None
When employees go around a company's AI policy
A popular advert was made without AI.
The pieces I most enjoyed writing in 2025
Google Maps as a middleman of the dining scene.
A curated subset of my weekly reading
Is it? Or is it not?
When your website sends a certain message
AI-backed toys aren't quite there yet
Spotting different types of AI use cases
Creating your own 'wrapped'-style data summary
What debt derivatives might tell us about the state of AI
Every incident is a learning opportunity
Pick up a copy of my latest book
Remember to think of the bigger picture
On short-sellers, bubbles, and excitement
Companies in China are making headway with AI, by focusing on what the technology can achieve today.
On peas, cookies, and AI
What a cruise ship's test run can teach us about deploying technology.
Investors want genAI to actually deliver
You can't put AI in everything. Can you?
Getting the most out of AI
A gift from a newsletter subscriber
Sometimes an offer looks good. Too good.
We need a new term for AI-the-technology, to separate it from AI-the-hype
Anthropic's new partnership with Sweden's IFS
Testing your AI-driven products before they're released to the public
Live demos are a tightrope act
Datacenter construction has gotten too far ahead of itself
Seeing parallels between today's genAI hype and the 2008 Great Financial Crisis (GFC)
My latest book, Twin Wolves, has arrived.
The impact genAI companies are having on the stock market
Customers want products that work. Even if you insist on calling them beta releases
Thoughts on synthetic data
On Chief Data Officers and Chief AI Officers
The model often sounds right, even when it's not
Be careful with middlemen. Especially the robotic variety.
What airline booking systems can tell us about deploying AI
Five words can save you a lot of headache
Legal experts get caught using genAI
Even genAI providers are calling it a bubble
Thoughts on the premium credit card space
How to think about the possibility of an AI bubble
Five things. That's it.
How verification laws create data risks
Does your AI actually work?
It takes more than AI to improve your product
A reminder of where to look for those AI use cases
Even your airline has become a data broker
Revisiting an old newsletter, in light of recent AI headlines
Sometimes you have to override the machine
Cleaning up after the genAI bot that replaced you
As it turns out, those genAI bots still need a lot of human help
Helping journalists identify AI-generated content
You need to do more if you want to impress customers
Not everyone shares this CEO's love of AI
Big consulting firms fail to create real value for their clients' genAI efforts
Virtual influencers raise questions and also introduce a shift in the risk/reward tradeoff.
Meta chatbots will now avoid discussing certain topics with teens.
The relationship between AI knowledge and experimentation.
A reminder to build safeguards for your AI-based system.
When good job postings go bad.
What airport terminal design can teach us about AI-based products.
People ask whether recent news indicates the end of the genAI wave.
My favorite definition of a complex system.
Looking back on something I wrote four years ago.
A reminder of what kinds of work to hand off to machines
Musing on AI's use cases down the road.
How some execs are building AI literacy
GenAI projects continue to not deliver.
Trust is everything
Meta's AI app has not been a hit
Take care of your data fundamentals
Additional details on Delta's AI-based pricing
ChatGPT has surprised end-users by making some data public.
Establishing boundaries for genAI bots
FInding the right place(s) to use that AI model
Debt takes many forms. All of them must be repaid.
Apple is now marking AI-generated notification summaries.
Sometimes – oftentimes – that genAI bot is overkill
The value of a genAI bot in brainstorming sessions
Periodic reminder: do not let the machines run unattended.
Remember that people are on the receiving end of your AI-driven automation.
One year on, how are the lessons holding up?
What chess can teach us about assigning work to AI
Don't collect data you can't protect
Dynamic pricing is almost as old as the airlines themselves.
Three questions to ask before AI alters your org chart.
You need AI that works today.
For company executives, genAI is no free lunch.
Apple's opportunity to skip out of the so-called AI arms race.
Private markets exist beyond Wall Street.
Customers don't care what's under the hood.
Tune out the static to make the most of AI.
Your periodic reminder that genAI chatbots are a poor substitute for search
When people and genAI bots do not mix ... or, they mix too well
How to approach your company's AI plans
Four questions to ask before throwing away data points
A brief note about complexity and knock-on effects
Company-wide AI mandates can bring needless downside risk exposure
Periodic reminder: always check the bot's outputs
One study shows that 75% of corporate AI projects don't provide value.
Improving your company's AI adoption
What Warren Buffett and Costco can teach us about adoptiong AI.
The notion of an 'AI arms race' is matter of marketing
Treat a model's outputs as opinions, not facts
Lessons from Home Depot's gardening division
Who is impacted when your AI model is wrong?
A recent study finds that leaders should exercise ambivalence
Be careful what your model picks up off the street
Three challenges of AI-based content moderation
What to consider before handing your data to a service
Consider what happens to cusomers' data if your company folds
Following the example set by office workers
Be careful announcing those AI products before they're ready
Sometimes your AI chatbot gets loose and bites you
This is really a lesson in tech interview practices
When designing tools, make it easy for people to follow the right path
The BBC ran the numbers on chatbot goofs
AI agents aren't quite ready to take over our busywork
It helps to know the difference
AI doesn't make the product more attractive
What industrial robots can teach us about AI
A look into AI chatbot companions
How one nation's speech influences LLMs
How to run a marketplace where everybody wins
Factory machines and AI have a lot in common
A Meta genAI bot incident is a reminder for everyone to check their own work
Five steps to improving your AI efforts in 2025
Be careful where you store sensitive data
The pieces I most enjoyed writing in 2024
In many companies, AI is just a topic of conversation
It's disappointing to see certain companies' AI efforts stumble
Creating an effective summary is not as easy as it seems
... at least, not the same way that people do
Troubling expansion plans of an identity-verification company
Save on your AI spend year-round
Contrary to popular belief, data barriers are a good thing
Some 'basic' analysis can make a big difference
What happens when you let the machines think for you?
Finding the best (and worst) tasks for bots
Beware promises of tell-tale signs
What every middleman can learn from OnlyFans
I wonder how things are going at LinkedIn HQ these days?
LinkedIn has default its genAI data collection to opt-in
You can't copycat the FAANGs' data practices
Share the story of your worst meal ever
What can you learn from a successful YouTube channel?
The power to build
Thoughts on a recent episode of Intelligence Matters: The Relaunch
A person with technical know-how can ulso understand how a business operates.
Have I gone post-technical?
What the model doesn't know, can hurt you
It helps to know the difference
The fall of Enron created an interesting and real-world dataset for text mining
Someone who attended the BlackHat conference left with concerns about LLMs
Using machines to say 'no'
When a company bypasses its own data safeguards
I've said it before, and I will have to say it several more times
A broad skillset goes a long way
This is a good time to move at your own pace
Wait, two _trillion_ dollars??
A reminder of what makes AI projects possible
I've been posting elsewhere...
Executives want to learn about AI. I can help.
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