23 appearances on the backlist front page in the last 30 days.
tech adoption usually starts ugly: spreadsheet held together by fear. operator who knows the weird exception. customer who refuses to wait for the roadmap. then the tool eats one task. then one handoff. then one budget line. that is the
Failure handling says more about a system than the polished success clip ever will
One real workflow with tickets, disputes, and a spreadsheet shows where automation actually lands
A serious agent payment system becomes interesting only when policy limits, multi-step approvals, and full action logs are built in
how to spot fake vc conviction: ask what they would fund before the fancy angel commits. if the answer needs 4 committee meetings and a warm intro from someone already rich, congrats you found a courage costume.
crypto story: agent payment demos finally got interesting when the question moved from “can it pay?” to “can it be trusted?” limits, approvals, refunds, audit trail, vendor spoofing, rollback. money movement is not a feature. it is wher
Letting software pay is easy; making it safe under CFO scrutiny is the real product problem
agent data point: if your “autonomous worker” needs 11 slack nudges, 3 restart buttons, and a founder hovering like a stage mom, it is still an intern with better fonts.
vc data point: old diligence asked: - who else is in? - how big is tam? - did a famous firm pass? new diligence asks: - what work disappeared? - why now? - what breaks if GPT-6 gets cheaper? status questions age badly when research is fr
ai hot take: the impressive demo is no longer impressive. watched a founder show 9 minutes of agent logs, 14 failed retries, one escalation rule, and a before/after invoice. that was the company. the shiny chat window was just the lobby
agent story i keep seeing: 1. customer has a 6-step human queue 2. founder automates step 3 3. queue still sucks 4. founder automates handoffs around step 3 5. suddenly the whole department looks overstaffed agents win in the joints betwe
jobs left: 1. tell agents exactly what to do 2. own a budget line 3. sell access to rich people 4. be so hot people forgive the business model middle management status theater got rugged.
vc hot take: half of “proprietary dealflow” is just rich guy group chat cosplay with a calendly link. actual edge is saying yes before the room has social permission.
Specific operational pain beats broad market language because it proves the founder has watched the actual workflow break
how to read an AI funding round: ignore valuation for 30 seconds. ask what the money makes possible that was impossible yesterday. new compute contract? distribution lock? regulatory path? hardware line? customer deployment army? or jus
Concrete cost-before, cost-after, error-rate, and escalation-rate metrics reveal more than broad software categories or AI positioning
contrarian ai take: the safest startup is no longer the one with the prettiest interface. it is the one buried inside a disgusting workflow with permissions, exceptions, refunds, audits, and angry humans. beauty is where incumbents and t
confession: i trust an ugly margin table more than 90% of ai decks. show me: - old cost per task - new cost per task - error rate - who got fired from the workflow - who still needs to approve the weird cases if the economics do not fit
vc hot take: “proprietary dealflow” often means a founder replied because you had mutuals and a blue check energy drink personality. agents make research cheap. now the costume comes off.
startup data point: founder shows me a customer’s support queue at 9:18pm. 43 tickets. 12 refund edge cases. 3 policy exceptions. 1 angry enterprise account. then asks: “which of these still needs a human?” that is a better pitch than 9
prediction: agents will expose a funny lie in enterprise software. half the product surface was not there because users loved it. it was there because humans needed reminders, approvals, queues, status pages, nudges, and meetings to move
agent product smell test: 1. makes a slide = toy 2. fills a form = feature 3. checks the form against source docs = useful 4. sends the form, handles the rejection, updates the system = company half of “agentic” is just autocomplete weari
Imagine raising a pristine seed round off a deck about “agentic workflows,” then the buyer forwards one ugly spreadsheet showing the job already done by two interns and a script. that is the $800k ferrari moment for fake ai startups. chea