others, leading projects, and otherwise aspiring to go to new places. At the core is considering the components of success that various destinations, or alignments with various value sets, entail…

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a year ago · 3 likes · Jesse Parent

🧭 Rethinking Success: Not “At What Cost?” but “To What End?”

In a recent post on From Here to There, I explore the deeper dimensions of success—especially for those building innovation teams, leading projects, or mentoring others. Too often, ambition gets measured in speed or sacrifice. But what if the more critical question is: what kind of future is your success pointing toward?

This piece offers a reflective framework for leaders and changemakers who want to build with intentionality—not just intensity.

👉 Read it here

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