Toby Daniels, Author at ON

The Infinite

Zuck

How Mark Zuckerberg Became a Shape-Shifter Across Culture—and What That Tells Us About Power Now

Editor’s Note: While everyone was focused on Zuck's coffee habits and his vision for AI companionship, Toby was focused on his code-switching.

Mark Zuckerberg gave three interviews this week. One to Dwarkesh Patel. One to Theo Von. One to Ben Thompson.

Three hosts. Three audiences. Three different cultures of attention.

And somehow, three different versions of the same man.

With Dwarkesh, Zuck was the architect—carefully explaining the inner workings of LLaMA 3, scaling challenges, the logic of open source, and why infra is destiny. A version of Zuckerberg that speaks to the developer class with surgical calm. Less ambition, more constraint. Less metaverse, more compute.

With Theo, he got weird. Not just funny-weird. Existential-weird. He talked about coffee, jiu-jitsu, whether AI can be your friend, and what it means to feel overwhelmed by the world. For a guy who once wore the same grey shirt for a decade, he seemed surprisingly alive here. Vulnerable, even. A human dad, not a techno-overlord.

With Ben, he went back to strategy mode. Threads. Messaging as the new platform layer. Apple’s walled garden. The arc of Meta from feeds to frictionless business tools. This was Zuckerberg as systems analyst, reflecting not just on what Meta is doing, but on what it failed to do. “We just didn’t prioritize the developer ecosystem,” he says, with the tone of someone who won’t make that mistake again.

Same man. Same week. Entirely different presence.

This isn’t a coincidence. It’s choreography.

Toby Daniels

Co-Founder, ON_Discourse

Power Is No Longer Singular

We used to think of tech founders as having a “core identity.” Jobs had design. Bezos had logistics. Zuck had scale.

But identity doesn’t work that way anymore. In a media landscape where your audience is fragmented, your persona has to fragment too. Zuckerberg is showing us what that looks like in real time. He’s not broadcasting one version of himself to everyone. He’s customizing presence for context.

This isn’t about authenticity. It’s about fluency.

Zuck doesn’t need you to like him. He needs you to recognize him—as credible, legible, and aligned with your values, at least for the duration of the interview. He’ll talk parameter tuning with Dwarkesh, moral complexity with Theo, business model compression with Ben. None of it is fake. But all of it is performative.

He is, in this sense, the first post-founder founder. A man who no longer builds for the internet, but performs on top of it.

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So What?

Because this isn’t about Zuckerberg. Not really.

It’s about how power morphs in a world of narrative collapse. When no one voice can dominate, the only ones left standing are those who can slip between voices. Who can code-switch—not just linguistically, but existentially. Zuckerberg isn’t just changing the story. He’s changing who tells it.

The platform once defined the founder. Now the founder becomes the platform.

What’s Next?

He’s done the intellectual web. He’s done Americana surrealism. He’s done strategy’s back room.

So what comes next?

Spirituality? Therapy culture? Gen Z moral philosophy?

Don’t be surprised if you see him on a podcast about neuroplasticity. Or debating Harari on cognition. Or sliding into Twitch streams with creators half his age. Not because he has something to prove—but because he knows that staying still is the surest way to disappear.

That’s the play. Zuckerberg isn’t repositioning the company. He’s reprogramming himself.

He’s testing personas like features. Shipping them like updates. Measuring feedback in trust, not just clicks.

Final Thought

If Musk is trying to be a meme, Zuckerberg is trying to be a mirror.

And maybe that’s the scarier thing.

Because a meme can be ignored. A mirror makes you look back.

And right now, Mark Zuckerberg is reflecting something we might not want to admit: the future belongs to those who can move between worlds without ever claiming one as home.

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What If

Jack Dorsey

Is Right?

The case for abolishing intellectual property in an age of infinite remix.

Toby Daniels

Co-Founder, ON_Discourse

The Provocation

Jack Dorsey recently suggested that all intellectual property laws should be abolished. It sounded absurd at first — another high-profile provocation in an era full of them. But what if it’s worth considering?

In a recent ON_Discourse group chat, a media strategist, two IP attorneys, a founder of an AI venture studio, and other members of our community gathered to confront the uncomfortable possibility: in a world where AI can remix art, code, identity, and likeness infinitely, does intellectual property (IP) still serve the purpose it was designed for? Or has it devolved into a protection racket for legacy power?

"The code of IP law doesn’t map to the code of the internet."

The Icebreaker: What Should Be Liberated?

We began, as we always do, with an icebreaker: What piece of culture deserves to be stolen, remixed, or liberated from its owners?

One IP attorney nominated memes: “They’re designed to be shared, but still fall under copyright gravity.”
A media strategist called for Superman to enter the public domain now rather than waiting for a slow expiration timeline.
The founder of an AI venture studio advocated for software code, the infrastructure upon which future culture is increasingly built.
A brand protection attorney argued for liberating technologies subsidized by public investment — such as SpaceX and foundational AI models.

The common thread: the lines between ownership, creation, and collaboration are being obliterated.

Rethinking the Purpose of IP

As the conversation deepened, the tension between old frameworks and new realities became clear.

You can’t abolish IP with a single stroke. It’s a system of many different protections — each serving a different purpose.

IP Law Isn’t One Thing

One legal voice reminded us: abolishing "IP" isn't a coherent position. Copyright, patents, trade secrets, and trademarks exist for distinct reasons. Reform must be nuanced, not reactionary.

The Internet Broke the Old Rules

Our media strategist observed that traditional IP law was designed for physical goods, not the infinite replicability of the internet. In the online world, engagement — not scarcity — drives value.

Ownership Models Are Misaligned

Another participant framed it sharply: today's cultural production demands participation models, not protectionist ones. Yet our legal structures still assume a single author and a static object.

Law as Infrastructure, Not Obstacle

The IP lawyers in the room pushed back on the notion that IP laws are inherently barriers. Every open-source license, every permissive API agreement, every blockchain-based contract — all rely on IP frameworks to exist in the first place.

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The New Creative Dilemma

We explored a tangible example: imagine rogue creators producing hundreds of thousands of Pirates of the Caribbean spinoffs using AI.

At some point, Disney won't be able to send takedown notices fast enough. They'll have to rethink the entire system.

Should Disney issue a hundred thousand takedown notices?
Or should it accept the reality of AI proliferation and build code-based mechanisms — watermarking, blockchain revenue splits — to harness this creative chaos?

The consensus: it will be both. Litigation where necessary. Monetization where possible.

The Platformized Future

Several participants outlined a likely future where large media companies behave more like platforms than studios. Instead of guarding IP fiercely, they would create APIs, encourage derivative works, and share revenue with creators who participate in expanding their universes.

Gatekeeping is a losing strategy. Participation is the new moat.

Imagine an official Marvel API: fans creating their own characters, building micro-stories, and selling digital merchandise — all governed by smart contracts that ensure original creators share in the upside.

Critical Pushbacks

While the conversation was provocative, it was not naïve.

Abolishing IP outright would be like defunding the police — provocative, but terrible policy.

Abandoning IP entirely would be reckless. IP is flexible — it needs reinterpretation, not eradication.
Engagement ≠ Value. As one strategist reminded, "On the internet, IP has marginal value. But engagement without ownership can quickly become a race to the bottom."
Synthetic content will flood the internet, and discerning quality, originality, and human authorship will become even more essential — and difficult.

What Happens Next

We are entering an era where a single piece of creative work could have thousands of contributors, while attribution, compensation, and credibility may be managed by blockchain, not the courts. Companies that open their IP to remixing — and build mechanisms to share value — will dominate those who cling to legacy ownership models.

Consumers will flock to the content they can co-create, not just consume.

We run events every week. If you want to participate, inquire about membership here. If you want to keep up with the perspectives that we hear, you can subscribe to our weekly newsletter.

Duolingo

is

Solving

Small

in AI

Transformation

Less Moon Shots, More Operations

Toby Daniels

Co-Founder, ON_Discourse

Editor’s Note: This morning, Duolingo CEO Luis von Ahn sent a company-wide email declaring a major shift: Duolingo is now officially an AI-first company. What follows is Toby’s real-time reaction to this news.

At first glance, this might seem like another predictable headline in a year overflowing with "AI transformations." But having interviewed countless CEOs in the build-up to our AI Transformation Summit, this one deserves closer attention—especially if you're sitting in the C-Suite.

The Solve Small Mindset

At ON_Discourse, we’ve been pushing a counterintuitive idea: AI transformation doesn’t start by rewriting your 5-year plan—it starts by solving small. The companies that win will be the ones that treat AI like compounding interest: tiny, workflow-level improvements that snowball into massive strategic advantage.

Duolingo isn’t "experimenting" with AI. They are operationalizing it. They are choosing small—automating manual tasks, enabling micro-innovations (like AI-led video tutoring), and re-allocating human talent toward what machines can’t do: building better experiences.

Whether you agree with their decision, or what impact this might have on whether they will need to hire human instructors in the future, I think this is important for a number of other reasons:

Strategic Parallel

CEO Luis von Ahn compares this pivot to Duolingo’s 2012 mobile-first bet—the move that fueled their meteoric rise.
They're signaling that AI isn't an add-on; it's foundational.
They’re reorganizing workflows, slashing contractor work that AI can replace, and embedding AI literacy across hiring, reviews, and team structure.

Workflow-Level Disruption

This is not top-down transformation theater. It’s bottom-up operational rewiring.
AI is showing up in the places where friction actually lives: content creation, tutoring, customer experience.

If you're a C-Suite leader still treating AI like a moonshot or a lab experiment, you're already behind. This move by Duolingo is both a warning shot and a blueprint.

AI transformation isn't about spectacle. It's about sweat.

Not about scale first. About solving small first.

Not about replacing people. About freeing them to do meaningful, creative work.

The question isn’t if you will adapt. It’s whether your operational DNA will let you.

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We run events every week. If you want to participate, inquire about membership here. If you want to keep up with the perspectives that we hear, you can subscribe to our weekly newsletter.

The Internet

is

Being

Rebuilt

You Just Haven't Noticed Yet

Gen Z isn’t coming for the internet. They’re rebuilding it from scratch. And most of us haven’t noticed.

Editor’s Note: Our Network Has Range — And That’s Our Edge.

Yes, our members bring decades of experience. They’ve shaped industries, navigated disruptions, and steered through hype cycles that most people only read about. That depth is our advantage. But even the sharpest minds risk repetition if they only talk to themselves.

So here’s our first deliberate attempt to break pattern. Not because we’re stale—but because staying sharp means letting in noise, disagreement, and the unexpected. This is how we stretch, and how we stay vital.

Every now and then, a conversation hits differently, flips your expectations, and reframes how you think about the future.

That happened during a recent group chat when two teenage AI builders joined a session with a handful of founders, technologists, and operators. They weren’t there to be interviewed. They were there to discuss their relationship to AI.

Here is the answer: they are too busy building to have a relationship with AI. They are already deploying. Already iterating. Already outpacing the roadmap most of us are still trying to draw.

What followed was less of a panel and more of a live feed from the future.

We’ve been talking for months about “who’s building what.” What became clear in this moment was that the most compelling builders might not be in your network yet. They might not be pitching VCs. They might not even be out of high school.

But they are building. Faster than you think.

Toby Daniels

Co-Founder, ON_Discourse

They Build Without Permission

The first signal wasn’t what they were working on. It was how.
There was no talk of accelerators or incubators. No LinkedIn-friendly “I’m thrilled to announce…” posts. Just an explanation of how one of them reverse-engineers Upwork job listings to generate MVPs in minutes using AI tooling—and sends a finished product with their pitch before anyone else even replies.

Here’s how they described it:  “I find people on Upwork who describe what they want. I feed it to an AI coding platform. It builds the project. I record a Loom video showing it working. Then I send it with my proposal.”

What sounded like a hustle was, in fact, a paradigm shift. A redefinition of what it means to be a product builder. Not someone dreaming about solutions—someone shipping them before the request is even accepted.

We often say, “Move fast and break things.” They move faster. And they’re not interested in breaking anything. They’re too busy building what’s next.

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Their Stack Changes Every Day

Ask how they’re learning and the answer isn’t courses or curricula. It’s real-time community—Twitter, forums, Discord, tutorials, builder blogs.

“I follow people who build in public. If they show a tool, I try it. If they use a method, I copy it. Then I break it. Then I rebuild it.”

The developer stack that used to take years to learn is now learned through vibe and repetition. Cursor, Lovable, Vercel, Superbase—these are not platforms they’re discovering. They’re the default environment.

What they lack in credentials, they make up for in cadence. What they lack in polish, they replace with speed.

They don’t care about enterprise readiness. They care about whether it works. Whether it ships. Whether it scales to the next test.

They Think About Trust Very Differently

We asked a basic question: What platforms do you trust for information—Google, Reddit, TikTok, or ChatGPT?

One answered:
“I don’t really trust any of them. They’re all collecting data. You just have to use a few, cross-check everything, and rely on some common sense.”

Then came this line, casually delivered and absolutely unforgettable:
“If there’s anyone you should trust the least, it’s yourself.”

This wasn’t cynicism. It was a working theory of intelligence. A worldview shaped by systems thinking, fast iteration, and feedback loops. They trust outputs only as far as they can validate them—and that includes their own.

This is not a generation raised to believe they’re right. It’s a generation raised to test their assumptions. Repeatedly.

They Are Rewriting the Internet

When asked to imagine what the internet will look like in ten years, one of them didn’t hesitate.

“You won’t need the internet. You’ll just talk to your own AI assistant. Like Jarvis. It will do everything for you.”

Not a prediction. A prototype. They’re already building around this idea—browserless agents, custom assistants, interfaces built on prompts, not clicks.

Where older generations grew up navigating websites, this generation is replacing that cognitive framework entirely. They’re not refining the internet—they’re redesigning it.

And what they imagine feels far less like a user interface and far more like an extension of themselves.

They’re Building Games as Funnels and Writing Code as Culture

One of the more surprising moments came when one shared a recent project: a simple browser game tied to a current event. Fast-paced, made in Cursor, deployed in a day.

What made it interesting wasn’t the gameplay. It was the logic behind it.

“After people play the game, I ask them to enter their email to be added to the leaderboard. That’s how I grow the list for my newsletter.”

It was a loop: event > game > lead capture > distribution > repeat.

It wasn’t a startup. It wasn’t even a product. It was a funnel disguised as fun, and a signal of how deeply embedded systems thinking has become in how they build—even when the projects feel light, fast, or playful.

This Is Already Happening. Now What?

These moments add up to a clear truth: the next version of the internet isn’t being debated on panels or whiteboarded in boardrooms. It’s being built by a generation that doesn’t see the old structures as sacred.

They are building agents, not apps.
They are deploying experiences, not websites.
They are moving through rapid, recursive loops of experimentation and iteration.
And they are doing it without waiting for a job title, an invite, or a budget line.

That’s not something to fear. That’s something to learn from.

If we want to understand where the internet is going, we have to look outside the usual circles. The next generation is already inside the machine, modifying the blueprint, rewriting the rules—and doing it faster than we think.

They aren’t just using AI. They’re shaping it.
They aren’t just consuming the internet. They’re rebuilding it.
And if we’re paying attention, we can meet them there.

We run events every week. If you want to participate, inquire about membership here. If you want to keep up with the perspectives that we hear, you can subscribe to our weekly newsletter.

The
Gartner Hype Cycle is Over

We Need a New Paradigm

Editor’s Note: One of the most common references we hear in all of our events is due for a take-down. Our co-founder Toby does his best work when he’s fired up. Check it out and let us know if he is leading you into a 'trough of disillusionment' or a 'plateau of provocations.'

The Gartner Hype Cycle has become the go-to cliché in tech. Not only does this lead to boring, useless perspectives, but it also relies on an outdated research methodology that makes no sense.

To the uninitiated, the Gartner Hype Cycle is a 30-year-old research method that tracks tech adoption over time. Despite its title, the hype cycle is an undulating line (not a cycle) that tracks emerging ideas from the so-called “innovation trigger” through the “peak of inflated expectations” beyond the “trough of disillusionment” and up the “slope of enlightenment” until it reaches the “plateau of productivity.”

If this sounds like the narrative arc of a hero’s journey, that is because the hype cycle is based on fiction. You can read about its origins from its creator here. It was designed to help organizations time and calibrate their investment in developing technology accordingly. The key idea: Early adopters should expect a dip in enthusiasm — the so-called trough of disillusionment — requiring patience and capital that will eventually work its way up into the plateau of productivity.

To be clear, I’m not here to take down Gartner. They successfully leveraged an unprovable narrative arc as a brilliant marketing tool for their services. It’s like a technology horoscope that sounds right 65% of the time. It served its purpose, but now we’re dealing with something bigger.

AI transformation is bigger than hype. It is a super trend. If we plot it on a linear curve, we are obscuring much more interesting considerations.

To navigate AI transformation, leaders need a framework that embraces uncertainty and complexity. I’ve spent the past year interviewing dozens of AI leaders and innovation experts. Their thoughts and observations fit into three states of AI transformation: Possible, Potential and Proven.

Here’s how to envision these three states and the one essential provocation leaders should be asking themselves:

Toby Daniels

Co-Founder, ON_Discourse

First State:

Things That Are Possible

This is AI’s realm of imagination, where ideas spark but haven’t yet materialized into practical use cases. Theoretical concepts like Artificial General Intelligence (AGI) exist here. Think Hal from 2001. AGI will have human-like cognitive ability.

OpenAI’s focus on AGI reflects the company’s ambition to tackle problems that are decades, if not centuries, away from being solved. And IBM’s neuromorphic computing explorations into brain-inspired chips like TrueNorth aim to mimic human cognition. These chips promise transformative computing capabilities but remain highly experimental. In the First State, research papers and experimental algorithms dominate the landscape, and progress is measured in breakthroughs, not revenue.

If you’re investing here, are you betting on the future or indulging in a fantasy?

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Second State:

Things That Have Potential

Here, AI technologies are emerging from academic journals and proof-of-concept stages into early-market experimentation. They’ve demonstrated value but haven’t yet reached a point of reliability or scalability. This is where buzzwords often overshadow results, and visionary leaders must decide how to allocate resources to support fragile ideas.

Tesla’s full self-driving vehicles exemplify this potential. They function in controlled scenarios but remain far from delivering consistent, regulatory-approved results.

Stripe fraud detection with AI, which uses AI to detect and mitigate fraud in online payments, also lives in the Second State. While effective, it requires continuous refinement to adapt to new threats and remain reliable at scale.

Are you willing to nurture fragile, early-stage innovations, or are you only here for the immediate return on investment?

Third State:

Things That Are Proven

This is AI’s gold rush — the domain of scaled, reliable technologies delivering measurable value. Companies here have turned AI from a speculative bet into a fundamental driver of their operations and profits.

Amazon’s AI-driven recommendation algorithms are foundational to the company’s success, influencing customer purchases and optimizing logistics.

Siemens’ predictive maintenance systems in manufacturing ensure operational efficiency, reducing downtime and saving billions annually. And UPS’ AI-optimized logistics, dubbed On-Road Integrated Optimization and Navigation or ORION, optimizes delivery routes in real time, saving millions in fuel and time.

Will you leverage these for the present while building for the future? Or will you let comfort become your cage?

Three Challenges AI Leaders Must Accept

Being an AI leader capable of handling the three states of AI transformation isn’t about frameworks, acronyms or decks.It’s about who you are amid ambiguity, pressure and doubt.

The most productive conversations I’ve had over the past year have involved the three components below. If you drive AI transformation in your enterprise, ask yourself:

1. Can you embrace being wrong?

Leading AI means making calls with incomplete information. You will fail. The question isn’t whether you’ll stumble — it’s whether you’ll learn fast enough to stay in the race. When was the last time you admitted a mistake to your team? If you can’t remember, you’re already in trouble.

2. Are you ready to rethink your identity?

You’re not a decision-maker; you’re a decision-shaper. Your role isn’t to control outcomes — it’s to create environments where great outcomes emerge.How often do you let your team’s experiments challenge your instincts?

3. Can you manage fear — yours and theirs?

AI is a pressure cooker. It amplifies anxieties about jobs, ethics and the future. You can’t outsource courage to a playbook. Leadership means stepping into those conversations, not avoiding them.Have you addressed your team’s fears about AI — or have you assumed their silence means support?

This moment requires breaking out of the regimented ways of thinking. Much like music, classical leadership thrives on precision and control. What’s needed is jazz leadership, which thrives on responsiveness and improvisation. AI’s pace means leaders must constantly adapt to new rhythms and riff on emerging opportunities.

Expecting AI to follow the classical path of the Gartner Hype Cycle will only lead you into a cacophony of failure.

We run events every week. If you want to participate, inquire about membership here. If you want to keep up with the perspectives that we hear, you can subscribe to our weekly newsletter.

AI

TRANSFORMATION

Solve Small

and

Think Like

a Founder

Don't fall for the big talk

Editor’s note: Our Co-Founder has developed this perspective about AI transformation after hearing countless talks about the so-called AI revolution. We think it’s such a good break from the conventional approach to AI adoption that we organized a summit around it.

Artificial Intelligence is sold to the C-suite as transformation at scale—a revolution in business, a redefinition of the workforce, a paradigm shift. Every AI keynote, whitepaper, and corporate summit emphasizes “epic transformation,” the kind that reshapes industries overnight.

But here’s the truth: AI transformation rarely happens in a single leap. Instead, it evolves through a series of incremental, often messy, small-scale shifts. And it’s in those smaller moves—often overlooked in corporate case studies—where AI’s true impact is being felt. This is the Solve Small approach: focusing on targeted, bottom-up AI interventions that remove inefficiencies while preserving the human touch where it matters most.

This is where AI transformation mirrors the way great founders run their companies. Conventional business wisdom says that scaling an organization requires distributing decision-making, adding layers of management, and diffusing control. Yet, the most effective founder-led companies—like Apple, Airbnb, Shopify, and Nvidia—reject this model. Instead, they remain deeply involved in the details that matter, ensuring that speed, adaptability, and clarity drive their organization forward. AI transformation requires the same approach: high-touch, iterative, and deeply embedded within the business.

Toby Daniels

Founder, ON_Discourse, former Chief Innovation Officer, Adweek, Founder and Chair, Social Media Week

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The Problem With Epic AI

The way we talk about AI inside boardrooms is broken. The discourse is full of sweeping, cinematic narratives—AI will “reinvent how we work,” “unlock human potential,” and “create limitless efficiency.” Yet, this kind of hype obscures the real work required to integrate AI successfully.

Consider the C-suite executive who leaves an AI conference with visions of radical automation, only to return to an organization struggling with basic data hygiene. Or the startup founder promising a fully autonomous AI-powered workflow, only to realize that employees don’t trust AI-generated insights. The gap between expectation and execution is vast because the AI discourse favors spectacle over substance.

This is why AI transformation should be approached like a founder running their company—not through bureaucratic committees and abstract strategies, but through direct involvement, rapid iteration, and a relentless focus on solving small, meaningful problems.

Thinking Like a Founder: AI Transformation in Small, Impactful Steps

The best founder-led companies thrive because they embrace hands-on decision-making and fast, iterative improvements. AI adoption should follow a similar model. Here’s what that looks like in practice:

Solve Small: Incremental Change That Compounds Over Time

Great founders don’t overhaul their entire organization overnight; they make continuous, strategic adjustments. AI transformation should follow the same principle. The most effective AI-driven businesses treat AI like compounding interest—small investments that build on each other:

A sales team starts with AI-assisted meeting transcriptions, then layers in automated CRM updates, and later integrates predictive sales forecasting.
A manufacturing plant implements AI for maintenance logs, extends it to predictive downtime prevention, and eventually integrates it into supply chain optimization.

Like a founder iterating on product development, AI transformation isn’t about flipping a switch—it’s about stacking small improvements until they create something larger than the sum of their parts.

AI as a Bottom-Up, Ground-Level Initiative

The best ideas don’t always come from leadership—they emerge from people closest to the work. Founder-led organizations like Nvidia empower employees at every level to share insights directly with leadership. AI adoption should work the same way:

A call center rep starts using ChatGPT to summarize support tickets before management even considers AI integration.
A junior designer leverages AI-generated layouts to speed up work, improving both quality and output.
A coder uses AI-assisted debugging not because leadership mandated it, but because it’s simply faster and more efficient.

AI initiatives should mirror the Solve Small model—where leadership listens, learns, and scales what works, rather than imposing AI from the top down.

AI as a Fast, Iterative Process

Founder-led companies don’t rely on long planning cycles. Airbnb’s Brian Chesky eliminated unnecessary layers of management and engaged directly with product teams to make faster, better decisions. AI transformation should follow the same principle:

A legal team pilots AI contract review with one clause at a time rather than automating the entire process at once.
A retail company A/B tests AI-generated product descriptions for a subset of SKUs before rolling it out across the catalog.
A logistics firm implements AI-driven route optimization for a single delivery region before expanding nationwide.

Successful AI adoption moves at the pace of iteration, not perfection.

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AI as Local, Not Just Enterprise-Wide

Not every AI innovation needs massive cloud infrastructure. The most impactful AI-driven improvements happen at the local level—on an individual’s laptop, phone, or department-specific system:

A doctor using AI for voice-to-text medical notes on their own device, rather than a hospital-wide AI integration.
A journalist using AI summarization locally for research without relying on centralized editorial AI mandates.
A salesperson using an AI-powered meeting assistant that operates on their phone, rather than waiting for IT to implement a corporate-wide AI tool.

AI as Specific, Not Broad

Great founders don’t try to do everything at once. They focus on solving one problem exceptionally well before expanding. AI transformation should be approached the same way:

AI for one type of document scanning (e.g., invoices) works better than trying to automate all document types at once.
AI in one language model per department (e.g., legal vs. marketing) avoids generic, diluted results.
AI that refines a single metric (e.g., reducing customer service handle time) often outperforms AI designed to “optimize” an entire workflow.

AI as an Invisible, Seamless Part of Work

Founder-led companies prioritize clarity—teams work best when they know exactly what to focus on. AI should operate the same way: it should be so seamlessly integrated that it disappears into the workflow.

AI-powered email filters reduce spam and prioritize important messages.
AI-driven search ranking surfaces better results without users thinking about it.
AI-enhanced writing suggestions feel like part of the workflow, not a separate tool.

The Future of AI Is Solve Small—And Founder-Driven

Big AI transformation stories will always dominate headlines, but in reality, the organizations that win will be the ones that think like great founders—staying hands-on, moving fast, and solving small, again and again, until the transformation is undeniable.

If business leaders want to “go big” on AI, they should start by solving small—and staying directly involved every step of the way. This is why AI transformation should be approached like a founder running their company—not through bureaucratic committees and abstract strategies, but through direct involvement, rapid iteration, and a relentless focus on solving small, meaningful problems.

Interested in attending the Summit? Learn more and request an invitation here.

Don't

Listen to Us,

Listen to

Them

Announcing our first round of summit speakers

Request an invite to attend or inquire about partnering with us.

Learn more

Editor’s note: After 18 months and hundreds of events, conversations, and activations, we curated a group of executives who have a real story to share about practical AI transformation. These are the people who are solving small for the enterprise.

No one comes to an ON_Discourse event to hear me or anyone on my team speak. We are provocateurs, not pontificators. We leave that to the real experts - the executives who are building, leading, doing hard things that will move markets. This type of expert is hard to find in the AI era.

These experts are driving meaningful AI adoption at the enterprise level. To do this, they abandoned epic and hyperbolic AI theories in favor of practical, immediate investments that improve business outcomes today. We call that solving small, and we organized a summit around their stories.

And on March 26, we are going to provoke them into telling their story in a new way. I am excited to share our first round of confirmed speakers for the Solve Small ON_Discourse Summit on AI Transformation.

Setting the stage with the Solve Small mindset—why small, tactical shifts lead to big impact.

Dan Gardner

Co-Founder and Executive Chairman, Code and Theory, ON_Discourse

The rise of Agentic Managers and what they mean for the future of management, leadership, and productivity.

Katherine von Jan

Founder, CEO, Tough Day

How a global marketing team is integrating AI, not just experimenting with it.

Don McGuire

CMO, Qualcomm Incorporated

The AI guardrails every business needs—and why the entire C-Suite needs to get on-board.

Mark Howard

President, COO, TIME

In the coming days and weeks we will announce more provocateurs, agitators, builders and makers who are driving enterprise level transformation from the bottom up.

What to expect at The ON_Discourse Summit

This is not another AI conference filled with high-level platitudes. ON_Discourse is designed for those leading AI transformation inside the enterprise—across functions, across teams, and across the C-suite.

Sharp, provocation-driven keynotes that move beyond theory and into action.
Small-group discussions designed to generate practical, real-world strategies.
A cross-functional approach that goes beyond AI as a tech initiative to AI as a business transformation tool.

Why Solve Small?

Big AI transformation stories dominate the headlines, but the most meaningful change happens at a smaller scale:

Small is implementable—today, not next year.
Small is iterative—it can fail, adapt, and evolve.
Small is tangible—it moves beyond theory into action.
Small is powerful—because when compounded, it leads to massive transformation.

Request an invite to attend or inquire about partnering with us.

Learn more

Request an invite to attend or inquire about partnering with us.

Learn more

Scenes from a
Nashville dinner

There’s No Way  I’m Getting a  Neuralink Implant,

Toby Daniels

Founder, ON_Discourse, former Chief Innovation Officer, Adweek, Founder and Chair, Social Media Week

Unless...

EXPLORE ISSUE #006

Editor’s note: This comes from a private dinner event in Nashville. The room was instantaneously skeptical of Neuralink surgical implantation which was interpreted as a challenge by our co-founder. Underneath all of that skepticism was a lot of fear and curiosity. Would you do it?

This post was written by human Toby Daniels and narrated by AI Toby Daniels (powered by Wondercraft.ai).

0:00 / 0:00

“Do you think you would have a chip like Neuralink implanted in your brain in your lifetime?”

This was a question that someone asked at a recent ON_Discourse private dinner.

The room was filled with C-Suite business leaders, investors, a music industry exec and former professional athlete, and the answer was unequivocally, no, apart from two people, myself included, who said, without question, yes.

10 minutes later, and after an impassioned debate, most of the people who said no, changed their answer. But why?

Context

Before I explain, let’s understand the technology.

Neuralink is a technology developed by a company aiming to build a direct interface between the human brain and computers. The technology uses extremely thin wires, much thinner than a human hair, which can be inserted into the brain. These wires have electrodes that can detect brain activity and send signals. Neuralink’s technology is designed to bridge the gap between the brain and digital world, potentially enhancing human capabilities or treating neurological disorders.

It’s worth noting that Neuralink is an Elon Musk company, which comes with baggage. So, the question needed to be reframed slightly:

“Would you have a brain-computer interface (BCI) implanted in your lifetime?”

Ok, back to why people changed their minds, in less than 10 minutes.

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Use case

We’ve spent a considerable amount of time talking to leading experts for the Internet 2025 Living Issue. One prevalent theme emerged.

Every past technology interface has been insufficient in how we interact with it. Each technological step we take, we look back and scoff at the inadequacies of what came before.

Remember the quill, the pen, the typewriter, the keyboard, mouse, touchscreens, remember swiping?

“Hey Siri!!” How stupid was voice?

But BCIs? Bending technology to your will, with a single thought? How do you improve upon that? How could it possibly get more seamless, integrated and non-intrusive? Think of the possibilities!

We have become slaves to our smartphones. Count the number of people on a train, walking across the street, at a restaurant with friends, driving a car for fucks sake, who are NOT also on their phones?

You’re telling me you would prefer to live a life where you are tethered to your screen for large parts of the day? You’re happy to half listen and be only a little bit engaged with whoever is sitting across from you? You’re ok that all of this is making you sick?

Remember the quill, the pen, the typewriter, the keyboard, mouse, touchscreens, remember swiping? “Hey Siri!!” How stupid was voice?

So, it’s still a no?

“Life moves pretty fast. If you don’t stop and look around once in a while, you could miss it.” ― Ferris Bueller

Just to be clear, you will never trust a technology that can interpret the brain’s activity and help control devices externally? What if it was FDA approved? What evidence do you need that the technology is safe, your data protected, and that you would be finally untethered from the insufferable weight of your smartphone?

Dude, what if you can also control your TV with your brain? You’d never have to spend time looking for the remote, ever again!

Still a fat no? WTF.

Ok, last question. What if you were a quadriplegic and the device would allow you to regain some, perhaps even all of your motor functions? Would the gift of extra mobility convince you?

Just to be clear, everyone here would accept a pacemaker, right? Electrical impulses inserted directly in your heart chamber is a yes, but a BCI

No?

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Oh, yes, you said yes?

One last question. Earlier you said, no, especially not Neuralink. You don’t trust Elon. Elon’s bad. He’s evil? What if Neuralink was the only option?

Yes?

I’m sorry, it sounded like you said yes, but I couldn’t hear clearly due to the indignity in your voice.

Just to be clear, you would get Elon’s chip implanted into your brain if it meant you freely and fluidly interface with computers again??

Alright, so context matters.

The good news is that Elon is not the only company working on this, so this might end up being a false choice. BrainGate, Kernel, Openwater, Emotiv and others are all pioneering in this space and while it might take a few more years of clinical trials before government approval, it seems inevitable that the ultimate UI, one that we control with our brains, is going to happen in our lifetime and most of us will get one, not just because of the edge use cases, but because technology always wins, whether you like it or not.

The

Shift from

Knowledge

Work

to

Direction

Work

Toby Daniels

Co founder ON_Discourse

Sorry, Not Everyone

Can Be a Director

Dan Gardner

Founder & Exec Chair of Code and Theory & Founder, ON_Discourse

The worst piece of advice you could give today to a college freshman hoping to work in tech is to tell them to major in computer science, math, or engineering. Same for coding, which is about to go from being a surefire way into the industry to immaterial.

Automation has been replacing manual labor for decades and now artificial intelligence is ready to take over the bulk of knowledge work. We are on the precipice of a great shift that will drastically change which workers will be the most valuable recruits.

Knowledge workers who, at the turn of the century, were described as the most important workers within a modern, thriving organization, will be replaced by what we’re calling direction workers. The evolution in technology isn’t so much going to eliminate high-end human jobs, it’s going to change what high-end human jobs look like and require.

But the shifts in these sectors do not just show AI replacing human skills. They show a need for a new kind of human skill set. This is where the direction worker comes in.

For the last 60 years, knowledge work has been used to describe a kind of intellectual work that demands a high degree of specialization or training, and the ability to perform non-routine tasks like problem-solving, analysis, decision-making, and the creation of new information. Knowledge workers were the upper crust of all white-collar workers: financial analysts, architects, lawyers, data scientists, and engineers.

That was before.

Across many disciplines, knowledge work is already being replaced. In the financial sector, AI systems are able to analyze vast amounts of data and make sophisticated investment decisions. In healthcare, AI systems are able to diagnose medical conditions and recommend treatments with a high degree of accuracy. AI systems don’t take days off; they do not call in sick. They can work 24 hours a day.

But the shifts in these sectors do not just show AI replacing human skills. They show a need for a new kind of human skill set. This is where the direction worker comes in.

I use “direction” not so much to convey the management work of a director in a company but more to refer to the literal act of directing, as in instructing or conducting. It could just as easily be called “Instruction Work.”

The image of an orchestra conductor comes to mind, expertly guiding musicians and instruments to produce the right sound. The image of a NASCAR driver may even be more appropriate. The engine may be beautiful, but it won’t win the race without the expertise, the direction, of its driver.

In finance just as in healthcare, human workers are needed to provide direction to AI systems even as they are no longer required to crunch the data themselves. On the tail end, human workers also need to evaluate the results, use critical, lateral thinking, and offer follow-up instructions.

Ferenc Huszár, a machine learning professor at the University of Cambridge, tweeted last year that the current version of OpenAI’s large language model, ChatGPT, would be a good teaching tool in mathematics, precisely because its answers are sometimes wrong. “Give it a problem, it generates convincing-looking but potentially bullsh*t answer, ask the student if they are convinced by the response,” he wrote.

What Huszár is suggesting here to me is not just teaching students to simply produce an accurate answer, but to develop an ability to go past the appearance of a fact and, with a critical eye, evaluate whether it is indeed accurate. If it isn’t, that eye needs to figure out why not, edit the original question, and do it all over again.

As systems progress, there may be less need for correction and editing, but the need for direction will not disappear. Ever-improving technologies will only call for more excellent direction.

Where to find and how to train these direction workers then becomes the question.

I am not sold on telling young people to just go to business school. Sure, we need a good generation of leaders who understand how to manage this new landscape, but more than managers we need critical thinkers who can ask the right questions, look for blind spots, understand connections, and have the creativity and humility to rethink the problem at its end and its base.

Direction workers are likely going to be people who can juggle different skill sets all at once: dual majors in math and anthropology PhDs who have trained quantitatively and qualitatively, journalism majors who work with Python, law school graduates willing to engage with the practicalities of coding and ethics. In short, we are going to need what David Epstein called “generalists” in his best-selling book, Range.

Realizing that to be competitive in the marketplace in the next ten years is going to look totally different than it did in the last ten is not just something young professionals need to do. Those of us in business should also be paying attention: The biggest cost to businesses over the next decade will be hiring the wrong people with the wrong skill sets.

As Max Penk put it in a post on LinkedIn earlier this year: