Great article. The part that stood out to me is the shift in how organizations define work.
In the old model, performance and OKRs were anchored in disciplines, job titles, and role-specific expectations. In the AI era, those boundaries are starting to collapse. The deeper issue is psychological and organizational: people are constantly negotiating the line between “this is my job” and “this is not my responsibility.”
That creates a key adoption problem: what is the upside of being visibly recognized as an expert AI user? If people learn that I can do faster, better, and more cross-functional work, why would I reveal that unless the company also creates a clear system for recognition, compensation, or career growth?
Eventually whoever is responsible to fix prod incidents and maintain has the ownership. And I agree that’s pretty messy in a world where agents are crossing those boundaries. Will the AI engineer with their horde of agents be responsible to keep everything running? I really doubt so, but we will see
> whoever is responsible to fix prod incidents and maintain has the ownership.
There's a mistaken assumption under there that businesses can identify who that statement describes.
Some can. But a lot of businesses cannot identify and reward, or support, or just not RIF, those people. Like a lot a lot. More than I'm comfortable lumping under statements like "well those are just bad places to work/places that should be shut down".
There's no punchline or counterproposal there; that's just my observation.
If they create a system to compensate expert AI users wouldn't that career have a problem in that anyone (enticed by the new careers existence and) integrating their advice on any company particulars with a (weeks) more modern approach is basically putting them in the role of domain expert being eliminated.
The part I push back on is the idea that expertise is easy to learn in just a few weeks.
Take Andrej Karpathy as an example. Even if I knew exactly what tools he uses and what his workflow looks like, I still would not be able to produce anything close to what he can produce in a few weeks. And he is not standing still either—he is evolving at the same time.
A lot of real expertise is not in the visible/system-able workflow. It is in someone’s experience, taste, judgment, and wisdom. You can copy the artifact, but you cannot easily copy the thinking behind it: the principles, the decision-making, and the ability to apply those principles across many different/subtle situations.
But I do agree with the concern behind the argument. People may worry that sharing what they know could weaken their own position. And the more uncomfortable question is about peers: if someone’s role can be “retired” because others absorbed their knowledge and skills, then it is hard not to ask, “Am I next?”
Sure but here you are not talking about the 100000+ local firm experts for windows networking or coding with agents you are talking about the people who can rewrite the best advice that makes those local experts out of date where their small experiences probably don't make up for not having integrated X, Y or Z yet.
Love this concept.
- If it could give me a near to showtime notifications to help me decide if it's a true empty screenings. That would be great.
- If it connect with my Google Calendar for schedule, it would be amazing.
John Ternus has been the core leader behind two of Apple’s most important post-Jobs achievements: AirPods, its best new product in that era from my pov, and the transition from Intel to Apple Silicon, one of its most ambitious and successful strategic bets.
I’m very bullish on his innovation mindset and on Apple’s next chapter.
Trust is a major theme & I agree.
Beyond trust, I think individualism is another major theme, especially from the perspective of an Eastern cultural background. If too much of my time and energy is spent turning inward and focusing on myself, that feels completely opposite to what Buddhism teaches: letting go of self-grasping is the path to happiness.
It is on my to do list to add automatic subtitles.
The 3 editors you mentioned are backed by cloud juggernauts that either asked already or will ask for a perpetual licence to any content you process with them because they can and they also offer cloud options.
I do have to say that they are probably more mature and feature rich than VidStudio at this point in time ahhaha
Very happy about this news. I think he might could bring apple back to a innovation culture company again. Seems well balanced Jobs + Cook. Looking forward next 18 month of Apple. I already setup recurring investment for Apple in 2 months ago, finger cross
I’m a product designer, and I could totally see this fitting into my workflow for design briefs, strategy, review, and crit docs. Markdown is too simple, and Figma is too visual. This feels like a great middle ground.
Every time this happens the debate goes the same way — trust Google or don't, switch to Proton, self-host everything. But the real issue I believe isn't whether we trust Google. It's that the data existed somewhere it could be taken from in the first place.
I've been thinking about this a lot while working on a side project. I ended up making it work entirely offline — no server, no account, no network calls. Not out of paranoia, just because I couldn't come up with a good reason to ask users to trust me with their data. Turns out the best privacy policy is just not having anyone's data.
No monetization plan — it's all local, no server, near-zero cost to run. Free and open source. I believe good tools should be accessible to everyone. Open source first, monetization will figure itself out down the road.
Outstanding, and ethical too. So tell us, did you forgo monetization forever, or do you have a plan for revenue? Perhaps it’s not an issue for you, but knowing what you have up might help others conceive of a shift of the Overton window such that it’s no longer a given that that must be harvested.
In the old model, performance and OKRs were anchored in disciplines, job titles, and role-specific expectations. In the AI era, those boundaries are starting to collapse. The deeper issue is psychological and organizational: people are constantly negotiating the line between “this is my job” and “this is not my responsibility.”
That creates a key adoption problem: what is the upside of being visibly recognized as an expert AI user? If people learn that I can do faster, better, and more cross-functional work, why would I reveal that unless the company also creates a clear system for recognition, compensation, or career growth?
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