Christophe Barre

CEO @TandemAIx.

AI brains, agents & risks for FDEs & implementation teams.

[Christophe Barre @barrec03]

@zkevinbai @espricewright How else was Palantir screening for mission-driven people? And Anthropic?

[Christophe Barre @barrec03]

@businessbarista congrats!

[Christophe Barre @barrec03]

Grok bot was a game changer for me. Similar effect to Claude Code for eng imo.

It's both deep/powerful yet the easiest AI tool out there. Wide as a puddle, deep as an ocean. And voice notes, computer use & speed are the real Claude-killers here!

My setup:
- I have a main bot that dispatches. "Chief of staff". I talk to it via voice note.
- 3 EAs it can dispatch work to.
- Digests for LinkedIn, X, Bookface, Events/Luma, Job postings (GTM signals)
- Follow-ups I should do today (HubSpot, tandem, mail, LinkedIn)
- A CCO, a network manager, an SEO specialist (with Sanity), a prospector.

LinkedIn messages via Kondo MCP.
Emails via Superhuman MCP.

The limits:
- writing isn't great (even after training). never send messages directly, always draft first (unless 0 risk)
- far from as powerful as Fable 5, so stay with transactional and research

But you can force Grok bot to use more advanced models. Use those instructions on some bots:
> use grok CLI on grok4.6-high-fast

Exciting, and this is just the beginning.

[delost @thedelost]

https://t.co/frSVznyGY7

[Christophe Barre @barrec03]

💯 something I've been saying for quite a while. FDEs (in its current meaning) will rise and fall as AI finds it market and as custom agents become real products.

FDEs as meant by Palantir will remain, but in a lot less companies.

[Dr Don Perugini @DrDonPerugini]

The CEO of AI company Decagon, now valued at $4.5 Billion, is a skeptic that Forward Deployed Engineers (FDE) should be needed to implement and maintain enterprise AI systems.

CEO Jesse Zhang  said “Long-term reliance on an embedded engineer is evidence the software is too hard to use, not proof that deployment requires one.”

“[A] customer spent a year with Sierra’s forward-deployed engineers and built three customer service workflows in that time. Zhang described the arrangement as a black box. Any new workflow, or any deeper look inside the conversations, meant going back through the engineers, who were eventually reassigned to other accounts.”

“After switching to Decagon, Zhang said, the same customer built seven new workflows within about a month. He credits the product, which he said lets a client’s own staff, including non-technical employees, operate it directly instead of routing every change through an embedded engineer.”

This is exactly what I would have expected, which I talked about in a previous post (see link in comments).

It is not practical to rely on FDEs to implement and maintain AI agent workflows for non-technical employees to automate aspects of their work.

(Cognitive) Workflows that represent how people think, reason and break down tasks to do their work are dynamic and personalized to each individual. Employees will want to update their own workflows, create new ones for new types of work, and share their workflows (expertise) with others.

Relying on an FDE is an impractical bottleneck. AI tools should have the ability for non-technical people to create their own workflows, by capturing their tacit knowledge (expertise) that lives in their head.

This is why I’m building @CogFlowAI. It uses cognitive science to elicit tacit knowledge (expertise) from non-technical people via a human-AI agent interaction model to create what we call a cognitive workflow. Like teaching a new hire.

AI agents then use the cognitive workflows to help people automate cognitive tasks (e.g. advanced knowledge work performed by experts and professionals), as well as preserve previously inaccessible institutional knowledge.

https://t.co/ngwy3t3Cug

[Christophe Barre @barrec03]

@fieldproducer I guess difference is that agencies had different roles for each.
FDE = eng + PM + business

[Christophe Barre @barrec03]

@dennishegstad @andruyeung True for some

[Christophe Barre @barrec03]

@rdominguezibar Sales → GTM → Operators now in SF 😂

[Christophe Barre @barrec03]

@RaphaelDeLio Great content! Could you share more of how you handle product decisions that might have an impact on the main product itself?

[Christophe Barre @barrec03]

@mfishbein What changed iyo? Just the fact FDE is popular now?

[Christophe Barre @barrec03]

Another absurdity. Good luck automating the relationship with the client. And the on-site visits.

[The Information @theinformation]

Google says AI agents can automate some of the costly data preparation work typically handled by forward deployed engineers.

The company still plans to hire hundreds of those engineers, but expects more of their work to be automated over time.

Read more: https://t.co/vxjOMWBufO

[Christophe Barre @barrec03]

@sethrosen @djpardis The leave in month six line is the whole job.

If the FDE is still there, you built a services company.

[Christophe Barre @barrec03]

"Product org looks 50% smaller because half the job moved into FDE (in GTM)."

Totally aligned with what I'm seeing and often a major, overlooked difference with "regular" engineers.

FDEs are their own PMs. Which comes with a ton of trade-off obviously. Often they also are their own account manager/business profile.

Question is: is it temporary state while AI is find its market, or will it remain as such?

I think it will stabilize after a while, as more AI product becomes real product OR the AI is just absorbed inside the companies themselves.

[rachel wolan @rachelwolan]

I analyzed 1,858 openings in Product, Design, and FDE from The @lennysan 100 (aka The AI-native leaderboard).

Here's what I learned:

1. The Product org looks 50% smaller because half the job moved into FDE (in GTM). The ratios look way off of trad benchmarks. 1 PM : 13.5 engineering openings, but if you include FDE, the ratio goes back to normal …  1 PM/FDE : 8.7 engineering openings. Outbound PM’ing (aka talking to customers and translating requirements) is now happening with FDEs. PMs are now “builders," responsible for translating edge cases.

2. Everyone being hired in AI is in the majors. 89% of Product and 76% of Design roles now require 5+ years of experience. There’s no farm system, no minor league.

3. The model is eating both the product and the interface. AI-native PMs spend their time on evals, edge cases, composability, and understanding model behavior. AI-native Design shifted toward prototyping, research, content design and trust. The titles look familiar; the day-to-day jobs don’t.

What do you think this will look like a year from now?

[Christophe Barre @barrec03]

@radienren @xeophon 😄 good one. FDEs now listed here https://t.co/x38ws3OuJb

[Christophe Barre @barrec03]

@KirillGoldinBiz @rachelwolan @lennysan The org chart is the lagging indicator. The work already moved. Titles will catch up messy.

[Christophe Barre @barrec03]

@davesaunders The Looker math is the filter. If the deal cannot support a person on site, you do not have an FDE motion. You have a job title.

[Christophe Barre @barrec03]

@andruyeung implementation / consulting / pre+post sales -> FDEs

[Christophe Barre @barrec03]

@luisvelasco Curious to know if you guys also fine-tune models for larger customers? What's technically the most challenging?

[Christophe Barre @barrec03]

@edzitron @luisvelasco +1

[Christophe Barre @barrec03]

@ChrisHayduk @luisvelasco Yes. Write the life sciences one. How evals change when the ground truth is a wet lab.

[Christophe Barre @barrec03]

Anthropic and OpenAI have only so long to lock enterprise revenues (using FDEs) before application layer catches up.

We already saw it with Grok Bot totally wiping the floor with Claude cowork UX.

Clock is ticking.

[SoftwareIQ @software__iq]

This week we've seen a ramp detected in Anthropic daily hiring trends. Daily posting trends now available in our platform. Sales and FDEs roles growing