Fantastic read. Everyone talks about AI changing the way we do things, and this is a clear explanation on how AI changes hiring. It's from both sides - how to find, interview, and hire, but also how to be interviewed. Must read.

Your AI is vaporware without deep integrations with your customers’ systems of record. Introducing @WithAmpersand: integration infrastructure for enterprise agents. We power 11x, Orb, and Square's ability to take agentic action in systems of record like Salesforce, SAP, NetSuite, and Workday. Ask anyone serious about building AI and they’ll tell you: - The SaaSpocalypse didn’t happen. The world depends on CRMs, ERPs, HRISs, and ITSMs. - Your customers customized their deployments beyond recognition. - Systems of record companies are basically monopolies. They never had to make their APIs and MCPs user-friendly. - Docs don't explain half the weird edge cases you'll hit. - One bad write can blow up your pilot or renewal. - Once you finally get it working, someone changes a field and it breaks again. The world's data lives in structured databases. AI needs a translation layer to work with it. So, for your AI to truly transform the way enterprises work, you'll need deep integrations built for: - scoped permissions - bi-directional actions - real-time speed for agents - custom objects, fields, and workflows for each of your customers We built Ampersand to power the future of software. AI didn't trivialize writing integrations. It made them the critical path. I believe that deeply, so I didn't make a launch video about Ampersand. Instead, it's the best builders I know explaining just how big of a challenge this is.

This is how we streamline AI implementation across 26 companies in a PE portfolio. Each company has 10 departments, and each department has 10 workflows. If you do them one at a time, you need "50,000 forward deployed engineers across three years." Our method is to group the companies by system of record. For finance, the 5 companies on NetSuite are one group. The 4 companies on Dynamics are a different group. Then you find what NetSuite can do and what it cannot do. You build only for the gaps. In one group, the processes are still different. The 5 NetSuite companies used 12, 9, 15, 18 and 13 steps for their finance process. The target is 6 steps, with agents at specific points. You will not often make all 5 exactly equal. You can come close if you map each process with the stakeholders before you change it. A single company or 26, sort the work by its system before you build.

Today, Nolla Health became the first organization in the U.S. (and possibly the world) to receive regulatory approval for an AI to issue initial prescriptions. This makes Nolla the first ever actual end-to-end AI doctor.

AI agents should add to the systems your client already uses. One client spent several years and $10 million on one ERP migration. Offer that client a move to an AI-native CRM, and "you've lost them." So we build our agents inside the systems of record. They read the data in Salesforce and do the work in Salesforce. When a human must be in the loop, the agent sends a message in Slack. The staff need no new training, so they use the agents faster. SMB clients with no CRM are the exception. You can put them on one. If you want to work as a forward deployed engineer, build on what your client already uses.

If you're a college student looking to be an HR intern for a few weeks (potential to go full-time), send me a DM/email. Must be based in the US, remote ok. Need you to help us hire our next 50 people.

Yesterday we previewed Griffin, a Human Interaction Model capable of seeing, hearing, sounding, and looking like a human does. There have been a lot of questions, so I wanted to take a moment to share our thoughts. By way of introduction: Tavus is a research lab focused on enabling machines to meet us where we are, and to understand the nuances of how we communicate beyond words. Griffin, our latest model, isn’t publicly available yet. Yesterday’s announcement was a limited research preview to demonstrate what the model is capable of. With AI progressing so quickly, we prefer to share breakthroughs openly and in real-time as we work on a safe public release. Face-to-face is how we evolutionarily communicate, it carries the most meaning and intent, and we want computers to be able to help with work that benefits from that emotional understanding, expression, and immersion. Some examples of the kinds of use cases we care deeply about: - A tutor that can build understanding of how a student learns, see exactly when there is confusion or disengagement, and adapt the lesson to fit them. - A health expert that can answer any questions about your upcoming appointment or prescription, at the pace you want, at any time you need, even on a weekend. - A language coach that you can practice speaking with, that can correct your movement and pronunciation, and help build confidence to have real conversations - Or the perfect assistant for everyone, that understands intent, knows how you work and remembers what matters. We’re working with partners on safeguards and systems for disclosure, as well as inviting discussions with officials around wider regulation and safe use. People will always know they’re interacting with AI, while providing an interface that removes the need to ‘speak computer’. We believe in a future where computers understand us well enough to make technology more accessible, more useful, and make us more capable as humans. That is the world we want to build, and we understand the responsibility to do so safely.

Happy Friday. We're hiring Forward Deployed Engineers at Varick. You'll be deployed inside some of the largest companies on Earth, solving their hardest problems and redesigning how the whole business runs so it's AI-native. You won't get bored, because every 3-6 months there's a new company and a new problem waiting for you. Requirements for the role: - Strong engineering. You can write code, design systems, and estimate timelines and resources accurately. - Strong consulting. You communicate clearly, present well, and can command a room of C-suite executives at Fortune 100 companies. - Hungry. We're a lean team of the sharpest people in applied AI, and we're here to win. Apply through our website and we'll read every single application. If it's a fit, we'll reach out by email.

Top 25 questions the world's largest C-Suite executives ask us re: AI transformation in the enterprise: 1. Are AI transformation firms more like McKinsey or Palantir? 2. We launched Copilot for everyone to use, but why hasn't it changed anyone's week? 3. We've done training, custom GPTs, enablement. What's next? 4. Where do we even start with AI? 5. How do we choose the first workflow to AI-enable? 6. Should AI be owned by IT or the business? 7. Do we really have to train all 10,000 people in the company? 8. How do we enable everyone to build agents, without ending up with agent sprawl? 9. Why are our token costs increasing? 10. How do we know if using AI is really cheaper than just hiring another person? 11. Which steps should remain human? 12. How do we communicate AI won't replace our team's jobs? (Will it?) 13. Will we own the agents you build for us? 14. What does maintenance look like in 6 months? 15. Will our data leave our environment? 16. What models should we be using? What do we do when they're deprecated? 17. Can we run these agents inside our ERP, or will people have to use a new tool? 18. Do we need to clean up our data first? 19. How long until we start seeing ROI? 20. When you talk about ROI, do you mean headcount? 21. How do we keep growing our business without increasing headcount? 22. Why did our last pilot project die in POC purgatory? 23. Our process documentation doesn't match what our team actually does – how do you know what to automate? 24. How do we keep up with how quickly AI is changing? 25. If AGI becomes a reality tomorrow, where should we point it? We solve these every day at Varick. Work with us below:

How does an FDE audit a real workflow? We did one for a $5B public software company with 150+ products. Step 1: Read their process document. - Theirs had 7 steps: build the quote, submit, Deal Desk, approve, send, negotiate, sign. Step 2: Interview more than one person. - One person can tell you the wrong story. Step 3: Put process mining agents on the CRM. The agents found 20 steps and 7 loops. - 61% of requests go from step four or five back to step one. - Legal sends the request back 12% of the time. - 30% of the time, a new quote must get approval again. Step 4: Map the real workflow. - 4 of our deployed engineers did this. - The CRO and CFOs told us, "I feel like you understand our department better than we do." Before this map, no one in the department had seen the workflow this clearly. Do this audit before you use AI, so you know which process is broken.

How does an FDE map the processes to deploy AI in a real business? We use 3 sources. Source 1 is people. - Companies tell us: "this person's been at the firm for 20 years and they just handle it." - We hear this from Fortune 50 companies and from SMBs. - So in finance, you talk to the head of AP, AR, reconciliations, billing, banking, and FP&A. You make a "human API." Source 2 is the systems of record. - With real-time access to Salesforce for 3 to 4 weeks, you see what data comes in and how often people correct it. Source 3 is the documents in SharePoint, Drive, Notion, Slack, Teams, and Gmail. If you use the system data without the interviews, you miss half of the picture. The split is different for each company. A large company can have 10 years of Salesforce data. An SMB has most of it in people's heads. Choose your angle for each company before you build.

Six AI debates: 1. Frontier models get cheaper vs. more expensive 2. The world is underinvested vs. overinvested in compute 3. The gap between frontier and open source shrinks vs. widens 4. AI is job-destroying vs. job-creating 5. AI detection & regulation keeps pace with generation vs. never catches up 6. AGI/ASI is in sight vs. unachievable Answers vary heavily based on age, job, industry, time-spent-on-Twitter.