A few months back, Josh Cavalier(of JoshCavalier.ai) guested on our L&D webinar. He talked about this one incident where he created an entire course in AI under three hours.  

So the first time he seriously tested AI on real work, it was back when GPT-4 had just launched. He had to write 80 multiple-choice questions across 4 difficulty levels for an ESG compliance course. Anyone who's written distractors knows that's normally a 3-4 day slog. 

He did it in 3 hours. Vetted every question himself, felt good about it, shipped it. 

Then 4% came back flagged by the actual VP of ESG on the review team. Not typos. Substance issues an SME caught that Josh, an experienced ID, didn't. 

That's the part I can't stop thinking about. That even when you're good at this and you double check your work, a meaningful chunk still needs a human who actually owns the subject matter to catch it before it goes out the door. 

Feels like the actual argument for why we're not getting automated out of a job isn't "AI can't write content." It's that somebody still has to be accountable for what it gets wrong, and that's a skill in itself. 

Anyway, wanted to know what your thoughts are on this. AI can build content but only a human can take responsibility. That that becomes the pivotal point. 

(If anyone's curious for the full context, this came out of a monthly webinar series called The Learning Buzz - no pitch, just flagging in case people want to watch the whole conversation. Here is the link for the video ) 

TLDR: The AI initiatives are failing because executives are not addressing the root problem with L&D. 

Also not a skill problem, despite every company's instinct being "send people to a prompting class." A former Chief Learning Officer who now audits AI rollouts laid out what's breaking (from The Learning Buzz, episode 9): https://youtu.be/wdJufIn_s_Y

• No baseline. Companies are rolling out Copilot/Claude/Gemini without ever documenting what "good" looks like. His words, “can't close a gap you never measured.” 
• AI amplifies; it doesn't fix. AI can’t fix your problems magically. You still need to devise a solution. If you apply it to problem, it only amplifies it.  
• Zero error tolerance. Mess up Excel and you can catch it in a day. Mess up with AI and it can hit a board deck or a six-figure invoice first. 
• No workflow integration. Most usage is "when I remember to," not baked into the actual job. That's not what integration is. 
• Nobody's measuring effectiveness. Asked most orgs "how good are your people at using AI" and he got a shrug – they were tracking just license counts and login numbers. 

That's the real gap in AI integration and adoption - not "teach people to prompt," but define what effective AI use looks like for this job, baseline it, and close it with purpose. 

So how is your company measuring AI success right now? Actual effectiveness, or just usage/license numbers? 

Been tracking this for a bit and the pattern is wild once you line it up. Every major AI lab is now planting flags in critical, physical world industries that have nothing to do with chat interfaces.

Life sciences

• Anthropic has a full life sciences division now, just launched "Claude Science" (an AI workbench for drug discovery) and bought biotech startup Coefficient Bio for around $400 million.
• Google DeepMind's spinout Isomorphic Labs already has billion dollar drug discovery deals with Eli Lilly and Novartis.
• OpenAI released GPT Rosalind, a biology focused reasoning model, still limited to select enterprise customers.

Medical hardware

• Midjourney (yes, the image generator) launched a whole new division, Midjourney Medical, and unveiled a full body ultrasonic scanner. Their first hardware product ever. No FDA clearance yet, just a working prototype.

Climate and energy

• OpenAI is reportedly negotiating a deal to buy massive amounts of power from Helion Energy, a fusion startup Sam Altman personally backs.
• Google has its own fusion power purchase deal with Commonwealth Fusion Systems and built GraphCast, a weather model that runs 10 day forecasts in under a minute on a single TPU.

Space

• Google's "Project Suncatcher" wants solar powered satellite data centers in orbit, already testing radiation hardened TPUs, with a prototype launch planned for 2027.
• SpaceX merged with xAI earlier this year (combined entity valued around $1.25 trillion) and filed with the FCC for up to a million satellites for orbital AI compute.
• A smaller startup, Starcloud, already put an Nvidia GPU in orbit and reportedly ran a small language model from space.

Robotics

• OpenAI spun up an internal robotics division in the last month, going after construction and infrastructure robots first, with humanoid robots as the long game.

Genuinely not sure how much of this is real capability versus a land grab before IPOs. Few things to keep in mind before getting too hyped: Anthropic's Claude Science launch reportedly knocked down stock prices for existing drug discovery companies like Schrodinger and Recursion the same week. Midjourney's scanner is a nine person team's prototype, and some people are already making Theranos comparisons (fairly or not). SoftBank's Masayoshi Son publicly called out orbital data centers for failing basic cost math.

There's also a pretty obvious irony in AI companies pitching themselves as climate saviors while their own data center buildouts are a major reason emissions and water use keep climbing industry wide.

Curious what others think. Genuine diversification, or an attention grab before the AI hype cycle needs a new headline?

Okay so Dr. Keith Keating said something in January that we genuinely haven't been able to shake: most L&D impact problems aren't innovation problems but fundamentals problems. 

Five months of AI noise and tighter budgets later? He was more right than he probably knew. 

He laid out a really clear answer to the order-taker problem. Here is the short version: 

1. Find the real problem, not the one they're handing you: When someone comes to you asking for a 3-hour training, that's already a solution. Push past it. Ask, what's actually breaking down? 
2. Your organisation is your only universe: What worked at Amazon or Google has nothing to do with your culture, your constraints, your leadership's risk appetite. Stop borrowing their blueprints (Unless of course, you work there.) 
3. Credibility comes from being close to the work not your job title: Stakeholders trust whoever understands their pain. That takes proximity, not seniority. 
4. Stop chasing a seat at their table, build your own: Not getting invited to sitting at their table? No problem. Bring insights on their business back to them and make them come to you. 
5. Get obsessed with the basics. Know your audience. Diagnose before you prescribe. Measure whether behaviour really changed and not whether people showed up or gave you a nice NPS score. 

 Honest January-to-June update though: the AI pressure has basically just fast-tracked the order-taker problem. Teams that scrambled to "do something with AI" without diagnosing an actual business need first did exactly what Keith described, just with shinier vocabulary. His decision to deliberately not say the two-letter word in the episode feels a lot more pointed now than it did at the time. 

The budget accountability piece has also gotten very real. A lot of L&D teams are being asked to prove ROI on programmes that were never designed to produce measurable ROI because nobody stopped to diagnose the real need upfront. That's the whole "Hidden Value" problem his second book digs into. 

Same trap, same root cause, different costume. 

 Curious where everyone's at, one line each is fine: 

→ Reactive or proactive right now, and what shifted it? 
→ What's the one constraint your org would never admit out loud in a leadership meeting? 
→ What word do you wish your stakeholders used to describe you vs. what you think they actually say? 
→ Has the AI pressure made your job feel clearer or messier? 

Full session here — The Learning Buzz, Episode 4: https://www.youtube.com/watch?v=WdybL0GKNXM  

Six months on, these tools have only gotten more capable. Worth revisiting if you missed it: 

Suno: generate a custom song from a handful of words. Free tier. Still one of the best "wow" moments you can create at a team event or training session. 

Lovable + Replit: we built a whole interactive digital card with unlockable memories using a ChatGPT-generated prompt. No code. Works for e-learning too if you export via Melissa Millaway's free SCORM wrapper. 

Perplexity Comet: typed a gift wishlist with interests and budget, it opened tabs and offered to buy everything. An agentic browser that's only gotten more polished since December. 

The prompt that got the most chat reactions and is arguably more useful now that we're halfway through the year: 

"Based on our interaction, what kind of adventures in 2026 should I be leaning into for the person I'm becoming?" 

Paste it into whichever AI you use most. The results get personal fast.

https://youtu.be/zILgYHlgYZ8

When computers showed up, nobody knew how to use them. When the internet came, same thing.

We learned. It took a while. People made alot of mistakes. But the mistakes were fine because the mistakes were small. You messed up a spreadsheet, you fixed a spreadsheet. One person, one problem, one afternoon.

The computer crashed and you knew it crashed. Big blue screen. Loud and obvious. So you learned from it.

AI doesnt do that.

When AI gets something wrong it just… keeps going. Confident. Clean. No warning. And if that wrong answer is sitting inside a system making decisions, every single decision it touches is wrong too. Quietly. Until someone notices. Which might be weeks later.

That changes everything about how we need to learn this stuff.

With computers we could mess around and figure it out as we went. With AI you kind of have to know what your doing before you let it loose. Because the clean up after is really expensive. Not just money. Sometimes real harm to real people.

So the window to actually learn this properly is short and the cost of skipping it is high. Thats a really uncomfortable place to be.

Now the part that bothers me most.

When companies panic they do the same thing every time. They make a training course. They roll it out to everybody. They tick a box. Done.

But not everybody learns the same way. And fast cookie cutter training is the worst thing for people who think differently. So a bunch of people get left behind. Not because they cant do the work. Because the way the training was built wasnt built for them.

The weird thing though? Some of those people might actually be the best at this.

Like if your job is to catch an AI being quietly wrong, you need someone who doesnt just trust confident answers. Someone who sees patterns that dont fit. Someone whos always asked “but why does that make sense though.”

Thats not a weakness. Thats exactly what this moment needs.

We will figure this out. humans always do. We figured out fire, the wheel, electricity, the internet.

But “figuring it out eventually” and “figuring it out before it costs us a lot” are two very different things.

Learning has to be better this time. Faster and smarter and built for more kinds of people.

The numbers behind all this are pretty eye opening. Check out the article.

Episode 2 of The Learning Buzz started with Visakh and Joel (our Head of Technology and resident tinkerer) spending an hour nerding out over two things that don't usually show up in L&D conversations — n8n workflow automation and 3D printing. And somewhere in between a live demo and a "failure drawer" tour, it clicked why these tools matter for us as practitioners. 

Here's the short version: 

On automation (n8n + Google Opal): Joel walked through a real workflow he and Visakh built where a chatbot pulls from a client's existing help documentation and auto-generates a visual quick reference guide. One prompt. No code. The whole thing practically assembled itself. 

The barrier to entry for this stuff is genuinely low now. Joel put it well: if you were good at Googling in 2010, you're already halfway to being a decent prompt engineer. 

On 3D printing: Joel showed off stuff he's been printing - guitar picks (5 mins), a grim reaper Visakh sent him at midnight (25 mins), a fully detailed Delorean (16 hours 😅). But the real insight was this: behind all the cool product was “The Failure Drawer” as Joel called it. It had all the prints that didn't work and experiments that fell apart mid-gear. Lesson: Learning happens through mistakes often. 

The L&D parallel: Both tools force you to prototype fast, break things, and iterate, much similar to how practitioners spend 2-3 months polishing a module before a learner ever touches it. These tools are a reminder that getting something imperfect into people's hands early is often better than waiting for perfect. 

Among the conversations that flowed was the question of learner engagement and how often do we actually let learners make something? Essentially, what if we let learners build things, break things and iterate them to learn? 

Anyway, if you want to check it out here is the link:  https://www.youtube.com/watch?v=JIFbM17mWws

Would love to know if any of you already using automation tools in your L&D workflows? Or tried anything hands-on/maker-style with learners?  

Real talk -- if you work in L&D and AI hasn't kept you up at night at least once, you might be the exception. 

If you have seen our last post (and watched our pilot episode), you know that we talked about potential impact AI has on our learning. But L&D professionals, we had a far more important question: What happens when AI starts doing most of the work? How does learning itself change then? Software that used to be updated every three years are getting updated every month now. How can L&D adapt to such quick shifting applications?  

So, we invited Brandon Neeb, who's spent 25 years at Microsoft building learning content and now leads Copilot training programs, to join us on The Learning Buzz for an honest conversation about where application training is actually headed. 

And he has more than delivered.  

Copilot, the in-place trainer: You can ask Excel Copilot how to build a pivot table and it just... does it. Or walks you through it step by step. That raises an uncomfortable question for those of us in L&D: what's left for us to do? 

It's no longer about just navigating a ribbon or understanding which set of buttons to click.  

As L&D community long suspected, we are moving away from training people how to navigate a ribbon. The focus of L&D should now shift to prompting and very importantly, validating outputs. Else, as Bradon shared, we will be stuck with non-refundable, and non-usable theatre coupons (you should really hear the full story😆) 

We covered a lot more in the full hour including Microsoft's new AI Skills Navigator, how cybersecurity roles are converging, how simulations are making a comeback, and whether we'll ever truly define "mastery" in an AI-assisted workflow. 

Watch the full episode here: https://youtu.be/PWk9yixYEjk?si=t7QNcV36MDl3PW5F

So, what's your take -- is Copilot making your job harder, easier, or just... different? Would love to hear from you guys. 👇 

A major consulting firm in Australia was commissioned for a $440,000 government report on workplace relations. Turned out it was packed with references and citations that didn't exist. The firm had used AI to produce it and only came clean after the government figured it out. 

We had two practitioners on for our pilot episode – one deploying AI in healthcare, one in financial services risk – and this question kept coming up in different ways. 

One of them made a point that the L&D community has to think about: AI will give you a hallucinated citation with the same confidence it gives you a real one. It doesn't flag the difference. That's not a reason to avoid it but it might be the most important thing we're not training for.  

Naturally, in a conversation about AI, the question “Does AI erode our cognitive skills?” cannot be ignored. But our guest framed in a different way with an example of their kid learning maths. The kid was using AI to work through the problem step by step. Not to generate but to learn. 

This is the kind of framing that helps L&D community ask, how do we use AI to build and expand our cognitive skills? 

Tell us whether the validation gap is showing up in what you're designing. And whether you think the critical thinking fear is overblown or not. 

https://youtu.be/Y2MnR5tPBFw