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Mike Fishbein

@mfishbein • 10,043 subscribers

Founder at Atherial, AI strategy and engineering agency. Plug AI engineers into your company. Read State of AI Report: https://t.co/pzpCt8pVr2

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Box CEO Aaron Levie (Aaron Levie) calls out the MASSIVE opportunity for AI deployment services: "every single one of those companies, whether that's a 50-person firm or a multi-100,000 person firm, is gonna need an army of people to go in and help them with that transformation" "when you go to that law firm and you go to that pharma company and you go to that bank, they need something that bridges the core technology to their workflow in their business process" "somebody has to go into that organization and get it set up, and somebody has to go and provide domain expertise to this model so it really understands our particular business process" Vendor FDEs are incentivized to get you hooked on their platform and to spend more money. Indie FDEs are incentivized to use the best tool for the job and to save you money. Hire indie FDEs.

Box CEO Aaron Levie (Aaron Levie) calls out the MASSIVE opportunity for AI deployment services: "every single one of those companies, whether that's a 50-person firm or a multi-100,000 person firm, is gonna need an army of people to go in and help them with that transformation" "when you go to that law firm and you go to that pharma company and you go to that bank, they need something that bridges the core technology to their workflow in their business process" "somebody has to go into that organization and get it set up, and somebody has to go and provide domain expertise to this model so it really understands our particular business process" Vendor FDEs are incentivized to get you hooked on their platform and to spend more money. Indie FDEs are incentivized to use the best tool for the job and to save you money. Hire indie FDEs.

240,273 views • 1 month ago

Ben Horowitz (benahorowitz.eth): The laws of physics of company building changed. You can throw money at problems now. "If you have the data and you have enough GPUs, you can solve damn near anything." Technical moats aren't dead. They're just turning into capital moats. Whoever spends more on data and compute can catch up (or stay ahead). Strong teams that are well capitalized get exponentially more leverage than before.

Ben Horowitz (benahorowitz.eth): The laws of physics of company building changed. You can throw money at problems now. "If you have the data and you have enough GPUs, you can solve damn near anything." Technical moats aren't dead. They're just turning into capital moats. Whoever spends more on data and compute can catch up (or stay ahead). Strong teams that are well capitalized get exponentially more leverage than before.

38,667 views • 9 days ago

Databricks CEO Ali Ghodsi went off on a16z pod about enterprise AI adoption: "They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali says most companies are still just using chatbots. There's hardly any agentic transformation. Why is that? "The models are smart enough, but they just don't have the context that exists inside of any organization." "They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "If you just fused that and gave that context into the AI models...there's so much productivity gains you could get for any organization on the planet." Context Creation is the biggest opportunity in AI right now.

Databricks CEO Ali Ghodsi went off on a16z pod about enterprise AI adoption: "They're just so far behind in the adoption curve of actually automating things and getting value out of this stuff." Ali says most companies are still just using chatbots. There's hardly any agentic transformation. Why is that? "The models are smart enough, but they just don't have the context that exists inside of any organization." "They have not been in every meeting. They don't know what's in everybody's heads. They don't know all the processes." "If you just fused that and gave that context into the AI models...there's so much productivity gains you could get for any organization on the planet." Context Creation is the biggest opportunity in AI right now.

151,188 views • 1 month ago

Databricks Chief AI Scientist Jonathan Frankle (Jonathan Frankle) says the bottleneck to enterprise AI adoption is evals, not models. "The models are good enough at a lot of really complex enterprise tasks and knowledge tasks" "the real challenge is describing what you want AI to do and knowing whether AI did it"

Databricks Chief AI Scientist Jonathan Frankle (Jonathan Frankle) says the bottleneck to enterprise AI adoption is evals, not models. "The models are good enough at a lot of really complex enterprise tasks and knowledge tasks" "the real challenge is describing what you want AI to do and knowing whether AI did it"

28,166 views • 9 days ago

Writing evals is about to be the highest paying job in AI. Evals are how you enable agents to verify their own work. Evals create the grading rubric to quantify "what good looks like." Give an AI agent the rubric and it can try, get graded, iterate, and try again until it's verified. The agent can even do it hundreds of times in parallel without a human in the loop giving feedback. That loop is called hill climbing: > An expert writes down what "good" looks like (the rubric) > The agent does the work > An AI judge scores the work against the rubric > The agent iterates until it gets verified It hill climbs toward whatever the rubric rewards. So the person who writes the rubric controls the quality of the agent's work. Whoever writes the grading rubric produces the output. Harrison Chase nails it: "If value is derived through verification, I think the people who win are the domain experts" "a lot of the value will accrue to whoever can write those verifiers, capture that domain knowledge."

Writing evals is about to be the highest paying job in AI. Evals are how you enable agents to verify their own work. Evals create the grading rubric to quantify "what good looks like." Give an AI agent the rubric and it can try, get graded, iterate, and try again until it's verified. The agent can even do it hundreds of times in parallel without a human in the loop giving feedback. That loop is called hill climbing: > An expert writes down what "good" looks like (the rubric) > The agent does the work > An AI judge scores the work against the rubric > The agent iterates until it gets verified It hill climbs toward whatever the rubric rewards. So the person who writes the rubric controls the quality of the agent's work. Whoever writes the grading rubric produces the output. Harrison Chase nails it: "If value is derived through verification, I think the people who win are the domain experts" "a lot of the value will accrue to whoever can write those verifiers, capture that domain knowledge."

24,318 views • 8 days ago

This is what product-market fit looks like.

This is what product-market fit looks like.

58,978 views • 3 months ago

OpenAI’s “The Deployment Company” is stealing clients’ IP and selling it to their competitors. This used to be a conspiracy theory. It’s not anymore. Watch this clip of OpenAI’s Head of Forward Deployed Engineering (speaking at South Park Commons). He says it directly: FDE service revenue doesn’t matter to OpenAI. Only Recurring Revenue from product does. To turn FDE engagement into product, OpenAI needs three things: 1. Subject matter expertise 2. Context to train on 3. Engineering And clients are paying OpenAI for them to get all three. OpenAI’s FDE clients don’t matter to them. They’re just a data source. Indie FDE agencies, on the other hand, obsess over clients because that is their business model. There isn’t a backend offer. Indie FDE agencies make money from happy clients. The Deployment Company makes money when shoplift clients’ data. Incentives matter. Hire Indie FDEs.

OpenAI’s “The Deployment Company” is stealing clients’ IP and selling it to their competitors. This used to be a conspiracy theory. It’s not anymore. Watch this clip of OpenAI’s Head of Forward Deployed Engineering (speaking at South Park Commons). He says it directly: FDE service revenue doesn’t matter to OpenAI. Only Recurring Revenue from product does. To turn FDE engagement into product, OpenAI needs three things: 1. Subject matter expertise 2. Context to train on 3. Engineering And clients are paying OpenAI for them to get all three. OpenAI’s FDE clients don’t matter to them. They’re just a data source. Indie FDE agencies, on the other hand, obsess over clients because that is their business model. There isn’t a backend offer. Indie FDE agencies make money from happy clients. The Deployment Company makes money when shoplift clients’ data. Incentives matter. Hire Indie FDEs.

57,984 views • 4 months ago

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

We've built 40+ AI agents and internal tools. The hardest part is Context Creation. AI runs playbooks and makes judgment calls for you. But without your company's context, you get slop. Context Creation means extracting the subject matter expertise and playbooks that live in people's heads, not in LLM training data, or even your tools. As forward deployed engineers (FDEs), we create context and turn it into code. We evaluate the business impact, how it aligns with the dev roadmap, and come up with creative solutions. We built The FDE Factory to replace ourselves. It drives AI adoption inside our clients' companies by running discovery sessions using prototypes to create context. Here's how it works: We put a prototype in front of a stakeholder. The stakeholder gives feedback via voice while they're using or reviewing it. Then our FDE Factory Agents builds in their expertise in minutes: > Context Agent reviews the codebase and feedback, extracts the requirements, and creates a spec > Scope Agent checks the spec against the development roadmap, validates it, and hands it off > Engineering Agent builds a new feature and wires the integration > QA Agent runs tests to prove to itself it works > PR merges, feature goes live, product updates itself in real time It's like the nontechnical stakeholder wrote the code without even knowing it. Coding agents are great at turning good development plans into code, and they're getting better at turning context into good development plans in collaboration with professional engineers. But nontechnical people are capped on what they can build without product people and engineers. The bridge that takes nontechnical people from vibe coding basic apps to building production AI tools that run on first party context is FDEs. Our new FDE Factory gives you the system to go from idea to production. Context Creation is the first and most important step in our FDE lifecycle, and we just automated it. Now clients get the right agents and tools built for them, customized to their unique business and encoded with their expertise. PS: If you're building AI agents within your company, reply "Playbook" and I'll DM you the entire FDE playbook we've run with 30+ companies. It covers finding high-impact AI use cases, building them, and deploying them across the org.

10,171 views • 5 months ago

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