InVideo AI: Future of Ads, or Slop at Scale?

InVideo just dropped a campaign that might be one of the sharpest AI ads to date. Or one of the most controversial.

Not because the ad itself is “good” or “bad.” But because of what it demonstrates.

The premise is simple. A local business wants awareness and local footfall. A single prompt arrives. Then a “creative team” appears on screen. A writer, director, producer, and sound designer. They brainstorm, storyboard, pull assets, debate tone, change direction midstream, swap narrators, land a punchline, and ship a finished promo.

The twist is that the “team” is not human. It is AI agents collaborating in real time.

Some people will see this and think: finally, creativity at the speed of thought. Others will see it and think: here comes manufactured content. At industrial scale.

So let’s unpack what’s actually happening here. Not the hype. Not the fear. The shift.

What this campaign is really showing

On the surface, it’s a product story.

Under the surface, it’s a proof-of-concept for a new production model. Prompt-to-video, orchestrated by role-based agents, pulling from your assets, and iterating like a team would.

That matters because we are crossing a line:

  • Yesterday: AI helped you edit.
  • Today: AI can generate components.
  • Now: AI attempts to run the full production loop. Brief to concept to execution to polish.

If that sounds incremental, it isn’t. The bottleneck in content has never been “ideas.” It has been translation. Turning intent into something shippable, on brand, on time, and fit for a channel.

This is what changes. The translation cost collapses.

The “agents” idea. Why it clicks so hard

Most AI video tooling gets described as features: text-to-video, voiceover, stock replacement, templates.

Agents are a different mental model. They mimic how work gets done.

Instead of one tool trying to be everything, you have multiple role-based systems that divide the labor:

  • Writer: Hook, script, narrative beats
  • Director: Framing, pacing, scene intent
  • Producer: Assets, structure, feasibility, assembly
  • Sound designer: Voice, music cues, timing, emphasis

The output is not just “a video.” It’s a workflow that looks like collaboration.

And that’s why the campaign is sticky. It doesn’t just show a capability. It shows an operating model.

Fast definition. What “AI agents” means in this context

AI agents are role-based AI workers that take responsibility for a portion of the task, coordinate with other roles, and iteratively refine toward a shared goal.

In practical terms, this is orchestration. Task decomposition. Decision loops. And multi-step iteration that feels closer to a real production process than a single prompt and a single output.

In enterprise marketing teams, agentic video tools compress production time while making governance, briefing quality, and brand standards the real constraints.

Why the bakery storyline matters. It’s not about video

The reason this lands is the bakery.

A small business is a stand-in for every team that has historically been excluded from “premium” creative production. Not because they lacked ideas, but because they lacked:

  • Budget
  • Time
  • Specialist talent
  • Access to production infrastructure

If AI production becomes cheap and fast, a new baseline emerges.

Customer expectations tend to move in one direction. Up.

In other industries we’ve seen this pattern repeatedly:

  • Shipping went from weeks to days. Then days to “why isn’t it here tomorrow?”
  • Support went from office hours to 24/7 chat.
  • Information went from gatekept to instant.

Content is heading the same way.

When a local business can generate credible, channel-ready creative quickly, the competitive advantage shifts away from “who can produce” and toward “who can differentiate.”

So is this the future of content. Or a shortcut that kills creativity?

Both outcomes are plausible, because the tool is not the strategy.

Here are the three trajectories I think matter.

1) Creativity gets unlocked for more people

AI reduces the friction between an idea and a first draft. That can empower founders, small teams, educators, non-profits, internal comms teams, and marketers who have always had the brief but not the bandwidth.

If you’ve ever had a good concept die in a doc because production was too heavy, you know how big this is.

The upside version of the future looks like:

  • More experimentation
  • More niche creativity
  • More localized storytelling
  • Faster learning cycles

2) The internet floods with “content wallpaper”

When production becomes cheap, volume spikes. When volume spikes, attention gets harder. When attention gets harder, teams chase what performs. When teams chase what performs, sameness creeps in.

The downside version of the future looks like:

  • Infinite mediocre ads
  • Homogenized pacing and tone
  • Interchangeable visual language
  • “Good enough” content dominating feeds

That’s the fear behind “slop at scale.” Not that content exists. That it becomes meaningless.

3) Premium creative becomes more premium

There is a third outcome that’s often missed.

When baseline production becomes abundant, true differentiation becomes rarer.

Human advantages do not disappear. They concentrate around the things AI struggles with reliably:

  • Strategy and intent. What are we trying to change in the market?
  • Cultural nuance. What does this mean here, with these people?
  • Original point of view. What do we stand for that others don’t?
  • Brand taste. What is “on brand” beyond templates?
  • Ethical judgment. What should we not do even if we can?
  • Lived insight. What’s the human truth behind the message?

In that world, AI does not replace creative leaders. It raises the bar on them.

The practical question every marketing leader needs to answer

People debate whether AI can “replace creatives.” That’s not the operational question.

The operational question is: Where do you want humans to be irreplaceable, and where do you want machines to be fast?

Because if AI handles production, your competitive edge moves to:

  • The quality of your briefs
  • The clarity of your brand system
  • The strength of your POV
  • The governance of your outputs
  • The measurement of creative impact
  • The speed of iteration without brand drift

A simple maturity test you can run this week

If AI can produce at scale, the risk is not “bad videos.” It’s unmanaged systems.

Ask this:

Who owns the continuous loop of prompting, testing, learning, scaling, and deprecating AI-driven creative workflows in your organization?

If the answer is “no one,” you don’t have an AI capability. You have scattered experiments.

My take

Production is getting cheaper. Differentiation is getting harder.

So the real decision is not whether you can generate more content. It’s whether you can scale output without losing taste, brand truth, and accountability.

Is this the future of content. Or a shortcut that kills creativity? It depends on who owns the brief, who owns the guardrails, and who is willing to say no.


A few fast answers before you act

Can AI agents replace a creative team?

AI agents can replicate parts of the production workflow and speed up iteration. They do not automatically replace strategy, taste, or cultural judgment. Those still require accountable humans.

What does “prompt-to-video” actually mean?

Prompt-to-video is the ability to turn a single idea into a finished video. Script, scenes, voice, music, edit, and formatting. Without traditional filming or manual timeline editing.

Will this create more generic ads?

It can, especially when teams optimize for speed over differentiation. The antidote is strong briefs, clear brand constraints, and human ownership of taste and intent.

Vibe Bot: AI Meeting Assistant With Memory

At CES 2026, I am seeing a familiar pattern. Earlier AI bot ideas are returning with a new coat of paint, powered by stronger models, better microphones, better cameras, and much tighter product positioning.

Razer’s Project AVA is one example. It reads like a modern update of the “companion in a box” category, echoing Japan’s Gatebox virtual home robot from 2016. Think less novelty bot, more designed product, with better sensing, better personalization, and clearer use cases.

And then there is Vibe Bot. It is not a “robot comeback story” in the literal sense, but it does feel like a spiritual successor to Jibo. The social robot pitched for the family back in 2014. Same emotional shape. Different job to do. This time, the target is the meeting room and the problem is continuity.

What is Vibe Bot?

Vibe Bot is an in-room AI meeting assistant with memory. It captures room-wide audio and video, generates transcripts and summaries, and supports conversation continuity by carrying decisions forward so meetings do not reset every week.

What Vibe Bot is trying to own

In other words, it is meeting intelligence plus decision logging, packaged as AI hardware built for real rooms.

  • Capture meetings with room-wide audio and video
  • Generate speaker-aware transcripts, summaries, and action items
  • Track decisions and surface prior context on demand
  • Sync with calendars and join Zoom, Google Meet, or Teams with minimal setup
  • Connect to external displays and pair wirelessly as a camera, mic, and casting device

This is not just meeting notes. It is a product trying to own the layer between conversation and execution. The strategic bet is continuity. Less rehashing, fewer resets, more forward motion.



What I find strategically interesting is that:

  1. Hardware is back in the AI conversation. We went from bots, to apps, to copilots. Now we are circling back to room-based systems because the capture layer matters.
  2. Context is the moat. Summaries are table stakes. The defensible value is continuity over time, across people, decisions, and follow-ups.
  3. Meeting tools are becoming workflow tools. The winners will connect decisions to action, not just document what happened.
  4. Privacy is now a product feature. If a device sits in a room, trust is part of the user experience, not a compliance footnote.

Vibe Bot fits a broader CES 2026 pattern. AI agents are evolving from chat windows into systems that live where work happens. In this case, the bet is that the meeting room becomes a persistent context engine. If this category gets it right, teams will spend less time reconstructing the past and more time executing the next step.

If Vibe succeeds, it becomes a small but important building block of a contextual AI workspace where teams can retrieve “what we decided and why” on demand. More product info at https://vibe.us/products/vibe-bot/


A few fast answers before you act

What is Vibe Bot and what problem does it solve?

Vibe Bot is an AI meeting assistant designed to capture, remember, and surface context across meetings. It addresses a common failure point in modern work: decisions and insights get discussed repeatedly but are rarely retained, connected, or reused.

What does “AI with memory” actually mean in a meeting context?

AI with memory goes beyond transcription. It stores decisions, preferences, recurring topics, and unresolved actions across meetings, allowing future conversations to start with context instead of repetition.

How is this different from standard meeting transcription tools?

Most meeting tools record what was said. Vibe Bot focuses on what matters over time. It connects meetings, tracks evolving decisions, and helps teams avoid re-litigating the same topics week after week.

Why is memory becoming more important than note-taking?

Knowledge work has shifted from isolated meetings to continuous collaboration. Without memory, teams lose momentum. Memory enables continuity, accountability, and faster decision-making across complex organizations.

What risks should leaders consider with AI meeting memory?

Persistent memory raises governance and trust questions. Teams must define what is remembered, who can access it, how long it is retained, and how sensitive information is protected. Without clear rules, memory becomes a liability instead of an asset.

Where does an AI meeting assistant deliver the most value?

The highest value appears in leadership forums, recurring operational meetings, and cross-functional programs where context is fragmented and decisions span weeks or months.

What is a practical first step before rolling this out broadly?

Start with one recurring meeting type. Define what the AI should remember, what it should ignore, and how humans validate outputs. Measure whether decision velocity and follow-through improve before scaling.