Philip Lessner

Experienced Electronics industry professional with extensive background in capacitor, inductor, resistor, and sensor technology.

[Smoltek @Smoltek]

𝐒𝐦𝐨𝐥𝐭𝐞𝐤 𝐉𝐨𝐛 – We are looking for a 𝐂𝐡𝐚𝐫𝐚𝐜𝐭𝐞𝐫𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫.
You will do electrical characterization and reliability assessment of our ALD-based dielectric stacks and CNF-MIM capacitors.

https://t.co/1SkzrIF3yW

[Philip Lessner @plessner]

I’m glad I’m retired and can use Apple Mail/Gmail now.

[Aakash Gupta @aakashgupta]

NASA pays $100M for Microsoft 365 licensing across the agency. They standardized every system on Microsoft. They put Microsoft Surfaces on the Orion spacecraft as the crew's personal computing devices.

And the first technical crisis of humanity's return to the Moon was Reid Wiseman radioing Houston to say he has two Microsoft Outlooks and neither one works.

Mission Control's response? "With your go, we can remote in and take a look." The same exact workflow your company's IT helpdesk uses when you submit a ticket on a Monday morning. Except the user is traveling at 4,275 mph, 30,000 miles from Earth, and the Wi-Fi situation is considerably worse.

This spacecraft survived hydrogen leaks, helium leaks, a faulty heat shield, and a broken toilet. Outlook broke anyway. The toilet actually got fixed faster.

The real story here is that Microsoft has achieved something no other software company in history can claim: a support ticket from lunar transit. Their enterprise sales team should frame this. "Battle-tested in space" is a positioning statement most B2B companies would mass murder for, and Microsoft accidentally earned it because Outlook crashes everywhere, including orbit.

Outlook remains the only software in human history that performs identically whether you're in a cubicle in Redmond or aboard a spacecraft bound for the Moon. Universally, reliably broken. And we keep buying it anyway.

[Addy Osmani @addyosmani]

Every time we've made it easier to write software, we've ended up writing exponentially more of it.

When high-level languages replaced assembly, programmers didn't write less code - they wrote orders of magnitude more, tackling problems that would have been economically impossible before. When frameworks abstracted away the plumbing, we didn't reduce our output - we built more ambitious applications. When cloud platforms eliminated infrastructure management, we didn't scale back - we spun up services for use cases that never would have justified a server room.

@levie recently articulated why this pattern is about to repeat itself at a scale we haven't seen before, using Jevons Paradox as the frame. The argument resonates because it's playing out in real-time in our developer tools. The initial question everyone asks is "will this replace developers?" but just watch what actually happens. Teams that adopt these tools don't always shrink their engineering headcount - they expand their product surface area. The three-person startup that could only maintain one product now maintains four. The enterprise team that could only experiment with two approaches now tries seven.

The constraint being removed isn't competence but it's the activation energy required to start something new. Think about that internal tool you've been putting off because "it would take someone two weeks and we can't spare anyone"? Now it takes three hours. That refactoring you've been deferring because the risk/reward math didn't work? The math just changed.

This matters because software engineers are uniquely positioned to understand what's coming. We've seen this movie before, just in smaller domains. Every abstraction layer - from assembly to C to Python to frameworks to low-code - followed the same pattern. Each one was supposed to mean we'd need fewer developers. Each one instead enabled us to build more software.

Here's the part that deserves more attention imo: the barrier being lowered isn't just about writing code faster. It's about the types of problems that become economically viable to solve with software. Think about all the internal tools that don't exist at your company. Not because no one thought of them, but because the ROI calculation never cleared the bar. The custom dashboard that would make one team 10% more efficient but would take a week to build. The data pipeline that would unlock insights but requires specialized knowledge. The integration that would smooth a workflow but touches three different systems.

These aren't failing the cost-benefit analysis because the benefit is low - they're failing because the cost is high. Lower that cost by "10x", and suddenly you have an explosion of viable projects. This is exactly what's happening with AI-assisted development, and it's going to be more dramatic than previous transitions because we're making previously "impossible" work possible.

The second-order effects get really interesting when you consider that every new tool creates demand for more tools. When we made it easier to build web applications, we didn't just get more web applications - we got an entire ecosystem of monitoring tools, deployment platforms, debugging tools, and testing frameworks. Each of these spawned their own ecosystems. The compounding effect is nonlinear.

Now apply this logic to every domain where we're lowering the barrier to entry. Every new capability unlocked creates demand for supporting capabilities. Every workflow that becomes tractable creates demand for adjacent workflows. The surface area of what's economically viable expands in all directions.

For engineers specifically, this changes the calculus of what we choose to work on. Right now, we're trained to be incredibly selective about what we build because our time is the scarce resource. But when the cost of building drops dramatically, the limiting factor becomes imagination, "taste" and judgment, not implementation capacity. The skill shifts from "what can I build given my constraints?" to "what should we build given that constraints have in some ways been evaporated?"

The meta-point here is that we keep making the same prediction error. Every time we make something more efficient, we predict it will mean less of that thing. But efficiency improvements don't reduce demand - they reveal latent demand that was previously uneconomic to address. Coal. Computing. Cloud infrastructure. And now, knowledge work.

The pattern is so consistent that the burden of proof should shift. Instead of asking "will AI agents reduce the need for human knowledge workers?" we should be asking "what orders of magnitude increase in knowledge work output are we about to see?"

For software engineers it's the same transition we've navigated successfully several times already. The developers who thrived weren't the ones who resisted higher-level abstractions; they were the ones who used those abstractions to build more ambitious systems. The same logic applies now, just at a larger scale.

The real question is whether we're prepared for a world where the bottleneck shifts from "can we build this?" to "should we build this?" That's a fundamentally different problem space, and it requires fundamentally different skills.

We're about to find out what happens when the cost of knowledge work drops by an order of magnitude. History suggests we (perhaps) won't do less work - we'll discover we've been massively under-investing in knowledge work because it was too expensive to do all the things that were actually worth doing.

The paradox isn't that efficiency creates abundance. The paradox is that we keep being surprised by it.

[Philip Lessner @plessner]

@MsMelChen And, the current Iranian regime will do anything to undermine peace. Intelligence says that Hamas initiated their terror attack because Israel and Saudi Arabia were getting too close to signing a peace treaty.

[Philip Lessner @plessner]

#Tantalum #capacitors are one of the five principal dielectric types and represent a market over $1.2B. These capacitors have an interesting history and this blog post covers the invention of the solid Ta cap in the 1950's👇
https://t.co/PDjTpMSF0D
More to come...

[Philip Lessner @plessner]

Everyone is concerned about the increasing power consumption of AI applications. YAGEO Group has the components that can help reduce that power consumption and enable a more sustainable future. Read about our offerings and their roles in
👇
https://t.co/gO5mCN3A5d

[Philip Lessner @plessner]

@GadSaad Of course, the better way is that each side of the river produces something of value to trade with the other side. Unfortunately, that doesn't happen in practice as often as we'd like.

[Philip Lessner @plessner]

@MsMelChen Well, democracy is messy. But, it works in the end.

[Philip Lessner @plessner]

Check out my latest article: Artificial Intelligence--Why Now? https://t.co/WyyiiHEGU8 via @LinkedIn

[Philip Lessner @plessner]

@EvanKirstel Full Self Driving

[Philip Lessner @plessner]

@MsMelChen Because you're a badass.

[Pulse Electronics, a YAGEO company @PulseElecYageo]

Don't forget! Join YAGEO Group along with DigiKey for our upcoming technical seminar, where we're dedicated to simplifying the process of choosing inductors. Secure your spot today – REGISTER NOW! https://t.co/XXwUTbtiFJ

[Philip Lessner @plessner]

@__mharrison__ Too bad they won't be able to read legacy Python code. 🙁

[Philip Lessner @plessner]

Our components don't work up a "sweat" in your high power AI applications. Get the latest scoop on low-loss, high-power💪 components from @YageoCorp in my @ttiinc Market Eye post.
👇
https://t.co/tLW2XXkzW7
#AI #electronics #ArtificialIntelligence

[Philip Lessner @plessner]

"KEMET has some of the most innovative capacitor technologies in the industry."-@SteveTaranovich in Electronic Design Magazine. One such @KEMETCapacitors technology is our Class I MLCC: KC-LINK for high power designs. Check out his technical article
👇
https://t.co/OeXlIfD7Nj

[Philip Lessner @plessner]

What's new at @YageoCorp? Read all about it in my interview with Markt&Technik. Learn about the latest developments in #capacitors and #magnetics and our expansion in #sensors.
👉https://t.co/51m6YCAahB

[Philip Lessner @plessner]

@MsMelChen @Aella_Girl My stable wife is crazy enough for me. Life is interesting enough.

[Philip Lessner @plessner]

Comprehensive article on different #capacitor types--their characteristics and applications. https://t.co/A4ELRI9yxf

[Philip Lessner @plessner]

@ylecun LLM--Like owning an AK-47
Ballpen--Like owning a handgun

[Philip Lessner @plessner]

@MsMelChen First World problem, but I sympathize.