A talk by Ted Johnson (JoinIn AI) arguing that AI should be understood primarily as an interface technology, not only an intelligence technology. It traces a recurring pattern in computing history—humans adapting to machines' protocols, from punch cards and command lines to prompt engineering—and asks how interfaces change when computers can reason, listen, speak, infer, clarify, and adapt. The speaker proposes that the next frontier is more human-compatible interfaces: systems that handle timing, attention, interruption, ambiguity, repair, shared context, and knowing when to stay silent. # Channel Presentation Observations ## Explicit Self-Description (Creator Claims) - **Speaker identity stated directly**: "I'm Ted Johnson, co-founder of Join an AI." - **Professional background claim**: "During my 25-year career building enterprise software, collaboration systems, and AI-enabled interfaces, I've always focused on human interaction." - **AI tenure claim**: "I've also been following AI for two decades, including back to the far less impressive GPT-1 and 2." - **Company mission claim**: The founding observation of JoinIn AI is framed as "why do we still have to learn AI? Why does something this powerful so often feel unnatural to use?" - **Field-convergence claim**: "We're seeing the field is converging on the same conclusion we built our company on. The interface has to stop being batch and start participating." - **Claimed company work**: "We're working on improving the protocol to the models. By giving it a better understanding of human and group conversation." (Followed by an unnamed product demo of group-conversation AI behavior.) ## Intended Audience - **Designers and engineers, explicitly addressed**: "here is what should bother us as engineers and designers as it's bothered and inspired me." - **Talk-format framing**: "I want to spend the next 20 minutes making prompting feel unfamiliar and strange again" and "Here's the path we'll take" indicate a prepared conference-style presentation. - **Meta-commentary on the medium**: "Most talks and videos cover how to use or adopt AI. The deeper question is how AI intelligence changes the interface" — positioning the talk against typical AI-adoption content, implying an audience saturated with such content. ## Claimed Scope / Positioning - **Thesis**: The prompt interface is a batch protocol inherited from punch cards — "the prompt is our present-day punch card." - **Three-part framework offered**: **channel** ("the physical transport that carries your intent"), **expression** ("the range and richness of meaning the channel can carry"), and **protocol** ("the shape or rules of this exchange"). - **Design philosophy advocated**: "if AI is for users, then we should obsess about maximizing the interface," and the reframed design question: "what burden are we still putting on humans only because the machine used to be too limited to carry that burden itself." ## Observable Presentation Patterns (Demonstrated) - **Structured lecture style**: Explicit agenda, historical analogies (keyboard patents ~1860, Hansen Writing Ball, loom origins of punch cards), and a closing mindset-shift summary. - **Third-party examples cited with attribution**: OpenAI ("GPT real-time 2 in late May... It backchannels now"), Nvidia ("This is Nvidia's Personal Plex, a research model, not ours"), Sherry Turkle at MIT ("Conversation is the most human and humanizing thing we do"). - **Product demonstration**: Multiple demo dialogues of an AI participating in multi-person conversation (labeling statements as "a question, a proposal, an answer" and taking turns), plus a co-founder anecdote about a "Frontier company's voice mode" misinterpreting "Hey Ted, come on in." - **Advocacy tone with user-empowerment framing**: "I want to say as clearly as I can, it is not our fault. We are not bad at using AI." - **Scholarly/citation habits**: Quotes researchers and distinguishes own product from others' ("a research model, not ours"), demonstrating care in attribution. # Delivery Observation: "The Prompt Is Still a Punch Card" — Ted Johnson ## Overall Tone & Register - **Conversational keynote style**: The speaker addresses a live audience directly ("most of you," "I want to spend the next 20 minutes," "Okay, let's make this concrete"). - **Reflective and slightly wry**: Humor appears early and is understated rather than punchline-driven. > "a little throbber cycled through clever gerunds like hullabalooing, tomfoolery, and philosophizing to hide the wait." - **Personal framing with credibility anchoring**: He establishes authority via biography. > "I'm Ted Johnson, co-founder of Join an AI. During my 25-year career building enterprise software..." ## Structure & Signposting Habits - **Explicit roadmap up front**: > "Here's the path we'll take to answer that. We'll start with... Then, I'll give you three key concepts." - **Numbered conceptual framework**, stated twice for emphasis: **channel**, **expression**, **protocol** — each introduced with a definition ("the physical transport that carries your intent," "the range and richness of meaning," "the shape or rules of this exchange"). - **Recurring "hold this idea" moves**, a recognizable teaching habit: > "Hold on to the word channel because here's the plot twist." ## Recurring Phrases & Rhetorical Patterns - **Repetition as emphasis** is the dominant verbal tic. Whole sentences repeat nearly verbatim: > "For the first time, you can say almost anything, the way you'd say it to another person." — appears twice within one passage. > "That's the second idea, expression. How much of what you actually communicate or mean will go through the the interface?" followed by a near-duplicate restatement. - **Anaphora / parallel lists**, used repeatedly: > "The human still decides what context matters, still remembers what to ask, still chooses the timing, still notices the ambiguity, still repairs the output, still has to carefully engineer a prompt." > "A keyboard is a channel. A microphone, channel. A screen, a punchcard, a prompt box, all channels." - **"Still" as a keyword** — used obsessively throughout ("still typing into a box," "still batch," "It's still batch," "still a box, still a submit button"), reinforcing the title thesis. - **Contrast pairs**: "It's not X, it's Y" constructions appear often: > "It's bandwidth, not the differences in the meaning." > "It's not our fault." > "Notice I said what it carries, not what it is." - **Memorable coinages / quotable labels**: "batch with interactive sprinkles," "Shorter batch is still batch," "We trade incantations. We've learned the magic words," "translation tax, a precision tax, context tax, repair tax." ## Teaching Habits - **Defamiliarization technique**, announced explicitly: > "I want to spend the next 20 minutes making prompting feel unfamiliar and strange again." - **Historical analogy as core pedagogy**: keyboard patents, Dvorak/Colemak, the Hansen Writing Ball, punch cards, and weaving looms are used to build the argument. - **Live demo narration**: Extended product demonstrations (Nvidia's "Personal Plex," his own company's meeting demo) are narrated line by line with commentary: > "Personal Plex stops. It yields. It picks the thread back up. That's real turn taking." - **Third-party citation** for authority: > "Sherry Turkle at MIT puts it very well. Conversation is the most human and humanizing thing we do." - **Closing takeaway framing**: > "Here's the mindset shift I want to leave you with." ## Emotional & Advocacy Register - **Direct reassurance to the audience**, an empathetic turn mid-talk: > "I want to say as clearly as I can, it is not our fault. We are not bad at using AI." - **Stated personal stakes**: he frames the talk's origin as personal ("actually surprising disappointment I couldn't shake," "that should bother us as engineers and designers as it's bothered and inspired me"). - **Vendor pivot near the end**: the talk shifts from analysis to product advocacy ("But we are. We're working on improving the protocol to the models"), with attribution hedging on the non-proprietary demo ("This is Nvidia's Personal Plex, a research model, not ours."). ## Fluency Notes (Uncertainty Flagged) - Occasional **disfluencies and apparent transcription artifacts** appear: "I say this is someone who loves keyboards" (likely "as someone"), "it's become interactive" vs. "that it become interactive," repeated "the the disappears," and a stray "[snorts]" marker. These are isolated moments, not a stable pattern, and may reflect transcription rather than delivery — **uncertainty is high on characterizing speech fluency from this transcript alone**. # Observation Record: "The Prompt Is Still a Punch Card" — Ted Johnson, JoinIn AI ## Speaker - **Ted Johnson**, co-founder of JoinIn AI, self-described 25-year builder of enterprise software, collaboration systems, and AI-enabled interfaces; usability-focused. ## Central Thesis - The prompt box is a modern punch card: not because of encoding, but because it forces a **batch protocol** — package a complete turn, submit, wait — onto an intelligence that no longer needs that constraint. ## Personal Origin of the Argument - When ChatGPT arrived, Johnson felt **amazement** followed by **surprising disappointment** he "couldn't shake." - This produced his founding question: **"Why do we still have to learn AI? Why does something this powerful so often feel unnatural to use?"** ## Framework: Three Concepts ### 1. Channel - The physical transport of intent: keyboard, microphone, screen, punch card, prompt box. - Channels differ in **bandwidth** (text = discrete symbols; voice = timing/pitch/hesitation; diagrams = spatial relationships), but carrying more signal ≠ machine understanding. - Key claim: **the channel never changed with AI** — still typing into a box. Only what's permitted through it changed. - Historical framing: the QWERTY layout traces to an ~1860 patent; the Hansen Writing Ball was an equally plausible alternative. **"Nobody alive chose it. We all inherited it."** ### 2. Expression - How much of what you mean the interface lets through. - Progression: assembly → shell commands → composable programming languages → **natural language**, which "blew the menu open." He calls this leap "real and enormous." ### 3. Protocol - The rules and shape of interaction — the part that **hasn't advanced**. - Punch-card batch history: encode everything in advance, submit, wait, read output, fix one thing, resubmit. The machine "never engaged with you while you were thinking." - **Prompting is the same protocol**: assemble, submit, wait, rephrase, resubmit. Interactive features are "batch with interactive sprinkles." Speed "fooled us into thinking it became interactive. It hasn't." - Voice input doesn't fix it: "Shorter batch is still batch." - He traces batch further back: it came from the **weaving loom** — set the whole pattern in advance, then run the cloth. ## On "Prompt Engineering" - Johnson's assessment: a set of rules for packaging batch, given a flattering name. - He frames prompt-engineering tricks (think step-by-step, give examples, "be an expert," paste more/less context, markdown documents) as **"incantations… magic words"** — an illusion of mastery comparable to knowing how to assemble a punch-card deck. - Preserved tension he acknowledges: "None of this means prompts are bad. Punch cards weren't bad… They're brilliant solutions for constraints of their time." - His concern: getting *good* at prompting black boxes "should bother us, not reassure us." ## User-Blame Argument - Humans still do all the interface labor: choosing context, timing, noticing ambiguity, repairing output. - When output fails, **people blame themselves**. Johnson's emphatic position: "It is not our fault… The mismatch isn't the user, it's the interface. In the race to enable AI, we shortcut the interface." ## Evidence and Examples ### Co-founder's voice-mode test - Asked "When is the next Timberwolves game?" — answered fine. Then said "Hey Ted, come on in" (not addressed to the AI); the model answered anyway ("Sure, I'm here") because the protocol has **exactly one slot**: your message, its reply. No concept of who is speaking or whether words were meant for it. ### Field convergence - **OpenAI GPT real-time 2** (late May, per Johnson): backchannels ("Mhm," "Right"). - **Nvidia PersonaPlex** (research model, attributed to Nvidia, "not ours"): demonstrates interruption, yielding, resuming threads, backchanneling. - Johnson's qualification on these: "Making listening noises is not really the same as knowing who's in the room." Current models aren't trained to tell that "Hey Ted" wasn't meant for them. ### JoinIn AI demo - Group conversation where the AI labels statements (question, proposal, answer), creates goals, and takes a turn only when no one else holds the floor. - Demonstrated: answering a question mid-meeting, holding a request ("AI, hold that"), summarizing a decision (expense approvals, second approver over threshold), asking for confirmation on an update ("Want me to update the requirement to a $10,000 threshold?"), and answering a room-availability question. - Johnson's claim: "No one wrote the prompt… the system was in the conversation, following it, understanding, and choosing its moment." - Caveat he notes: speaker-reference determination "was easy with a direct reference to the AI, but it will happen again without a direct reference." ## Closing Position - **AI is increasingly an interface technology**, not just an intelligence technology; book-smart models alone aren't enough. - Design question to ask: *"What burden are we still putting on humans only because the machine used to be too limited to carry that burden itself?"* - Answers aren't always chat, voice, or "a wall of markdown" — they're **human affordances**: a question, a pause, a sketch, a checklist, a quiet aside, "or saying nothing at all." Timing and modality choice should become the AI's job. - Historical framing: 75 years of humans adapting to machines created a **translation, precision, context, and repair tax**; AI is the chance to put those down — "not by making everything magical… not by replacing human judgment, but by making computers for once more fluent with us." - Cited quote: **Sherry Turkle (MIT)**: "Conversation is the most human and humanizing thing we do." # Speaker & Provenance Record — "The Prompt Is Still a Punch Card" ## Primary Speaker - **Ted Johnson** — **creator/presenter**; self-identified as "co-founder of JoinIn AI" (transcribed as "Join an AI"). Delivers the full 20-minute talk. - Cites a 25-year career in enterprise software, collaboration systems, and AI-enabled interfaces. - States he has followed AI for two decades, "back to the far less impressive GPT-1 and 2." ## Referenced / Quoted Third Parties - **Sherry Turkle (MIT)** — quoted: "Conversation is the most human and humanizing thing we do." Speaker attributes this to her. - **Ted's co-founder (unnamed)** — anecdote source for the voice-mode story ("When is the next Timberwolves game?"). Not a speaker in the video. - **OpenAI** — mentioned as releasing "GPT real-time 2 in late May" with backchanneling voice mode. - **Nvidia** — referenced for a research model the speaker calls "Personal Plex" / "Persona Plex" (explicitly "not ours"). Audio of that model's demo is played within the talk. - **Historical references**: QWERTY patent (~1860), Dvorak and Colemak layouts, Hansen Writing Ball, punch-card batch computing lineage from weaving looms, GPT-1/2, ChatGPT. ## Demonstrated Dialogue (AI Systems & Unnamed Participants) - Segments with `>>` markers show **pre-recorded demos**, not live guests: - Nvidia Personal Plex voice demo (diet/marathon; city murals). - JoinIn AI group-meeting demo with unnamed participants ("Sam," "Jordan," and others) plus an **AI assistant** responding to direct address ("AI, capture that for us"). ## Promotional Segments - **Flagged as promotional**: The latter demo block (group conversation, expense-approval workflow, room booking) showcases **the speaker's own company, JoinIn AI**, framed as "the difference between a smart machine behind the same old prompt and an interface that finally participates." The talk itself states the observation "started a company, Join an AI." - The Nvidia Personal Plex segment is explicitly disclaimed as "a research model, not ours." ## Availability Limits - No curated metadata was supplied; roles derive solely from the transcript. - **Comment availability**: unknown / not stated. - Co-founder and demo participants are unnamed; identity uncertainty remains explicit. \>\> I'm sure that sometime in the last few hours most of you did this. You typed a request into a small box to a super intelligence and then you waited. You watched the cursor blink, maybe a little throbber cycled through clever gerunds like hullabalooing, tomfoolery, and philosophizing to hide the wait. Maybe it gave you what you wanted, maybe you rephrased it and tried again. It all felt completely normal. I want to spend the next 20 minutes making prompting feel unfamiliar and strange again. I'm Ted Johnson, co-founder of Join an AI. During my 25-year career building enterprise software, collaboration systems, and AI-enabled interfaces, I've always focused on human interaction. I've also been following AI for two decades, including back to the far less impressive GPT-1 and 2. And when ChatGPT arrived, I felt two things at once. First, and unsurprisingly, amazement, knowing the world would never be the same, followed by actually surprising disappointment I couldn't shake. This disappointment turned into an observation that started a company, Join an AI, and that I keep coming back to, which is why do we still have to learn AI? Why does something this powerful so often feel unnatural to use? Here's the path we'll take to answer that. We'll start with the most familiar computer interface and make it strange again. Then, I'll give you three key concepts. The channel, the physical transport that carries your intent, expression, the range and richness of meaning the channel can carry, and the protocol, the shape or rules of this exchange. I'll use those three concepts to show you that the prompt is our present-day punch card. We'll share examples of the ways interfaces could progress, and we'll wrap with some practical advice for AI and human-centered design. Everyone knows what this is, the keyboard. It's everywhere and it feels completely normal or natural. But it isn't. We all had to take lessons. We all had to practice. And I say this is someone who loves keyboards. But it seems that we've been trying to fix them as long as they've been around. People have tried more efficient layouts like the Dvorak or Colemak to save their fingers some work. A more extreme example, some keyboard enthusiasts refuse to squander their two digits on the spacebar giving them four or eight or 10 keys to press with that efficient thumb of theirs. And what do we even mean by the keyboard? Here's the patent drawing for the layout we use every day. This patent's from about 1860. And my personal favorite, it just as could have easily been the Hansen Writing Ball, which looks anything but like a way you want to talk to a superintelligence. We carry these legacies of an arbitrary input device designed under constraints that haven't existed for a century. And we put it between ourselves and the most capable machines ever built. Nobody alive chose it. We all inherited it. And then we stopped noticing. So, that's the first idea, the channel. The medium an interface gives you to work in. A keyboard is a channel. A microphone, channel. A screen, a punchcard, a prompt box, all channels. And channels matter because each one can physically carry a different kind of signal. For example, text is a stream of discrete symbols. Voice can carry timing, pitch, hesitation, and words. A diagram can carry spatial relationship all at once. But these are differences in what the medium can transmit. It's bandwidth, not the differences in the meaning. And carrying more signal isn't the same as the machine understanding \[snorts\] any of it. That's a separate question. That's the next idea. Humans use all these channels constantly without thinking. We never pick one channel and force everything through it. That would be absurd. And yet, that's what we ask people to do with machines over and over. Hold on to the word channel because here's the plot twist. With AI, the channel never really changed. You're still typing into a box, but what you were allowed to push through it was about to. For the first time, the computer channels carry rich, complete human language. Notice I said what it carries, not what it is. You're still typing into a box, you're still hitting the submit button. The keyboard didn't change. What improved is the range of what you're permitted to express through it. There's the second idea, expression. How much of what you actually communicate or mean will go through the interface? That's the second idea, expression. How much of what you actually communicate or mean will the interface let through? Here's an example of expression progress over time with computers. Starting with assembly, which gave you an instruction set, a few dozen opcodes. Then came the commands with the shell inputs, flags. Then modern programming languages gave you primitives that you could compose. While powerful, step-by-step, each one of these is a fixed vocabulary, requiring you to express your intent by choosing from a menu the machine will accept. Natural language blew that menu open. For the first time, you can say almost anything, the way you'd say it to another person. And on the expression one axis, the leap is real and enormous. There's an ocean of meaning in an ordinary human request, context, nuance, intent, all things we've never had to spell out to each other. For the first time, you can say almost anything the way you'd say it to another person. For the first time, a machine can take it in. So, here is what should bother us as engineers and designers as it's bothered and inspired me. The channels for computers have been the same for 15 years, some 180 years. Now, with AI, we've poured an ocean of expression into it. So, why does it feel like we're still sipping through a straw and struggling to learn how the AI thinks? Because there's a third idea underpinning the other two, and it's really the one that hasn't kept up, the protocol. The rules you follow and the shape of the interaction itself. Channel stayed the same. Expression exploded in the last 3 years with LLMs, but the protocol, prompting, is the protocol of a punch card. And the punch card's protocol is good old batch. Here's what punch card batch meant. You sat down away from the machine, carefully encoded your entire request in advance, carried your deck to the operator, you submitted the job, and then you waited, sometimes hours, sometimes overnight. Then you read the printout, found one thing that was wrong, fixed it, resubmitted it, and waited again. The machine never engaged with you while you were thinking. It engaged with the finished package after the fact. Now, let's look at the prompt. Assemble the whole request, submit it, wait, read what comes back. Something's off, assemble it again, submit it again, and wait. We have to acknowledge that there are features, interactive features improving this. You can ask for updates, you can ask for summaries of what was done. But in the end, it's still batch with interactive sprinkles. It's the same protocol. We shrank the wait time from overnight to a few seconds or few minutes, and the speed fooled us into thinking that it become interactive. It hasn't. It's still batch. You still package a complete turn before the machine is allowed to participate. We learn tricks, send tips to use code skills, or rewrite prompts a certain way to manage this. And speaking doesn't change it. Your voice just gets transcribed into the box and submitted. Shorter batch is still batch because the protocol is the part that did not advance. The protocol is the part we've had to learn. We just gave it a flattering name. We call it prompt engineering and treat it like it's a power user skill. Strip the label off and it's a set of rules for packaging up good old batch. For example, tell it to think step-by-step. Give it examples. Ask it to be an expert. Don't ask it to be an expert. Don't ask it that way. Paste more context. Paste less context. Only talk to it through markdown documents. We trade incantations. We've learned the magic words. That's the illusion. It feels like mastery, but it's the same sort of mastery a punch card operator had. Knowing exactly how to assemble the deck so the job wouldn't fail. Moreover, we've gotten good at prompting or these black boxes. And and that's the part that should bother us, not reassure us. None of this means prompts are bad. Punch cards weren't bad. Command lines aren't bad. They're brilliant solutions for constraints of their time. But that's the whole question. Is batch still the right protocol? Are we still pre-packaging our intent for a machine that no longer needs us to because it shouldn't need us anymore? It can ask a follow-up. It can clarify mid-thought. It can notice it's missing something and say so. It should be human conversational. Sherry Turkle at MIT puts it very well. Conversation is the most human and humanizing thing we do. It's one of humanity's superpowers. The capacity to engage and think is right there. And yet, we're still making people submit the deck and wait for the run. Even the punch card inheritance protocol in fact, batch came from the weaving loom. You set the whole pattern in advance, then ran the cloth. The punch card got reused on computers by default. We're still standing at the same moment again. AI could finally meet us in the middle of a thought, got handed a protocol of a loom. That's what I mean. The prompt is still a punch card, not because of how you encode it. The encoding is powerful and awesome. Because when the LLM is allowed to engage only after you've packaged a complete turn and submitted it. And this is where the mismatch bites. Model capacity is shooting straight up. Reasoning, speech, vision, memory, planning, all curving upwards. The interface protocol, flat. Still a box, still a submit button, still the human doing all the work around the LLM. The human still decides what context matters, still remembers what to ask, still chooses the timing, still notices the ambiguity, still repairs the output, still has to carefully engineer a prompt. But the intelligence feels magical. It's the interface that still feels like work. And when it feels like work, when the output's wrong, when the magic words don't land, people blame themselves. They decide they're bad at this. They're not specific enough. They don't get AI. I want to say as clearly as I can, it is not our fault. We are not bad at using AI. We are being asked to operate a brand new kind of intelligence through a protocol of a punch card. The mismatch isn't the user, it's the interface. In the race to enable AI, we shortcut the interface. Okay, let's make this concrete and familiar. A few weeks ago, my co-founder was using a Frontier company's voice mode. These are known as speech-to-speech models. He asked it a normal question, "When is the next Timberwolves game?" Fine. It answered it quickly. Then, he pretended I showed up as if to speak to me and said, "Hey Ted, come on in." He wasn't talking to the AI. But these models have no way to know that. So the AI did the only thing a prompt box can do. It took his speech as a turn and answered it. "Sure, I'm here. What's on your mind?" That's not a good answer, but it's not a dumb model. It answered the first question perfectly, but it's a protocol with exactly one slot. Your message, then it's reply. It has no concept of who's speaking, whether the words were even meant for it. And the frontier companies want to make strides as well. OpenAI released GPT real-time 2 in late May and started trying it for their voice mode as well. It backchannels now. It goes, "Mhm." and right. The little sounds we make to show we're listening actively. We're seeing the field is converging on the same conclusion we built our company on. The interface has to stop being batch and start participating. Others are working on real-time conversation as well. This is Nvidia's Personal Plex, a research model, not ours. Watch what happens when it gets interrupted. \>\> I've been thinking about starting a diet. \>\> Yeah, starting a diet can feel a bit daunting, but you could keep it simple. Focus on eating more veggies and fruits. Try to \>\> Before I forget, I signed up for a marathon. \>\> All right, congrats on signing up for the marathon. That's a big challenge. You've got a lot of time. Focus on building a solid base with regular long runs. Stay hydrated. Make sure you fuel right before and after, and don't forget to stretch and take care of your feet. \>\> Personal Plex stops. It yields. It picks the thread back up. That's real turn taking, listening and speaking at once in real time. \>\> You need to come visit me. \>\> Oh, okay. \>\> go into the city. \>\> Okay. \>\> Cuz that's the thing, like there's like the random spray paint, but then there's also like I'm not sure. People must commission them. Like these massive like mural spray paint pieces. \>\> Yeah, I think they do. \>\> And Persona Plex's back channels listens and lands where a person's would. Beyond conversational flow, there are lots of challenges and it's a complex problem. Making listening noises is not really the same as knowing who's in the room. These are not trained to tell that "Hey Ted" wasn't meant for it. But we are. We're working on improving the protocol to the models. By giving it a better understanding of human and group conversation. \>\> Good afternoon, everyone. \>\> Good afternoon, Sam. \>\> Hi, Jordan. \>\> Good afternoon. Good to see you both. \>\> Quick one, which requirement is this? Do we have an ID? \>\> This is our Q442, expense approvals. \>\> There, it answered a question. It only takes actions based on the utility-driven model. So, it creates goals to fulfill as it labels each of the participants' statements as a question, a proposal, an answer, and then only takes a turn when no one else is speaking or holding the floor. \>\> Right, we need users to approve requests faster. \>\> Yeah. The approval flow's too slow. \>\> What kind of requests, though? \>\> Expense approvals first. Access requests eventually. \>\> AI, hold that. \>\> Actually, let's pause. Expense approvals or a general approval workflow? \>\> Expense approvals first release. \>\> Access requests are future scope. \>\> That changes the data model. Good to know. \>\> AI, pull that up for everyone. \>\> Tracking determining who's the speaker is referring to is critical. In this case, it was easy with a direct reference to the AI, but it will happen again without a direct reference. \>\> Oh, I'd forgotten that was a rule. \>\> So, over 5,000 needs a second approver? \>\> Yep. Manager plus finance. \>\> So, a big one can't be a single tap. \>\> Right. Over the limit, it routes to a second approver. \>\> Agreed. Under five, one tap's fine. \>\> Works for me. \>\> Okay. Agreed. Expense approvals, 5,000 threshold. \>\> Right there, the AI resolved the scope objective. No one wrote the prompt, no one patched the turn and hit submit. The system was in the conversation, following it, understanding, and choosing its moment. \>\> AI, capture that for us. \>\> First release supports expense approvals only. Access requests are out of scope. Managers can approve or reject an expense right from a notification. And per the finance controls policy, anything over $5,000 routes to a second approver? \>\> Actually, make the threshold 10,000, not five. \>\> Want me to update the requirement to a 10,000 threshold? \>\> Yes. \>\> AI, is this room free after the meeting? \>\> Let me check. The room looks free until 3:00, but yes, it's yours until 3:00. \>\> And that's the difference between a smart machine behind the same old prompt and an interface that finally participates. Here's the mindset shift I want to leave you with. AI is not just an intelligence technology. It's increasingly becoming an interface technology. And if so, then book smart models alone are not enough. Stop picturing AI as a smarter machine hiding behind prompts, agents, loops, and all the old paradigms. We have to start seeing intelligence itself as a thing that can finally remove interface constraints and amplify human potential. For 75 years, humans adapted to the machine. It's syntax, it's forms, it's timing, it's batch. A system that can reason, listen, infer, adapt should be able to meet us partway, if not all the way. Instead, if AI is for users, then we should obsess about maximizing the interface. So, then the design question changes. It needs to become what burden are we still putting on humans only because the machine used to be too limited to carry that burden itself. Ask that question, and the whole interface space opens up. The answer isn't always chat. It isn't always voice, and not a wall of markdown. It's definitely not a decade-old set of digital constructs. The right answer is the affordance humans already use with each other. Communication, a question, a pause, a sketch, a checklist, a quiet aside, or saying nothing at all. An interface where timing and modality aren't the humans' job anymore. Where choosing the right channel at the right moment is done by the AI. And as a usability person, this is the part that excites me the most. When you take that burden off people, the the disappears and adoption follows. Computing has mostly been about to date improving how humans encode their intent for machines. The punch card, type a command, click a menu, use your thumb on an iPhone, write a prompt. Every step was progress and every step carried the old constraint forward into the next era. A translation tax, a precision tax, context tax, repair tax. AI is our chance to put those down. Not by making everything magical. Not by making everything voice. Not by replacing human judgment, but by making computers for once more fluent with us. Most talks and videos cover how to use or adopt AI. The deeper question is how AI intelligence changes the interface. Human conversation is the most human thing we do. Because if a machine can finally understand more of what we mean, then we can and should stop reshaping ourselves to be understood by it. Thank you.