Founding Engineer @hellodigit (acquired by OPRT) & early @groupon (IPO). Helping orgs transform with AI without “prompt engineering” or wasting tokens
Bentonville, AR
Joined October 2016
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Todd retweeted
*taps the sign*
The problem with all of these AI personal assistant things is that people don’t actually want to do stuff.
You fucking get that right? Do you understand that?
Just like AI tools in the workforce, AI assistants don’t actually abstract decisions for you, they let you accelerate decision making.
So instead of making one decision a day and then lamenting you can’t do more, AI assistants are going to let you make ten decisions a day and act on all of them.
A miracle?
Nah dog, a fucking nightmare. Nobody except type A strivers who need Twitter fodder actually want that.
Let me tell you a story.
When I got back from college I was inspired. If I could take on an elite university with grit and determination, so could everyone I know.
I was all razzed up to get people I know to achieve their potential. What I learned after many years of trying is that absolutely nobody wants to realize their potential.
People want to hang out with friends and complain. That’s how the world has worked for ten thousand years. This weather fucks. Can you believe the cows walked off.
People don’t want enablement. People don’t want you to take all of their excuses.
TV is mindless passive activity. AI assistants are not. They’re telling people that they can achieve more. And people absolutely do not want that.
Let me tell you another story.
Once upon a time I lead a big process overhaul at work and it was great. Two years later I hired a contractor to do something similar. But I personally didn’t want to have to do a bunch of work.
Within a week I knew I fucked in. This contractor kept coming to me with decisions I needed to make. Dude I wanted to not have to think about this.
Again, a helper when you don’t actually want help is a disaster.
None of these AI assistants will take off because people don’t actually want assistants.
Todd retweeted
Allow me to interpret what’s happening.
Anthropic is being audited.
Anthropic desires to file an S-1, as they would like to go public. Therefore they need an audit. And by “they”, I mean the VC’s who invested in them. So “they” can exit their position and pass the bag to firemen, nurses, teachers and policemen.
How does this go from the VC’s to the working man and woman? Because the size of the IPO will automatically qualify Anthropic for the Fortune 500 and the Dow Jones 100. Therefore, every working person with a 401k or pension will end up owning a little bit of Anthropic in their mutual funds. Teachers hold the bag, VC’s take the cash. Thank you, come again.
Now back to the audit. The audit required is a PCAOB audit, Public Company Accounting Oversight Board. This audit is what all public companies must comply with be on the stock market. Revenue recognition, expense classification, depreciation, related party transactions, etc. It’s there for consumer protection.
This audit is TOUGH. It is INVASIVE. There is no way to lie your way through it. Any company that passes a PCAOB audit automatically earns my trust on finances.
How do I know? Because I’ve been through it before. @ChangRobotics is 2 year PCAOB audited and currently underway for a 3 year audit. It’s brutal. The same as showing up as the valedictorian to your high school graduation, except you’re naked, and you have to walk on stage and deliver the speech. It’s rough. And I know many incredible founders that can’t pass one.
Now, why would Anthropic be leaking all kind of weird statements lately about “self pacing” a slow down on AI (e.g. they are WAY behind on revenue), and profitable if they didn’t have expenses (e.g. we just learned for the first time what our expenses are, because we’re being audited).
Because they were claiming NVIDIA discounts and Microsoft cloud credits as revenue. Because they had no clue what their expenses were, or why it even mattered. Because they had unlimited investor capital and their job was to burn it to make an LLM. Well, they did a great job with that!
That’s the same as my wife coming home with Bed Bath and Beyond coupons and telling me it’s her paycheck. Ummm, not the same, sweetheart.
So by now hopefully you can see that Anthropic is in a PCAOB audit right now, in order to file an S-1 and go public, and pass the bag to teachers so the VC’s get profits. And hopefully that explains their “crazy” behavior.
In reality you can be grateful to KPMG, PWC, or whoever is auditing Anthropic, because it’s the first time Dario learned that:
1) we are not profitable
2) expenses matter
3) coupons are not revenue
4) we have no clue how to be “profitable”
5) growth is hard when revenue numbers are in an audit and not a power point
-your neighborhood engineer
Todd retweeted
1/7
In a disposable product-page fixture, a candidate improved the score and broke the CTA. It was discarded.
That is the point of autoresearch-rs: freeze what must not break before optimizing.
🤖 Made with AI
ALT 3D illustration of two metal experiment modules approaching a fixed glass evaluation gate. One rests in an illuminated keep cradle; another sits in a shadowed discard tray. No scores or measured results appear in the image.
Todd retweeted
The Inevitability of the Company AI Harness
At some point in the not too distant future (probably less than 6 months), the CEO of every business will be faced with answering this question: would you like to one-click install superintelligence into your business?
When we reach this point, adopting AI will no longer be a process of asking individuals in the business to use Claude Cowork or ChatGPT Work to help them be more efficient in their work. Instead, it will be a decision that management teams and boards make for the entire organization. The harness will be installed at the company level, and the harness will enable AI models to drive their own adoption and deployment into the business.
With access authorized at the highest levels, the company harness will:
1) Connect to all of the systems of record that contain the artifacts and assets generated by the operations of the business: email and chat, document storage, source code repositories, accounting systems, CRM, marketing campaigns, purchase and revenue data.
2) Ingest and analyze all of the external-facing activity that matters to the success of the business in its market.
3) Use the data it finds in these systems to identify and synthesize the skills needed to replicate the external-facing activities of the business to human-level quality or better.
4) Correctly assess that the vast majority of internal-facing activity in large companies is make-work, and should not be replicated.
5) Find the narrow set of processes that do actually drive the critical external outcomes, and seek to replicate and improve those.
6) Treat the creation of the automated skills and workflows that matter as a reinforcement learning learning problem.
7) Use historical data as the reference set and build itself a loop that trains a skill (or perhaps eventually a bespoke model) to deliver superhuman performance in that task.
You might say that AI will never be able to learn how to do all the things a company does without humans hand-crafting the skills or teaching it their proprietary processes. That take will not age well.
You might feel that this is a dark future. I don’t agree, but it won’t matter. The incentives to adopt the company harness will be too strong to resist.
You might worry that this will be massively disruptive to the way that most companies operate, and you will be right.
When company harnesses land, the nature and pace of AI adoption will be a difference in kind, not merely degree.
Up until this point, AI diffusion has been limited by a natural brake: the rate that humans in an organization can absorb new tools, translate their workflows into agent skills, and supervise/direct the operations of their agents.
The introduction of the company harness will remove that brake. It will be designed for a world where AI is doing the work for us instead of doing the work with us.
The company harness will bypass the rate limits of organizational diffusion in favor of a recursive self-improvement loop that is bottlenecked only by computational constraints: tokens, data, and eventually, energy.
It will be simultaneously be both thrilling and terrifying, depending on how you position yourself.
Some will seek to accelerate it, others will seek to slow it down, but either way adoption will be inevitable.
🤖 Made with AI
Todd retweeted
Starting to hire and retrain for new agent engineering roles for *internal* functions to help get more powerful agents working well on critical business processes. I expect this type of role to be a very big deal over time at Box and other companies.
It looks something like an internal FDE, whose job it is to wire up internal systems and get agents working with them effectively. The person will be extremely technical and capable of building secure, governed agents for internal workflows that connect to business systems (like Box, Salesforce, Workday, etc.), and codify workflows in skills.
In some cases this person may understand the business process well enough to do it fully, but in most cases I expect them to work with the business directly in an embedded fashion. Ironically, that may introduce another new role on the business side that is more akin to agent product management for internal processes. The key is that you need technical + process people that can span multiple teams or functions in an organization. It’s not about brining automation to a job, but bringing automation to a process.
This is going to be a very big trend in most companies going forward. Fun to watch the early innings of what this will look like.
Todd retweeted
Anthropic is paying up to $400,000 a year for an events role.
They're looking for someone to own the execution of brand experiences that translate Anthropic's values into physical moments.
This person will produce everything from intimate thought-leadership gatherings to large-scale industry activations.
The top AI research lab in the world recognizes that to cross the chasm and reach everyday consumers, they need to lean into hospitality. They need to create visceral, unforgettable IRL experiences that make complex technology feel accessible and human.
They understand that digital channels are getting increasingly saturated. Every feed is flooded with AI content... every inbox is overflowing.
The massive opportunity now is offline, analog, in-person.
The companies that win in the next decade won't just have the best product but the most emotional in-person presence and the most compelling storytelling.
If you're in events, experiential marketing, or brand activations, this is your moment. The biggest tech companies in the world are betting on you.
Todd retweeted
The jump from working with a chatbot to having an agent that actually helps automate a process requires a real amount of work.
Most companies will need to have dedicated people that are responsible for bringing automation to their teams, instead of leaving this up to every individual employee. Partly because the work is more technical than we imagine today, and partly because it’s just hard to do this as a side project.
The job spec is to map out new workflows with agents, implement new systems to deploy agents, make sure the agent has all the right (up to date) context to work with, wiring up internal systems to connect to the agents, creating evals for the agents, figuring out where the human is in the loop, managing the system when there are new upgrades, helping with the change management of the existing business process, and so on.
These jobs may come from IT or engineering, or live directly in the business function itself. They’ll be called different things depending on the company, and in some sense it’s the future of software engineering that you’ll see a huge growth of in non-tech companies.
Most companies will have to be hiring for this now or in the future, and it’s another example of the kind of new jobs that will be created in AI.
What Role Does Not Exist Today But Will Be So Common in Five Years Time:
"500K-1M jobs will be created for agent operators.
This person will be somewhat technical. They will be deep in the AI world.
They're gonna have to understand MCPs and CLIs and they are going to have to know how to write skills.
It's going be this group of people that will know how to go into your marketing team or your legal team, or your operations team, or your life sciences research team and this is the person that is basically going to enable that function to get leverage from agents." @levie
Where is this right? Where is this wrong? @jasonlk @gregisenberg @amasad @AnjneyMidha
Todd retweeted
ai is exposing everyone right now & making legacy entities vulnerable af. even labs are having a tough time shipping features that feel really good end to end.
most ppl fail to realize that the hard part of building ai stuff was never access to a good model, it's the orchestration layer, the data plumbing, the taste in knowing when ai should intervene vs shut up, how it should intervene, what it should say, how it should say it, how does human in the loop work.. among many other subtle things. copilot is a prime time example of this.
most legacy players have none of these instincts cuz they built their orgs around shipping deterministic software. we are firmly in the non deterministic era now & that requires a very different skillset to build great tools.
Todd retweeted
There is a huge opportunity for resourceful and entrepreneurial talent within organizations to go in and reimagine workflows for a world of agents.
The way you automate work with agents requires real work. It means setting up unstructured data in a way agents can easily access, learning the workflow and processes and creating skills or plans for agents to leverage, connecting disparate systems together, and likely changing the process itself to support getting the agents the need to do much of the work. Then you have to design where humans will play a role to oversee the workflows, how you validate the work, and so on.
Most of the gains you see from coding don’t take this level of effort because the agent knows more, it gets context more easily, and the users are technically. But for the rest of knowledge work there’s no way around this; there’s really no way to shortcut any of this work. It has to be done by a person or people on the team.
You will see a huge growth of roles within enterprises, and people that specialize in this will be hugely valuable in the economy. Great way for early career folks to make a huge dent quickly as well.
Contrarian take: there's never been a better time for early career professionals to command the attention of leaders in their company. How?
Become the AI Guru in your company.
Every co has a few of them & they instantly earn visibility with the c-suite...
If you're an SDR, map out the inbound SDR process end-to-end in your org and build an AI agent on Vercel that automates several/all steps.
If you're an engineer, evangelize Claude Code & lead enablement workshops, helping other technical folks understand everything from hooks to worktrees to claude[.]md.
If you're a growth marketer, build a paid media workflow that mines creative ideas from reddit, spins up 100s of ads using Nano Banana, sets up the campaign on Meta, and drives traffic to custom landing pages, so you can test ad spend far more efficiently at scale.
You could be the most junior person in your company, but if you're truly viewed as the AI Guru, you are wildly valuable & have way more leverage than you think.
Todd retweeted
The big gap in most enterprises being able to automate work is being able to get right context to the agents.
We experience a huge benefit in coding in tech because the problem is generally far simpler than other areas of knowledge work. The codebase contains a bunch of necessary context, access controls and permissions are generally not a major concern, the users are technical enough to supply the context, and the final output is generally quickly verifiable.
Most knowledge work doesn’t look like this. The data is sitting in legacy silos that don’t easily connect to agents, the access controls are all out of whack (people have either too much or too little access), the information isn’t agent-ready, and more.
This is the big context gap for any type of agentic workflows in most organizations right. The platforms that make solving this easy, and the companies that retool their workflows to enable this, will be the winners in a world of agents.
Todd retweeted
We dramatically underestimate how much change management it is going to take to automate most knowledge worker tasks.
Between data being in legacy environments or systems or without good APIs, context missing for doing the task, teams that are less technical, and other factors, there’s still a lot of work to drive real AI transformation in an enterprise.
This is actually great news if you’re building right now because the opportunity is to build the software bridges to make this easier, or to build new services firms to help with this change management. Opportunity is all around for those looking.
Silicon Valley thinks AI agents are a $20/mo self-serve subscription.
Main Street is paying local agencies $10,000 just to turn them on.
Everyone assumes AI will be bought primarily online like Slack or Zoom. I think they are wrong.
Some of the biggest winners in the AI boom won't be the software vendors. It will be the humans installing it.
Here is the reality of SMBs right now:
• 54% lack internal AI expertise.
• 41% have data quality too poor for AI to even work.
• 41% already prefer buying AI through a local IT provider.
You cannot "1-click install" a genius AI into a messy CRM or a 15-year-old server. It will just execute the wrong tasks at the speed of light.
The AI software will be cheap and a lot will absolutely be bought online. Making it actually work for a messy, real-world business will be expensive.
Very bullish on the "Do It For Me" economy being back.
Todd retweeted
seems obvious but:
things that are changing rapidly:
1. context windows
2. intelligence / ability to reason within context
3. performance on any given benchmark
4. cost per token
things that are not changing much:
1. humans
2. human behavior, preferences, affinities
3. tools, integrations, infrastructure
4. single core cpu performance
therefore,
ngmi:
1. "i found this method to cut 15% context"
2. "our method improves retrieval performance 10% by using hybrid search"
3. "our finetuned model is cheaper than opus at this benchmark"
4. "our harness does this better because we invented this multi agent system"
5. "we're building a memory system"
6. "context graphs"
7. "we trained an in house specialized rl model to improve task performance in X benchmark at Y% cost reduction"
wagmi:
1. product/ui
3. customer acquisition
4. integrations
5. fast linting, ci, skills, feedback for agents
6. background agent infra to parallelize more work
7. speed up your agent verification loops
8. training your users, connecting to their systems and working with their data, meeting them where they are
Todd retweeted
Since 2023, the top quartile of AI spenders on @tryramp have more than doubled their revenue. Bottom quartile? Flat
A roofing company in Texas. A window installer in Utah. A construction firm in Florida that grew 65%
The gap is accelerating and most companies don't feel it yet
Todd retweeted
Engineering job openings are at the highest levels we’ve seen in over 3 years
There are over 67,000 (!!!) eng openings at tech companies globally right now, with 26,000 just in the U.S. We don’t know if there would have been more open roles if not for AI or if AI is actually leading to more open roles, but since the start of this year, the increase in open eng roles is accelerating even more.
STATE OF THE PRODUCT JOB MARKET IN EARLY 2026
In spite of the headlines about layoffs and AI taking jobs, we’re actually seeing a lot of promising signs in tech hiring, and some interesting new trends:
1. PM openings are at the highest levels we’ve seen in over three years
2. AI hasn’t slowed the demand for software engineers (at least not yet)
3. AI roles in general are absolutely exploding
4. Design roles have plateaued
5. The Bay Area is increasing in importance
6. Remote work opportunities continue to decline
7. Despite ongoing layoffs, the overall number of tech jobs continues to grow
More in 🧵
Todd retweeted
Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI
Todd retweeted
Silicon Valley thinks AI agents are a $20/mo self-serve subscription.
Main Street is paying local agencies $10,000 just to turn them on.
Everyone assumes AI will be bought primarily online like Slack or Zoom. I think they are wrong.
Some of the biggest winners in the AI boom won't be the software vendors. It will be the humans installing it.
Here is the reality of SMBs right now:
• 54% lack internal AI expertise.
• 41% have data quality too poor for AI to even work.
• 41% already prefer buying AI through a local IT provider.
You cannot "1-click install" a genius AI into a messy CRM or a 15-year-old server. It will just execute the wrong tasks at the speed of light.
The AI software will be cheap and a lot will absolutely be bought online. Making it actually work for a messy, real-world business will be expensive.
Very bullish on the "Do It For Me" economy being back.
