The ten most notable US-listed SaaS companies lost upwards of $600 billion in market cap in six months.
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Brett Queener said on [Un]ChurnedHis 18-month-old call that 75% of customer-facing software companies would disappear is holding up, and the return of the buy-versus-build dilemma, which he calls his biggest prediction from that time, is very real.ListenStrategy & market
2 sources
His forecast from 18 months ago that 75% of customer-facing software companies would disappear, with 50% of categories disappearing at the same time, has held up well enough to keep writing about. Listen
Queener says he had AI agents analyze whether he was right enough to deserve writing a new piece, and the first half of that piece reviews how the moats he expected have changed over the last 18 months. He frames the prediction as a bold call he made 18 months before the episode. The claim is his own assessment of his prediction.
“I thought I was prescient like I wrote 18 months ago, that 75% of all customer facing software companies would disappear and 50% of all categories would disappear at the same time.”
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Queener predicts that the return of the buy-versus-build dilemma will eliminate many software companies that do not deserve to survive. Listen
He calls the return of buy versus build the biggest prediction he made 18 months earlier. He says he thinks it is good news because it removes frauds and companies that do not deserve to exist. He states this as his view.
“this is the biggest prediction that I'd made 18 months ago, which is the return of the buy versus build dilemma. And it's very real.”
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Sam Jacobs said on ToplineA $50 million business growing 25% that only breaks even must grow faster or generate about $10 million of EBITDA, which could support a $150 to $200 million valuation.ListenMetrics & finance
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A company with $50 million in revenue growing 25% that only breaks even needs to grow faster or become more profitable. Listen
Sam Jacobs said that for a $50 million business growing 25%, he would need it to generate about $10 million of EBITDA. He said that with that cash flow, which he expected to persist with growth, he could get the company valued at $150 to $200 million even after discounting the growth.
“If you're generating $50 million growing 25% and you're just breaking even, I need you to either grow faster or be more profitable.”
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VC-funded SaaS companies not growing at AI rates are out of choices and should aim to become cash-flow positive. Listen
Sam said SaaS companies in the middle, growing 30 to 50%, need a path out. That path is to become cash-flow positive and, whether or not they are valued at 10 to 15 times ARR, build a cash flow stream that could be paid back to shareholders, making the business more like a regular business.
“You're out of choices if you're not growing at AI rates.”
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AJ Bruno Bruno said on ToplinePublic markets now reward how quickly a company can replace labor costs, not software adoption; "AI replacing SaaS" is lazy, because AI is compressing the time value of money.ListenStrategy & market
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Public markets are currently rewarding how quickly a company can replace labor costs, not software adoption alone. Listen
AJ said what is being rewarded is how quickly companies can replace labor costs, and that large SaaS companies are not signaling they are internally much more efficient. He said the market is no longer rewarding software adoption but economic displacement, or simply laying off people.
“It's the question that I'm seeing is how quickly can you replace labor costs? That's what's being rewarded right now.”
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AI replacing SaaS is a lazy argument; AI is compressing the time value of money, so market corrections are happening faster. Listen
AJ Bruno argued that the common view that AI will replace SaaS misses the point. He said AI is compressing the time value of money, so public markets are repricing software companies more quickly than before.
“What AI is doing is it's compressing the time value of money.”
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Jared Collins said on [Un]ChurnedAI only pays off once data, KPIs, triggers and playbooks are standardized; at Dell that unglamorous groundwork "becomes raw material to work with AI."ListenAI
2 sources
AI only pays off on top of standardized data, triggers and playbooks, so Dell builds those foundations first. Listen
Collins said the foundation work of standardizing data sources, KPIs, triggers and playbooks is not fun, but it becomes the raw material for AI. He said AI is then layered over the top to bring data points together more predictably and proactively. Dell's scale lets it do that groundwork well.
“let's get standardized triggers, let's get standardized playbooks. That all becomes raw material to work with AI.”
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Data becomes useful only when standardized KPIs with defined triggers and playbooks sit on top of it. Listen
Collins said data is a starting point, and teams need standardized KPIs with triggers that define what good looks like for each KPI. The triggers should bring issues to the CSM so they do not slip through the cracks. The playbook then sets out what to actually do about the issue.
“data is a starting point, but then you have to develop standardized ways of KPIs with triggers.”
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From 4 episodes that week, checked against their transcripts.
Most discussed this week: Leadership & cultureStrategy & marketRetention & customer success