Nik Singh

Now: Host @terminalvalue_
Prev: co-founder of @getcenterhq (acq by @americanexpress)

[Nik Singh @NikSingh19]

@DrewCohenMoney Good video. Worth a watch

[Nik Singh @NikSingh19]

@Jason Can’t hurt his free throw percentage, might actually go up

[Nik Singh @NikSingh19]

@SCHDETF No party this weekend but we are close man!

[Nik Singh @NikSingh19]

@SCHDETF @SCHDETF , now up to $32.80. Please feel free to say "I told you so" if it jumps up another 20 cents.

We can both party this weekend in that case.

[Nik Singh @NikSingh19]

100%, over the long term schd will do great. In just saying that Qcom and txn are up huge today so we probably don’t get 33 today as I imagine we will get a normal mid to late day sell off in those names as some investors take profits if they own them directly.

Besides mrk most of the other names are relatively small holdings.

But long term I actually like schd more than spy given where multiples are for each and how dependent spy is on mags and semis

[Nik Singh @NikSingh19]

Think it’s more related to the fact that seagate hinted today that they would not build out additional factories in the near term and instead focus on building storage density into their disk drives. This is first major sign that an ai hardware exec is not ready to underwrite “unlimited ai demand”

[Nik Singh @NikSingh19]

Looking forward to launching the first episode of @TerminalValue_ with CEO and Co-Founder of Frugal, @mweider

[TerminalValue @TerminalValue_]

AI-native software has a gross margin problem.

@mweider (CEO and Co-Founder of Frugal) explains why the next phase of cloud cost management may move from dashboards and budgets to Application Cost Engineering — tracing cloud, token, and observability costs back to the code driving them.

Full episode Thursday, May 7th.

[Nik Singh @NikSingh19]

@Eselim13 @oguzerkan Agreed, eventually supply catches up. It always does

[Nik Singh @NikSingh19]

@svarncapital @oguzerkan Yeah, this is a more sensible take. Longer cycle, but not new regime

[Nik Singh @NikSingh19]

Yeah, it’s an interesting point. I would group this into two categories:
1. Coding tools
2. Models used at inference time

For coding tools, maybe there is a bit more lock in due to the sales motion and deployment motion, I can buy that but even those can be switched out.

However, on the model inference time, most applied ai startups I am talking to are using evals for their major in product inference workloads and are swapping to cheapest models that can meet their eval kpis

[Nik Singh @NikSingh19]

I’m not sure I buy strong network effects here yet.

Two months ago, Anthropic looked clearly ahead in coding. Now Codex is gaining real developer mindshare. Most dev orgs also seem likely to keep relationships with multiple model providers.

And once open-source models become “good enough,” startups will adopt them quickly for cost reasons.

So yes, supply can stay constrained. But I’m less convinced today’s leader turns into a durable winner-take-most platform.

[Aaron Levie @levie]

Another week on the road meeting with a couple dozen IT and AI leaders from large enterprises across banking, media, retail, healthcare, consulting, tech, and sports, to discuss agents in the enterprise.

Some quick takeaways:

* Clear that we’re moving from chat era of AI to agents that use tools, process data, and start to execute real work in the enterprise. Complementing this, enterprises are often evolving from “let a thousand flowers bloom” approach to adoption to targeted automation efforts applied to specific areas of work and workflow.

* Change management still will remain one of the biggest topics for enterprises. Most workflows aren’t setup to just drop agents directly in, and enterprises will need a ton of help to drive these efforts (both internally and from partners). One company has a head of AI in every business unit that roles up to a central team, just to keep all the functions coordinated.

* Tokenmaxxing! Most companies operate with very strict OpEx budgets get locked in for the year ahead, so they’re going through very real trade-off discussions right now on how to budget for tokens. One company recently had an idea for a “shark tank” style way of pitching for compute budget. Others are trying to figure out how to ration compute to the best use-cases internally through some hierarchy of needs (my words not theirs).

* Fixing fragmented and legacy systems remain a huge priority right now. Most enterprises are dealing with decades of either on-prem systems or systems they moved to the cloud but that still haven’t been modernized in any meaningful way. This means agents can’t easily tap into these data sources in a unified way yet, so companies are focused on how they modernize these.

* Most companies are *not* talking about replacing jobs due to agents. The major use-cases for agents are things that the company wasn’t able to do before or couldn’t prioritize. Software upgrades, automating back office processes that were constraining other workflows, processing large amounts of documents to get new business or client insights, and so on. More emphasis on ways to make money vs. cut costs.

* Headless software dominated my conversations. Enterprises need to be able to ensure all of their software works across any set of agents they choose. They will kick out vendors that don’t make this technically or economically easy.

* Clear sense that it can be hard to standardize on anything right now given how fast things are moving. Blessing and a curse of the innovation curve right now - no one wants to get stuck in a paradigm that locks them into the wrong architecture. One other result of this is that companies realize they’re in a multi-agent world, which means that interoperability becomes paramount across systems.

* Unanimous sense that everyone is working more than ever before. AI is not causing anyone to do less work right now, and similar to Silicon Valley people feel their teams are the busiest they’ve ever been.

One final meta observation not called out explicitly. It seems that despite Silicon Valley’s sense that AI has made hard things easy, the most powerful ways to use agents is more “technical” than prior eras of software. Skills, MCP, CLIs, etc. may be simple concepts for tech, but in the real world these are all esoteric concepts that will require technical people to help bring to life in the enterprise.

This both means diffusion will take real work and time, but also everyone’s estimation of engineering jobs is totally off. Engineers may not be “writing” software, but they will certainly be the ones to setup and operate the systems that actually automate most work in the enterprise.

[Nik Singh @NikSingh19]

@SCHDETF You rock Craig

[Nik Singh @NikSingh19]

@SCHDETF Thanks for doing this Craig. Appreciate you

[Nik Singh @NikSingh19]

@vibes_nate Please let us know where the s&p500 finishes this year? We will act accordingly

[Nik Singh @NikSingh19]

@SCHDETF Congrats brother!

[Nik Singh @NikSingh19]

@thesamparr Thx for sharing Sam

[Rajeev Singh @rajeev_singh]

less hype, more solutions. ⁦⁦@scala_ai⁩ is grounded in ai native product tied to operators who have been in the battle. winning combo. https://t.co/NV3zlDMU0v

[Nik Singh @NikSingh19]

@thesamparr Congrats, great pod! Keep up the good work