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- Ep 693: AI Without the Jargon: The Language Every Business Leader Needs in 2026
Ep 693: AI Without the Jargon: The Language Every Business Leader Needs in 2026
ChatGPT brings apps and GPTs together, Microsoft Launches Elevate for Educators, Wikipedia Confirms Licensing Deals With Major AI Companies and more
Everyday AI
January 16, 2026
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🎙 Daily Podcast Episode: AI is no longer a mystery, but the language around it still is. Closing that vocabulary gap is becoming a core leadership skill for 2026. Give it a watch/read/listen.
🕵️♂️ Fresh Finds: Google may bring Gemini and Agentflow together, ChatGPT's memory improves, Google’s new translation AI and more. Read on for Fresh Finds.
🗞 Byte Sized Daily AI News: ChatGPT brings apps and GPTs together, Wikipedia strikes huge AI deal, Microsoft's push in AI and education, and more Read on for Byte Sized News.
💪 Leverage AI: Companies aren’t falling behind in AI because they lack tools. They’re falling behind because they lack a common understanding of what those tools can do. Here’s how to fix it. Keep reading for that!
↩️ Don’t miss out: Did you miss our last newsletter? We Covered: OpenAI and Anthropic closer to IPO, Microsoft Moves Toward $500M Anthropic Investment, and more Check it here!
Ep 693: AI Without the Jargon: The Language Every Business Leader Needs in 2026
The AI gap will kill companies.
What is it?
it's the large divide between AI's crazy impressive capabilities and what most companies are actually using them for.
And one of the biggest reasons for the AI gap?
Talking.
Like... no one understands how to talk about AI because the technology changes faster than Usain Bolt in Beijing.
You wanna talk to your AI team about LLMs?
PFT. They're running Ralph Wiggum loops in Claude Code and just kinda reading the code before it hits production.
Yeah, the divide is WIIIIIDE.
So we're gonna tackle it together on the second volume of our Starter Series: AI Without the Jargon: The AI Language Every Business Leader Needs to live by in 2026.
Also on the pod today:
• AI jargon slowing business? 🛑
• Agentic models make decisions 🤖
• Hallucinations = AI confidently lying 😳
It’ll be worth your 32 minutes:
Listen on our site:
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Here’s our favorite AI finds from across the web:
New AI Tool Spotlight – Stracti lets you build intelligent bots for any game, Waylight remembers everything you see on your computer — meetings, documents, websites, messages — and can chat about it anytime, Clodo Executes your outbound ideas at the speed of thought
Optimind Released — OptiMind turns your written problem notes into math models in seconds. Curious how it works?
ChatGPT Apps for Business — Workspace apps in custom GPTs are now in beta for Business accounts. Curious how live data and automation work behind the scenes?
ChatGPT Chat Memory — ChatGPT now remembers your past chats—find out what’s changed.
TranslateGemma Released — TranslateGemma delivers top-notch translation in 55 languages, all with smaller, faster models. Curious how?
OpenAI x Merge Labs — OpenAI just invested in Merge Labs to make brain-AI tech real. Ready for mind-controlled gadgets?
Raspberry PI AI — Raspberry Pi’s AI HAT+ 2 runs local AI models, but the regular Pi 5 might outperform it. Find out why.
Gemini AI — Google is testing bringing Gemini for Business and Agnetflow together.
1. Custom GPTs Now Tap Live Data with Workspace Apps 📊
OpenAI is rolling out a beta feature for select Business accounts allowing custom GPTs to integrate approved workspace apps, enabling real-time access to documents, calendars, and business data without manual uploads or custom coding. Admins control app availability, and GPT creators can selectively enable apps within their bots, which then use these tools seamlessly in conversation—no special commands needed.
This move replaces the older Actions system, restricting GPTs using apps to internal company use only and emphasizing workspace security. The rollout is gradual, so many users may not see this capability immediately, marking a significant step toward smarter, workflow-integrated AI assistants.
2. Microsoft Unveils Major AI Push for Educators 👩🏫
Microsoft just rolled out "Microsoft Elevate for Educators," a new initiative aimed at helping teachers worldwide harness AI and professional development resources as classrooms evolve in the AI era.
The announcement, timed with the run-up to Bett UK 2026, includes new AI-powered tools like Teach in Microsoft 365 Copilot, a Study and Learn Agent for students, and free premium subscriptions for eligible college students. This move signals Microsoft's big bet on transforming teaching and learning with secure, education-focused AI, as the company seeks to prepare schools globally for a future where digital skills are key.
3. AI’s Workplace Impact Gets Granular in Anthropic’s New Report 📝
Anthropic has unveiled new findings on how its Claude AI is shaking up the workplace, revealing that the biggest productivity boosts are showing up in complex, white-collar tasks.
Their new "economic primitives" tracking system suggests AI is accelerating work most for jobs requiring higher education, potentially reshaping skill demands and job content along the way. The report also notes that augmentation now outpaces automation in how people use Claude, and that AI’s spread remains uneven but is starting to balance out across US states.
4. BlackRock and Microsoft Supercharge AI Infrastructure Fund 🔌
BlackRock has just raised $12.5 billion for its AI-focused infrastructure venture, pushing toward a massive $30 billion goal set last year and positioning itself as a heavyweight in global AI funding.
The partnership, which includes Microsoft, NVIDIA, MGX, and xAI, targets new data centers and the energy grids needed to keep them running, with most early investments headed for the US. BlackRock’s latest survey reveals investors are turning away from big tech stocks and looking to energy infrastructure as the next wave of AI-driven growth.
5. Wikipedia Strikes AI Data Deals with Tech Giants 🤖
Wikimedia just revealed new data-sharing agreements with Amazon, Meta, Microsoft, Mistral AI, and Perplexity, marking a significant shift as these companies will now pay to access Wikipedia’s data for training their artificial intelligence models.
This move comes as part of Wikimedia’s 25th anniversary, formalizing partnerships over the past year that were previously kept under wraps. The deals signal a turning point in how tech companies source reliable information, moving away from scraping and toward compensation for human-generated content.
Nearly two billion people use AI every week.
But somehow, there's only like 52 people who know what a token is.
Huh?
The biggest problem with AI in your organization isn't the technology. It's that nobody can talk about the mysterious black box of AI that's Saran Wrapped in ever-changing jargon.
So on today's episode, we broke down why the AI lingo gap is silently killing your pilots, stalling your projects, and widening your competitive disadvantage every single day your teams can’t chat about the difference between a chat and an agent.
This isn't about memorizing definitions like on a flash card. It's about understanding the future of work.
Let's get it.
1. Your Teams Are Speaking Different Languages 🚀
Their head of AI was in the room too.
These two groups weren't speaking the same language. Technical teams throw around tokens, RAG, MCP, and vector databases while business leaders nod along and secretly Google definitions under the table.
That gap creates real damage.
It stalls pilots. It makes vendor evaluation nearly impossible. It drives waste that compounds every single quarter you ignore it.
Here's what nobody wants to admit: MCPs and A2As are becoming the new KPIs and SOPs. If your leadership team can't discuss AI implementation with the same fluency they discuss quarterly projections, you're building competitive disadvantage by default.
And it won’t turn out well.
The vocabulary shifts faster than anyone can track. And every day your teams can't communicate effectively is another day your competitors pull ahead.
Try This: Schedule a 30-minute meeting with your technical and non-technical leaders in the same room this week.
Ask one question: what does RAG mean and why should we care? Don't prep anyone beforehand. Just watch the conversation unfold and document exactly where confusion happens.
That confusion map is your roadmap for what needs fixing first, because the gap between how your technical team explains AI concepts and how your business leaders receive them is precisely the communication breakdown slowing your entire AI adoption.
2. Your AI Already Does Way More Than You Think 🔥
Most people still think AI is just a chatbot.
Nope.
You can give a single prompt to Claude or ChatGPT right now and get targeted web research based on your context, personalized analysis, downloadable spreadsheets, and presentation-ready PowerPoints. All in one shot. No tech skills required.
The models evolved while most organizations weren't paying attention.
They're agentic now. That means they make decisions on their own to accomplish goals. They choose which tools to use. They retrieve data without being told. They explore multiple pathways and course-correct when something isn't working.
But here's the painful part: if your team still treats these models like fancy search engines, you're leaving 90% of their capability on the table while competitors who figured this out build workflow advantages that compound every single quarter.
Try This:
Open your preferred large language model right now and give it a real business task you'd normally delegate to a junior team member. Something like analyzing three competitor websites and creating a comparison spreadsheet with strategic recommendations. Don't break it into steps.
Just describe the outcome you want in plain English and let the model figure out how to get there. Whatever it produces in the next five minutes will show you exactly how far behind your current AI usage actually is.
3. Year-Long Pilots Will Destroy Your Strategy ⚡
Yet, the majority of companies are sticking super glue on their AI implementation runway.
If you're running a year-long AI pilot, the chart-topping model you started with is probably the 50th best model available by the time you finish. That's not an exaggeration. That's literally how fast this moves.
The companies winning right now are sprinting while staying careful.
They build modularly so they can swap components as better options emerge. They treat implementation like product development, not infrastructure deployment. They accept that perfect evaluation frameworks don't exist yet and run anyway.
You can't apply the cautious, committee-driven approach that worked for ERP implementations to generative AI. By the time your year-long pilot concludes with a nice PowerPoint recommendation, your competitors shipped three iterations and learned from actual production data.
The cost of moving slowly isn't just missed opportunity. It's actively building competitive disadvantage while you wait.
Try This:
Pull up your current AI implementation timeline right now. If any pilot exceeds 90 days before reaching production use, you need a serious conversation about what's actually blocking speed. Most delays aren't technical. They're organizational fear dressed up as due diligence. Identify the single biggest bottleneck preventing faster iteration and eliminate it this month, because the companies that figured out how to sprint responsibly are already three versions ahead of everyone still waiting for certainty.
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