YouTube Shorts
YouTube Shorts from Skillion AI Labs with quick takes on practical AI, workflow design, and venture building.
Shorts
A speaker on the show compares flying a helicopter to a summit with climbing there on foot. The clip asks what is lost when you skip the steps that lead to a result.
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Terence Milstead argues that management skills will be the most important thing to develop as people start using AI tools. He draws on a year-long AI adoption study he says he co-directed with OpenAI across government agencies.
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Pete Cooper talks about the mental fatigue of working with AI tools and why it can be more draining than older ways of working. He suggests a daily limit to avoid burning out.
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Aleksandr Tynianov, CTO at Krasty Soft, gives his view on what AI changes for development teams. He argues that one developer who uses AI well can replace a couple of people, but warns against throwing anything into an LLM and expecting it to work.
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A speaker on the show compares finding a local business through search rankings with simply asking an AI chatbot. The example is a plumber needed this week for a leaking sink.
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Therese Grittner explains why she worries that local businesses will be drowned out as large corporations spend heavily on AI advertising. She uses a search for the best cheeseburger nearby to show what is at stake.
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SEO expert Therese Grittner explains why local businesses need websites built for AI-assisted search. She argues that sites should answer the problems of people in their service area, and that one-page scrolling sites tend to rank lower.
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A speaker on the show argues that robots will not replace people's need for human interaction for a long time. The clip also looks at where solopreneurs without a team find that interaction.
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A speaker responds to Elon Musk's picture of a future where robots do all the work. The clip explains why the speaker expects people's need for human interaction to hold for a long time yet.
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Pete Cooper shares that the channel is closing in on 100,000 YouTube views and explains what the show sets out to do. He asks viewers to subscribe, comment and get in touch if they want to appear on the podcast.
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Pete Cooper compares push prompting, where you hand AI a problem to fill in, with pull prompting, where you let it ask you questions. He explains why he thinks the second approach produces better answers.
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Frank Gonzalez explains why a Stripe account, an LLC and a marketing channel are enough to start a business without investors. He describes how offering an unsolicited redesign won him his first graphic design client, who he says he still works with today.
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Pete Cooper walks through a $650 billion figure he attributes to J.P. Morgan and compares it with what the biggest AI companies bring in. He explains why he sees the gap as a real risk for the industry.
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Pete Cooper explains why he thinks electric aviation has reached a turning point and where AI fits in. He also invites people working in the field to come on the podcast as guests.
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Pete Cooper and a college student compare the monthly cost of a human tutor with paying for AI subscriptions. They put a tutor at around $500 a month against about $40 for both Claude Pro and ChatGPT.
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A guest with no coding background describes how Claude Code and some basic Python let her build things she can sell. Pete Cooper adds his own story of building and deploying a Wheel of Fortune game in about five minutes.
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A guest on the show describes simple AI workflows, from automated client follow-ups after a meeting to AI tools that check code. The clip covers how these save business owners time and money.
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Pete Cooper runs through Anthropic's Fable 5.1 release and how he says it relates to the earlier Mythos model. He covers his claims about benchmarks, unattended running and lower running costs.
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Pete Cooper names context as the one word that sums up the AI trend of the last few years. He explains what context means in practice and how different LLMs handle it.
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This short makes the case for paramedics flying eVTOL aircraft to emergencies and landing close to the patient. It explains why the speaker thinks the aircraft are cheap and simple enough to make that work in the first critical minutes.
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Pete Cooper explains the idea of an AI super app, one chat interface that ties many separate apps together. He sets out why he expects it to make computers easier to use for people at any skill level.
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Pete Cooper explains why busy CEOs struggle to find time to learn AI and how Skillion's one-hour AI Level Up is meant to meet them at their level. He gives two examples of colleagues who he says found ways to save hours of work.
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Pete Cooper walks through a one-hour session in which he says he helped a colleague save his company $10,000 a year. He explains the steps, from checking current AI ability to cutting a weekly reporting task down to minutes.
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A short exchange on how AI changes the familiar inbox rules for forwarding and filtering. It explains why adding an AI agent on top of a fixed rule means the output can vary.
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Arthur Ketchel explains why an AI tool working over years of personal notes needs to know which ones still matter. He wants answers about today drawn from current notes, not from records made ten years ago.
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Pete Cooper explains where vibe coding fits in a Scrum or Agile development cycle and where it stops being enough. He argues that quick demos still need a planning step before they can become a product.
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Pete Cooper lists the problems he keeps seeing in code produced quickly with today's AI tools. He explains why he treats those tools like a junior developer and brings in experienced help before production.
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A speaker explains how an AI agent can pull client information from different apps into one place. The example is a business owner who wants the full context before calling a client, prospect or lead.
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Marcos Lima describes how much of his work now depends on AI and why he expects that dependence to spread. He says that without it he could not keep up with the newsletters and other projects he runs.
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Two speakers look at where AI voice agents and automation can take pressure off service businesses. The examples are a massage therapist who has to stop work to answer the phone and a busy reception desk.
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Pete Cooper explains what vibe coding is good for and which parts of software development it leaves untouched. He sets out why a quick AI-built prototype is still a long way from a production product.
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Marcos Lima explains why he thinks today's cheap AI prices will not last. He points to AI companies spending money they do not have and says customers will end up paying the real bill.
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Pete Cooper explains why he believes voice-driven AI chat is coming to cars and how it could change a drive. He walks through an example trip where the car finds an address, suggests a stop for a gift and re-routes on request.
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Pete Cooper answers a question he gets asked often: are consumer AI chatbots like ChatGPT and Claude going to get more expensive? He argues retail prices will stay roughly where they are, and explains who he thinks will end up paying the real cost instead.
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Alice Bull describes a lead-generation dashboard she built for a UK construction company that pulled six separate data sources into a single ranked view. It is a concrete look at AI-assisted prospecting built on public data rather than a bought list.
AI for Lead Generation: Construction Company Success Story #shorts
Pete Cooper explains Jevons' paradox, the 1865 observation that more efficient steam engines burned more coal rather than less, and applies it to AI. It is a short, clear answer to why falling AI costs are pushing resource use up instead of down.
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Pete Cooper picks up a guest's figure of an 83% downturn in the tech industry and argues the cause is not less code being written, but fewer developers writing more of it. He then names the data-heavy fields he expects the same pressure to reach next.
AI Marketing Revolution: Solopreneurs' New Go-To Strategy #shorts
Pete Cooper makes a quick case that AI is no longer an emerging story in business but the dominant one, using company valuations to make the point. He closes by spelling out what a unicorn actually is.
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Alice Bull explains why she chooses to work with solopreneurs and small businesses on AI: you can see the difference you have made far sooner than inside an enterprise. She contrasts that with a large US financial services firm where layers of hierarchy slow everything down.
Solopreneurs: Why Small Businesses Thrive with AI #shorts
A guest argues that AI has made him more productive but has also generated more work rather than more free time. Pete Cooper picks up the thread with The Jetsons, and the flying car and one-button job that never arrived.
AI: More Productive, But More Work? #shorts
Pete Cooper answers whether AI has a place in generating content, and lands on a qualified yes. His argument is that AI-made content earns its place as a genre of its own, and that the problem starts when it imitates a real person.
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AI is reshaping hiring by changing which roles disappear and which new roles emerge. This short asks whether businesses are prepared for that workforce shift.
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https://www.youtube.com/watch?v=YxXw2nj5M4U
AI Content vs. Human Connection: The Future of Marketing #shorts
Python combines rapid development with a large ecosystem of AI libraries, making it a practical choice for building and scaling AI automation.
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This short traces how a punk-rock festival inspired Liquid Death's unconventional approach to branding canned water.
Liquid Death: The Craziest Brand Marketing Strategy Ever? #shorts
Retrieval-augmented generation connects AI to current company knowledge so its answers can be more accurate without retraining the model.
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RAG can reduce AI hallucinations by grounding answers in retrieved information, but unclear or conflicting sources still require human verification.
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Large language models can answer confidently even when they are guessing. This short explains why AI output should support, not replace, human judgement.
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