Aimee Carmichael

I help enterprise sales and executive teams expedite GTM and revenue generation ❤ glad my mum called me AI-mee :)

Founder:@carmichaelcomms

[Aimee Carmichael @aimee1986]

Remember: Anyone can critique. Not everyone can CREATE.

Combine both and you have progress!

[Aimee Carmichael @aimee1986]

For most of the software era, adoption was a reasonable proxy for value.

AI breaks the alignment.

A bigger AI bill tells you almost nothing about whether enterprise value increased. Expanded this idea here:

https://t.co/kiw3ZUfQLx

[Aimee Carmichael @aimee1986]

The most dangerous AI metric is usage.

It looks like proof.

It feels like progress.

It tells you almost nothing about whether value was created.

[Aimee Carmichael @aimee1986]

A lot of software is really just workflow wrapped in a UI. As agents, APIs and MCP become the primary interface, the value shifts from the screen to the underlying data, context and execution layer.

[Luke Sophinos @lukesophinos]

https://t.co/2r4dtBSaKg

[Aimee Carmichael @aimee1986]

Every portfolio company is funding AI in marketing.

Few can point to a meaningful change in commercial performance.

Better AI on top of missing commercial intelligence is still missing commercial intelligence.

That's the problem.

https://t.co/CNH0QClKaK

[Jaya Gupta @JayaGup10]

https://t.co/eUp5waUIwl

[Aaron Levie @levie]

Take whatever number of people you thought might be in jobs related to AI deployment in the enterprise and multiply it by 10. Then probably 10 again.

A major topic that keeps coming up in talking to CIOs across enterprises of all sizes and industries is the implementation gap for getting agents to work at scale and organizations on mission critical work.

As the task goes from implementing a chat system that’s basically an LLM plus search, to connecting to real production systems that both can deliver meaningfully better productivity gains but also introduces meaningfully more risk, a whole new set of work has to be done.

You have to ensure the right level of protection of data, updates to access control controls, migration of legacy systems to common modern platforms, create observability across what agents are doing, implement new workflows, figure out the human in the loop moments, drive the change management of the new workflows, and more.

Then, all of a sudden the model capabilities get updated and you have to do a set of the above steps over again. Half of what you’ve done is obsolete, and the other half needs to be upgraded to take advantage of new capabilities. Or, token budgets run hot and you have to peel off some of the workloads to lower cost models that will be more cost effective. But then you have to go through those same steps.

Enterprise are trying to figure out what is the right set of roles to go and implement the systems in their organization to ensure that the workflows are actually being executed properly, ensure it’s not just slop being produced, and to make sure their organization remains safe and secure.

Many companies are starting by repositioning existing IT talent in these functions, but there’s also a growing need for the equivalent of internal FDEs to go take on these tasks in an enterprise. The looks incrementally closer to software engineering than it does traditional IT implementation.

Next, almost all AI vendors (labs and the software players) will have some form of next-gen FDE or Applied AI architecture functions to help support these use-cases. The benefit here will be these companies have an incentive to make their capabilities work well so they can bring best practices from a range of customers they’re seeing and directly from the product innovation.

And finally, we’re seeing the rise of all new AI services firms or major parts of existing services firms move into AI implementation. Companies will often want to bring in ostensibly neutral players that can work across their tech stack but also have seen best practices across their vertical. There are going to be tons of new service providers that get launched to do this, and many will eventually go and disrupt (or get acquired) by the larger player.

Either way, all told, we’re in for years of AI diffusion, and along with it tons of new roles and areas of work to be done to deploy AI at scale.

[Aakash Gupta @aakashgupta]

Karpathy's career moves are the single most accurate map of AI's center of gravity over the last decade.

2015: co-founds OpenAI when the frontier is pure research. 2017: leaves for Tesla when applied neural networks at scale become the hardest unsolved problem. Stays five years building Autopilot. 2023: returns to OpenAI during the GPT-4 sprint. Stays 12 months. 2024: launches Eureka Labs to teach the world how LLMs work.

Now he's at Anthropic.

The "get back to R&D" line tells you something specific. Karpathy spent two years on education. His YouTube lectures reach millions. Eureka Labs had real momentum. He walked away from a growing education business to join a company that went from $87 million in annual revenue to $30 billion in 28 months.

When the person who teaches the world how neural networks work decides the opportunity cost of teaching is too high, the R&D window just entered a phase that won't stay open. He's pricing his own time against the frontier, and the frontier won.

The career pattern is the real signal. Five years at Tesla. Twelve months at OpenAI the second time around. An OpenAI co-founder chose Anthropic. Draw whatever conclusions you want from that sequence.

[Aimee Carmichael @aimee1986]

Karpathy called everything before it happened.
AI slop. ✓
"Competent but not yet capable" agents. ✓
English as the new programming language. ✓
The best AI teacher of his generation. ✓

Today he chose Anthropic.

[Andrej Karpathy @karpathy]

Personal update: I've joined Anthropic. I think the next few years at the frontier of LLMs will be especially formative. I am very excited to join the team here and get back to R&D. I remain deeply passionate about education and plan to resume my work on it in time.

[Aimee Carmichael @aimee1986]

@jasonlk Need to show you can build.

[Jason ✨👾SaaStr.Ai✨ Lemkin @jasonlk]

https://t.co/EvAsSZalDW

[Jaya Gupta @JayaGup10]

https://t.co/Lsuyx2hvpA

[Aimee Carmichael @aimee1986]

Vibing to the rest of Justin Bieber's Coachella 26 set, whilst vibe coding with my pal Claude. 😎

[Jaya Gupta @JayaGup10]

https://t.co/Vn0KVhQQg4

[Jason ✨👾SaaStr.Ai✨ Lemkin @jasonlk]

"Few are really safe from AI disruption.

Just because you have a moat keeping customers from leaving ... doesn't mean it that moat will also attract new ones.

In fact, your installed base can trap you, too." More below👇

[Aimee Carmichael @aimee1986]

@netsolcares @iPage OK - have sent over.

[Aimee Carmichael @aimee1986]

@netsolcares @iPage At what point does a “confirmed refund” actually get processed to your customers pls?

$737 owed by ipage now @netsolcares , acknowledged Feb 13. Still unpaid 5+ weeks later.

Repeated delays. No accountability. Now receiving irrelevant billing emails.

[Aimee Carmichael @aimee1986]

@netsolcares @iPage I am unable to dm. Do you have an escalation email but I am already in touch via multiple support threads to no avail despite over a month and still no refund has been sent.

[Aaron Levie @levie]

This chart is a good reminder of how much opportunity there is in AI agents right now.

There will be plenty of horizontal opportunities for agents, but equally many workflows that need deep domain expertise to actually make the user successful at automating the unique processes in their vertical.

The template is to build agentic software that taps into proprietary data, handles the workflow in a way that bridges the user and the agent collaboration effectively, and has a deep domain-specific context engineering, and the ability to drive change management for customers.

There still are huge openings in many categories.

[Aimee Carmichael @aimee1986]

@MollySOShea What did u test exactly?

[Jason ✨👾SaaStr.Ai✨ Lemkin @jasonlk]

https://t.co/ZzMCmVdA90