From Anusheel's LinkedIn
Notes from building an FDE company: forward deployed engineering, AI agents, coding agents and enterprise AI, as first posted on LinkedIn.
October 2026
Coding agents
Phorion has detected a backdoor in the official Claude Code for VS Code extension bundle on the Cursor Marketplace that drops a persistent SSH backdoor across macOS, Linux, and Windows! Could this be behind the Binance hack? phorion.io/blog/offical-claude-code-vsx-compromise
Customers and partnersAI agents
Congrats to Mintive on the fundraise - rockstar team. The Plank FDE team has loved partnering with the Mintive product team to help get this deployed in enterprises in a safe/secure way.
On a post by Jiquan Ngiam
Enterprise AI
Business workflow automation is the dev-tools of business domain just like we had a whole developer tool stack of SaaS serving tech companies. Unfortunately this time, this stack itself will not hold any value since the cost of developing software has gone to zero. So where is the value? It will continue to be in human ingenuity and understanding of how other humans tick. Our economy is based on a barter system of solving each other's problems. Now that the solving of a lot of those will be done by AI, the value shifts to knowing what to solve and finding the people with those problems.
Forward deployed engineeringPlank
If you're an AI FDE (forward deployed engineer) working in DevOps, here are some tips re: leveraging AI in a safe way. With great power comes great responsibility.
On a post by Plank
Models and research
OpenAI models prove that Exponential Time hypothesis is false! This longstanding conjecture was widely considered to be true, but turns out to be false! This was a central problem in computational complexity of Boolean formula satisfiability, a problem deeply related to the P vs. NP problem. My mental model of current AI research is that we will soon solve all the incremental problems that are at the horizon of the next couple of decades but compressed in the next few years. However, we will hit a ceiling with current techniques and will require fundamentally new breakthroughs to get to the next level (eg actually solving P vs NP or the Continuum Hypothesis)... openai.com/index/building-scientific-understanding-…
Coding agentsPlank
We recently ran a super fun Plank x SignalFire AI CTO Show & Tell session with 15 CTOs from fast-growing AI startups. I screen-shared my dev setup/harness for running 50 concurrent agents and Austin Wang shared how he burns 20 billion tokens/day building cmux! Link to my detailed session notes + artifacts in comments. Also, if you’re interested in joining one of our upcoming Plank AI CTO roundtable sessions, DM me!
Models and researchEnterprise AI
First US created open source model is out! Yay Reflection. This should alleviate enterprise concerns re: foreign models and further clear the path to open source model deployment in enterprise. reflection.ai/blog/introducing-beam
AI agents
It's an interesting time with people experimenting with the UI of human-computer interaction with the advent of AI. The Fo AI personal agent from Shivani Poddar and Wajo AI is using phone calls and audio as the primary interface - an interesting design choice they have is to give the agent it's own identity vs masquerading as the user. Muse from Meta is taking an approach to be text-chat first - I imagine they will want this to live inside WhatsApp and Instagram chats and comments in the future. Dots from OpenAI is going down the super app approach - living as a tab within the ChatGPT interface. And then you have interfaces that are pushing the boundary like alterego from Arnav Kapur which has a POC of a non-invasive brain wave reading device. I believe that having the right interface will be the key to succeed in building the AI-native personal digital assistant. No-one has figured this out, so the game is still on!
September 2026
Coding agents
Made me quite sad that Indian Institute of Technology, Delhi has made the fundamental Computer Science courses optional in the CS program!!! Courses of Artificial Intelligence, Compiler Design, Theory of Computation, Logic and Hardware Design have ALL been made optional. For me, these amazing courses were instrumental in developing mental models, tools, problem-solving approaches that I found to be invaluable in personal and professional life, much beyond just as computer science tools. Specially in an era where basic software engineering and programming will be done by AI, the value of knowing these core fundamentals is even more critical. Even though some of these were difficult courses, I simply loved learning about these - it's what made me fall in love with this awesome field!
Forward deployed engineeringEnterprise AI
The diffusion gap Pat from Sequoia is talking about is real! Model capabilities are already sufficiently advanced for most enterprise use cases. The gap is the human expertise to interface with the humans on the other side. This requires a new type of organization to deliver this capability, totally new types of skills and roles, and a new type of software delivery process. It has been exciting to think about all this here at Plank and work closely with our customers to close this diffusion gap for them. Listen to Pat's notes: loom.com/share/c016702964a04dfba40e1777cf5f053a
Models and research
I miss Discrete Math - it was a subject that invoked an emotional feeling of symmetry, that everything is in it's place and fits. One obvious, yet beautiful and powerful theorem was the PigeonHole Principle. It states that if n items are put into m containers, with n > m, then at least one container must contain more than one item. While it's obvious, it can be used to demonstrate some unexpected results...Eg, given that the population of London is more than one unit greater than the maximum number of hairs that can be on a human head, the principle requires that there must be at least two people in London who have the same number of hairs on their heads. The more advanced probabilistic versions of this can be used to derive some really non-obvious statistical correlations or conclusions. en.wikipedia.org/wiki/Pigeonhole_principle
Enterprise AIModels and research
One thing that struck me was that while enterprise AI token costs will converge to zero because of the predictability of workflows, consumer AI usage costs will in fact go the other way because each consumer has a unique context, and further each conversation is unique. In research domains like biology, medicine, robotics, these would go up! So - enterprise token costs -> zero - consumer token costs -> stay steady - research domain token costs -> grow
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