Unless you are not yet old enough to drink, or lived out in a shack above the treeline in a mountain range, you probably remember to some degree the "Internet Era". Also known as the "Dot-Com Era" (along with its closely-released sequel, the "Dot-Bomb Era"), it was a time when the Internet was new, the boundaries were untested, and everyone's credulity was pushed out to the utter limits.

The reason I bring this up, of course, is that current thinking among AI critics holds that we are on the cusp of a "post-bubble" AI phase. That is to say, the "AI bubble" (which is a real thing, I hold that as axiomatic) is going to pop any day now, and when it does, all kinds of chaos and terribleness is unleashed. However, I draw different conclusions out of the "Dot-Com/Bomb" eras, mostly because (a) I already feel like I know the bubble is going to burst, so I don't have any real questions there, but (b) I want to know what's going to survive the silicon "pop" and what's not. And by "what" I mean "who"--which companies will survive the shift and which won't?

In the early days of the Internet, focus was on the Internet itself. Like it was, on its own, the end goal. Companies were formed to be "E-" companies (anything with an "e" prefix, a la "eBay") or an "I-" company (anything with an "i" prefix, a la "iMac"s, which obviously were a product and not a company but at the moment I can't think of a good "I-" company that's still around). The emphasis was on "The Internet"--whatever the company did, "The Internet" was at the center of it. Everything revolved around "The Internet". Microsoft even famously decided that it was going to put "The Internet" at the centerpiece of its operating system, when it decided to have users "browse their desktop" ¹. "The Internet" was going to make everyone a ton of money, and everyone knew it.

And companies responded accordingly, in some cases going so far as to create "Chief Internet Officers". (Not to be confused with its contemporary cousin, the "Chief Web Officer".)

Before I continue on, I'd like to point out that this is not the first time the industry has responded to a new technology by seeking to create a top-level C-suite role for it and then turning to that individual and saying, "Go on! Make it happen!" At the turn of the previous century, the world saw the emergence of the Chief Electricity Officer, a role designed to bring the company in line with the burgeoning field of "electricity".

Funny how we don't have any of those around anymore.

This is because as time progressed, we came to realize that focusing on the technology itself doesn't accomplish much. All of the companies that were formed to focus on the thing--in the most recent example, The Internet--eventually came to realize that the thing is only useful as a means to some other end. OReilly (the book publisher) had a commercial web server product, as competition to Netscape's and Microsoft ²'s commercial offerings. Today, the HTTP protocol is so ubiquitous (and, it turned out, simple) that we run HTTP servers out of single-line Python scripts. Just as electricity eventually just became part of the purview of the staff that manage the building's physical presence--and in some buildings, particularly manufacturing operations, that's a nontrivial task, to be sure--and dropped out of the CxO suite accordingly.

Success in either era came only when people realized that "the thing" was nothing more than a means to an end, and established the end accordingly. Electricity helps manufacture things, or helps the staff work more efficiently (consistent lighting) or provides power to automation (washers, dryers, etc). The only people who do anything around the electrical grid itself are commercial power producers, the folks who install solar panels on your roof, and electricians.

Therefore, success in this upcoming era will not be to those who establish "Chief AI Officers", but understand that AI is simply now a (powerful) part of your implementation strategy, and act (and code and staff and train) accordingly. The "moat" isn't AI itself, and the frontier models are never going to be "moaty enough" to keep a decent enough distance from open-source or locally-trained models. Just having a ton of compute available (which both of them don't have, as evidenced by Anthropic having to swallow its pride and sign the deal with Musk/X) isn't enough to keep the wolves at bay.

A couple of notes that I think come out of this:

• Apple survives quite well, given its near total non-engagement with AI thus far.
• Microsoft manages, though will have to do a ton of internal re-orgs to re-re-adjust to the new AI. Likewise for Facebook ³ and Google. Their stock may take a hit, executives may get cycled out for their "bad judgment" (even though a lot of it is being demanded by Boards), but they'll survive. They have other products on which they rely.
• OpenAI and Anthropic will collapse, and quickly get snapped up by somebody else when their value degrades enough to be a "convenient buy". (Honestly, it's too easy to stand up your own "ChatGPT" using open-source tools, and many developers are now seeing "Write your own coding agent harness" as the new Coming-of-Age Trial in the same way we used to think about writing a web server back in the 2000s.) The shift of domains will likely be the biggest public impact, but the economic untangling of all the loans and VC debt could very well be a new "sub-prime"-level banking crater.
• NVidia will never be much more valuable than it is now, because while the massive data centers aren't ever going to happen, the demand for GPUs will certainly continue. However, the manufacture of a GPU is not a relatively hard problem to solve if you're already a chip manufacturer, and Intel/AMD/others have plenty of incentive to retool some supply lines to start building out cheaper (and more importantly, available) GPU lines.

Keep in mind, too, that with the growing public backlash against all things AI (as exemplified by the "boos" at speakers praising AI during graduation speeches), companies whose brand is tightly aligned with AI (which seemed like a great idea in 2025 and a terrible idea for 2027) will likely take PR hits to go along with it all. Apple, Microsoft, Meta, they all have something to be known for beyond their AI efforts; OpenAI and Anthropic, not so much.

Improving C# memory safety
about 1 hour by Richard Lander

C# 16 is redesigning the unsafe keyword to act as an explicit contract between callers and API authors, not just a context that enables pointer use. Every unsafe operation must appear inside an unsafe block, and unsafe members must document caller obligations via a new safety comment. The compiler enforces these rules as errors, making unsafe code visible and auditable instead of hidden by convention. The feature arrives as a preview in .NET 11 and a production release in .NET 12.

Stop paying AI agents to play hide-and-seek with your test suite
sponsored by Jetbrains

Claude and Codex are great at writing tests – but terrible at finding them. JetBrains Rider teaches AI agents a new skill that cuts AI expenses by up to 50% while keeping generated tests aligned with your codebase. Curious what the secret sauce is? Their blog has the answer.

State machines in .NET: Modelling Uber's trip lifecycle
11 minutes by Adrian Bailador

Adrian explains how state machines can be used in .NET with EF Core to manage an Uber-like trip lifecycle safely and reliably. He covers trip states, transition rules, concurrency handling, domain events, and timeout management. The design prevents invalid state changes, race conditions, and duplicate actions while keeping the system scalable, maintainable, and fault-tolerant for real-time ride-sharing applications.

ASP.NET, factory patterns, and the illusion of control
7 minutes by Bipin Joshi

Factory patterns in .NET promise cleaner object creation, but often just move complexity elsewhere. The built-in dependency injection container in ASP.NET Core handles this better by distributing responsibility across components instead of centralizing it. Factories still have their place when runtime conditions demand them, but adding them by default makes code harder to read. The most maintainable systems are those easiest to understand, not those most tightly controlled.

Unity events: Why most devs still get them wrong
31 minutes by Darko Tomic

Most Unity devs get C# events wrong in the same handful of ways: they reach for UnityEvent when Action is cleaner, forget the event keyword, wipe out subscribers with = instead of +=, and skip unsubscribing in OnDisable. Darko walks through delegates, Action, the event keyword, UnityEvent, and the subscribe/unsubscribe pattern, using real code from a Unity game he shipped. By the end, you will know which tool to reach for, when, and why.

Why I switched to primary constructors for DI
6 minutes by Milan Jovanović

Milan explains why he adopted primary constructors in C# for dependency injection service classes. Primary constructors reduce boilerplate code, improve readability, and simplify service definitions. He also discusses their use in entities and value objects, while highlighting an important limitation: constructor parameters are mutable unless explicitly assigned to readonly fields.

And the most popular article from the last issue was:

• IAsyncEnumerable: Streaming data without loading everything into memory

Benedict Evans is an independent analyst and former partner at Andreessen Horowitz, where he spent years as their in-house “thinker” tracking the most important technology trends. For the past six years, he’s been publishing deeply researched presentations on where tech is heading, most recently focused on AI’s transformation of the economy. His work is read by founders, investors, and operators trying to make sense of a noisy field. His most controversial opinion: AI is as big a deal as the internet or mobile—and only as big.

In our in-depth conversation, we discuss:

1. Why we’re in “1997” for AI—early, exciting, and deeply uncertain about what comes next

2. Where value will actually accrue in the AI stack

3. The anti-AI backlash, and where it may lead

4. The surprising boom in consulting and professional services at AI companies

5. Why distribution is becoming the ultimate moat as software gets easier to build

6. Why the right question about your job isn’t “What percent can AI do?” but “Is this a task or a job?”

7. Why things will probably be okay—and what you need to do to prepare

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Brought to you by:

WorkOS—Make your app enterprise-ready, with SSO, SCIM, RBAC, and more: https://workos.com/lenny

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Episode transcript: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where

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Archive of all Lenny's Podcast transcripts: https://www.dropbox.com/scl/fo/yxi4s2w998p1gvtpu4193/AMdNPR8AOw0lMklwtnC0TrQ?rlkey=j06x0nipoti519e0xgm23zsn9&st=ahz0fj11&dl=0

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Where to find Benedict Evans:

• LinkedIn: https://www.linkedin.com/in/benedictevans

• Newsletter: https://www.ben-evans.com/newsletter

• Website: https://www.ben-evans.com

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Where to find Lenny:

• Newsletter: https://www.lennysnewsletter.com

• X: https://twitter.com/lennysan

• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/

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In this episode, we cover:

(00:00) Introduction to Benedict Evans

(02:19) What people aren’t pricing in about AI’s impact

(06:24) Why we’re in the 1997 moment of AI

(09:44) The unexpected boom in professional services and consultants

(17:44) Why distribution is becoming the ultimate moat

(23:17) The coming job transformation: what’s real vs. panic

(27:33) Why AGI definitions keep shifting

(38:11) Where value will accrue: models vs. applications

(42:55) Distribution wars: Google, Meta, Apple, and OpenAI

(48:12) The anti-AI sentiment and backlash

(53:11) How to raise kids in an AI future

(58:27) What jobs to steer toward or away from

(59:20) The question nobody’s asking about AI

(1:06:25) How to be successful in this coming future

(1:08:43) AI corner

(1:11:43) Lightning round

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Referenced: https://www.lennysnewsletter.com/p/a-rational-conversation-on-where

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Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.

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Lenny may be an investor in the companies discussed.

A bit of a lull before the BUILD conference next week. Highlights this week include: Claude Opus 4.8 is now available in Microsoft Foundry: Anthropic's most capable Opus model is now available in Microsoft Foundry, built for complex coding, reliable multi-step agentic tasks, and document-heavy enterprise analysis that holds context across longer sessions. Automate evaluations Microsoft Foundry: A walkthrough of Foundry's evaluation tooling, covering end-to-end run tracing, on-demand synthetic test datasets, automated red-team attacks, AI-driven root-cause analysis of failures, and runtime guardrails such as Task Adherence that inspect every tool call. Azure Functions MCP extension now supports MCP Prompts: The Azure Functions MCP extension now supports MCP prompt triggers in public preview, completing coverage of all three core Model Context Protocol primitives (tools, resources, and prompts) for building MCP servers on Azure Functions.

In Analytics: Incremental Liquid Clustering in Microsoft Fabric: Faster, smarter, and truly incremental: Now in Fabric Runtime 2.0, Incremental Liquid Clustering re-clusters only the data that needs it, keeping clustering time roughly flat as tables grow rather than rising linearly, which makes OPTIMIZE costs far more predictable. From Excel to Delta Tables - Simplifying Excel Data Ingestion with Shortcut Transformations Preview: Fabric's Shortcut Transformations (Preview) now extend their zero-code ingestion experience to Excel, letting teams bring spreadsheet data into Delta tables the same way they already do with CSV, Parquet, and JSON.

In preview Public Preview: Azure Site Recovery Support for Performance Plus Managed Disks: Azure Site Recovery can now replicate virtual machines that use Performance Plus enabled managed disks (Premium SSD, Standard SSD, and Standard HDD), giving high-IOPS and high-throughput workloads Azure-to-Azure disaster recovery with consistent performance across primary and secondary regions during replication, test failover, and full failover. How to use Instance Mix with Azure Virtual Machine Scale Sets: Instance Mix lets a Flexible-orchestration Virtual Machine Scale Set define up to five compatible VM sizes under a single "Mix" SKU, so Azure can choose from that list at scale-out based on your allocation strategy to ease regional capacity, quota, and pricing constraints.

Finally, Entra Automation: Users, Groups, and Conditional Access: A tutorial on using Microsoft Graph PowerShell to automate Entra ID user creation, security groups, group membership, and Conditional Access policies, with report-only mode for validating changes safely before enforcement.