It was fun working with you and covering multiple angles regarding the "SaaSacre" narrative! You're seriously missing out if you haven't subbed to TSCS already!
I’d bet on almost any SaaS company that already serves a large enterprise customer base and functions as a system of record or complex workflow integrator.
It’s far more likely that incumbents successfully embed AI into existing products than that customers migrate wholesale to an “AI-native” replacement. End-to-end probabilistic workflows are rare, and the most durable technology moats are still built around being a system of record or owning deeply embedded workflows.
I say this as someone whose job is to build enterprise AI products inside a Fortune 500. Agent-based systems consistently break down at scale because of accountability, compliance, and trust.
An AI agent works when I can review and correct it. That model doesn’t scale to an entire department where actions need to be controlled, auditable, and predictable.
At small scale, error rates are manageable. At enterprise scale, error volume becomes the problem - especially when the system can’t reliably flag what’s wrong because it has no epistemic awareness. An agent that makes 10,000 decisions a day and gets 5% wrong isn’t “mostly correct”. It’s operationally unusable.
To make agents enterprise-safe, you have to add guardrails - constrained actions, approvals, records, and workflows. But the more guardrails you add, the more the system collapses back into deterministic SaaS, which is exactly what incumbent platforms already do best.
That’s the core limitation of enterprise-grade AI today in my opinion, and LLMs are not going to solve it.
Correct. The guardrail paradox is the part the market refuses to engage with, every layer of control you add to make agents enterprise-safe pulls you back toward deterministic SaaS. That's the structural reason incumbents win. Thanks for the operational perspective, it's rare to hear from someone actually building this stuff inside a Fortune 500 rather than tweeting about it.
1) There’s a third bull case for AI model companies: the bad token economics lead VCs to cut funding for sub scale operators leading to consolidation that at the least stabilizes the cost per token.
2) Snowflake is at risk of open source disruption from Databricks.
I'm not sure if anyone appreciates the data moat held by Atlassian. It's reductive to say its just Jira. Besides Jira is a map of work and workflows. It's all of the enterprises knowledge in Confluence, code in Bitbucket / Github. They have diversified their revenue well beyond devtools into service management and broader business rules across every internal function and support for organisational alignment (goals to work mgmt).
Very well done! Comprehensive overview during the chaos, and many of your predictions will be spot on. It's just a matter of which of these companies (e.g. Adobe) will adapt.
Honestly, I was underwhelmed. The article is heavy on scope and buzzwords but light on substance. The Adobe thesis completely fails because it misinterprets the basic business model: Document Cloud and Digital Experience will only account for 39-41% of revenue (not two-thirds) and likely under 25% of operating profit, as Digital Media carries much higher margins.
Fair point on the Adobe revenue split, we were imprecise. Document Cloud and Digital Experience are ~40% of revenue, not two-thirds. That said, Creative Cloud isn't monolithically exposed to image gen. Premiere, After Effects, Illustrator, InDesign have zero overlap with text-to-image. The actually exposed surface is Photoshop/Lightroom at the prosumer tier. Real non-disrupted share is probably 55-60%, so the framing was loose but the structural point stands.
Happy to hear specifics on what else felt light. We put out six named positions with entry points and risk factors, so if there are holes beyond the Adobe split, that's useful feedback.
Why are Insiders at ServiceNow ($NOW) the only ones with conviction of the misprice?
CEO $3M open-market buying is news.
Leadership choosing not to sell, while the company leans into its largest buyback authorization in recent history, is evidence.
This insider-signal cluster is one of the most credible we’ve seen in recent enterprise software history, and it ranks in the top tier of credibility in the classification taxonomy.
The (Chief People and AI Enablement Officer) choosing not to sell, from the vantage point closest to the operational truth of human-AI workflow dynamics, is the signal most people missed.
The McDermott open-market buy at these levels is meaningful, CEOs of $150bn+ companies rarely put personal capital to work unless they genuinely believe the stock is cheap. And you're right that the Chief People and AI Enablement Officer not selling is an underappreciated signal given she's closest to whether AI actually displaces or augments the workflow business.
That said, insider buying is a necessary signal but not sufficient on its own. Our hesitation on NOW isn't conviction in the business, it's the growth deceleration (24% to 21% to guided high-teens) and whether the Armis integration goes smoothly. The insider cluster makes us more inclined to move this from 'watch closely' to 'accumulate on weakness,' but we want to see the Q4 print first before making a position. If the consumption-based revenue is actually offsetting seat compression, combined with what you've flagged on insider behaviour, it should probably be revisited.
Something I read recently that I hadn't appreciated -- most of these SaaS founders/CEOs sell stock on 10b5-1 plans, and there is generally a cooling-off period between terminating 10b5-1 sales and initiating an insider buy. My understanding is that McDermott terminated his 10b5-1 selling in August which is why he waited until February to initiate his buy (6 month cooling off period). insiders like the Atlassian CEO who just terminated 10b5-1 sales will have to wait a quarter or two to begin buying.
Thank you! We put out free pieces like this occasionally so people can see the quality of what we do on the paid side. If you liked this one, the paid research goes deeper on individual names and real-time positioning.
Very well written and insightful. However, have a look at this viewpoint which offers a counter to your system of record argument being a SAAS bear-case savior. Not saying I agree with either, it's all too dynamic and I am just trying to look at this from all angles before taking a longer-term view on investments in SAAS hereon. https://x.com/zain_hoda/status/2019049069134417975?s=20
The argument is elegant but it conflates data storage with data authority. Yes, an agent can clone your Salesforce instance in seconds. But a copy isn't a system of record, it's a snapshot. The moment two agents, or an agent and a human, write conflicting updates, you need an arbiter. That arbiter is the SoR. Multi-tenant concurrency, permissioning, audit trails, regulatory accountability, none of that disappears because an agent cached your database.
This article becomes materialistic on the simpler end of the spectrum, the Dead Zone names in our framework. If your entire value prop is 'we store your structured data and give you a UI to query it,' yes, that's getting hollowed out. That's exactly why we flagged project management tools and basic CRMs as vulnerable.
But the Fortress Zone thesis was never about data storage. It was about data authority in regulated environments. An agent caching Veeva's FDA validation trails doesn't make the agent the system of record. The FDA doesn't care where the data is cached. They care where it's governed.
Still, it's a dynamic worth watching. The line between 'governance layer' and 'platform' could blur faster than I expect. I'd rather be honest about that uncertainty than pretend the thesis is perfect.
It was fun working with you and covering multiple angles regarding the "SaaSacre" narrative! You're seriously missing out if you haven't subbed to TSCS already!
Likewise!
I’d bet on almost any SaaS company that already serves a large enterprise customer base and functions as a system of record or complex workflow integrator.
It’s far more likely that incumbents successfully embed AI into existing products than that customers migrate wholesale to an “AI-native” replacement. End-to-end probabilistic workflows are rare, and the most durable technology moats are still built around being a system of record or owning deeply embedded workflows.
I say this as someone whose job is to build enterprise AI products inside a Fortune 500. Agent-based systems consistently break down at scale because of accountability, compliance, and trust.
An AI agent works when I can review and correct it. That model doesn’t scale to an entire department where actions need to be controlled, auditable, and predictable.
At small scale, error rates are manageable. At enterprise scale, error volume becomes the problem - especially when the system can’t reliably flag what’s wrong because it has no epistemic awareness. An agent that makes 10,000 decisions a day and gets 5% wrong isn’t “mostly correct”. It’s operationally unusable.
To make agents enterprise-safe, you have to add guardrails - constrained actions, approvals, records, and workflows. But the more guardrails you add, the more the system collapses back into deterministic SaaS, which is exactly what incumbent platforms already do best.
That’s the core limitation of enterprise-grade AI today in my opinion, and LLMs are not going to solve it.
Correct. The guardrail paradox is the part the market refuses to engage with, every layer of control you add to make agents enterprise-safe pulls you back toward deterministic SaaS. That's the structural reason incumbents win. Thanks for the operational perspective, it's rare to hear from someone actually building this stuff inside a Fortune 500 rather than tweeting about it.
Good read. A couple thoughts:
1) There’s a third bull case for AI model companies: the bad token economics lead VCs to cut funding for sub scale operators leading to consolidation that at the least stabilizes the cost per token.
2) Snowflake is at risk of open source disruption from Databricks.
I'm not sure if anyone appreciates the data moat held by Atlassian. It's reductive to say its just Jira. Besides Jira is a map of work and workflows. It's all of the enterprises knowledge in Confluence, code in Bitbucket / Github. They have diversified their revenue well beyond devtools into service management and broader business rules across every internal function and support for organisational alignment (goals to work mgmt).
You beat JP Morgan to the punch: https://x.com/dividendology/status/2021314972588863871
Long (and in deep red) GWRE!
Always nice when JPM validates the thesis after we've already published it.
Veeva, Guidewire, CrowdStrike, ServiceNow, Snowflake, all on their list, all in our Fortress Zone framework from last week.
GWRE in deep red is where the asymmetry lives, the market is handing you a regulated vertical monopoly at distressed multiples. Patience pays.
Outstanding research, Your picks look solid, perfect for reinvesting profit from my IVG puts. Will initiate positions on further weakness
Patience on entries will pay off.
Fantastic read. Thank you.
Very well done! Comprehensive overview during the chaos, and many of your predictions will be spot on. It's just a matter of which of these companies (e.g. Adobe) will adapt.
I’m not buying but I’m not shorting either
Great share and first substack of yours I've ready. Thanka
Honestly, I was underwhelmed. The article is heavy on scope and buzzwords but light on substance. The Adobe thesis completely fails because it misinterprets the basic business model: Document Cloud and Digital Experience will only account for 39-41% of revenue (not two-thirds) and likely under 25% of operating profit, as Digital Media carries much higher margins.
Fair point on the Adobe revenue split, we were imprecise. Document Cloud and Digital Experience are ~40% of revenue, not two-thirds. That said, Creative Cloud isn't monolithically exposed to image gen. Premiere, After Effects, Illustrator, InDesign have zero overlap with text-to-image. The actually exposed surface is Photoshop/Lightroom at the prosumer tier. Real non-disrupted share is probably 55-60%, so the framing was loose but the structural point stands.
Happy to hear specifics on what else felt light. We put out six named positions with entry points and risk factors, so if there are holes beyond the Adobe split, that's useful feedback.
i was underwhelmed by this comment
Agreed!
Here is my take on it: https://theboringfinanceguy.substack.com/p/the-saas-is-dead-myth
Fantastic write-up!
Why are Insiders at ServiceNow ($NOW) the only ones with conviction of the misprice?
CEO $3M open-market buying is news.
Leadership choosing not to sell, while the company leans into its largest buyback authorization in recent history, is evidence.
This insider-signal cluster is one of the most credible we’ve seen in recent enterprise software history, and it ranks in the top tier of credibility in the classification taxonomy.
The (Chief People and AI Enablement Officer) choosing not to sell, from the vantage point closest to the operational truth of human-AI workflow dynamics, is the signal most people missed.
https://open.substack.com/pub/junkasspos/p/the-signal-retail-investors-missed?utm_source=share&utm_medium=android&r=2upe9x
The McDermott open-market buy at these levels is meaningful, CEOs of $150bn+ companies rarely put personal capital to work unless they genuinely believe the stock is cheap. And you're right that the Chief People and AI Enablement Officer not selling is an underappreciated signal given she's closest to whether AI actually displaces or augments the workflow business.
That said, insider buying is a necessary signal but not sufficient on its own. Our hesitation on NOW isn't conviction in the business, it's the growth deceleration (24% to 21% to guided high-teens) and whether the Armis integration goes smoothly. The insider cluster makes us more inclined to move this from 'watch closely' to 'accumulate on weakness,' but we want to see the Q4 print first before making a position. If the consumption-based revenue is actually offsetting seat compression, combined with what you've flagged on insider behaviour, it should probably be revisited.
Something I read recently that I hadn't appreciated -- most of these SaaS founders/CEOs sell stock on 10b5-1 plans, and there is generally a cooling-off period between terminating 10b5-1 sales and initiating an insider buy. My understanding is that McDermott terminated his 10b5-1 selling in August which is why he waited until February to initiate his buy (6 month cooling off period). insiders like the Atlassian CEO who just terminated 10b5-1 sales will have to wait a quarter or two to begin buying.
Dude you gotta paywall this article now its that good. flick the switch
Thank you! We put out free pieces like this occasionally so people can see the quality of what we do on the paid side. If you liked this one, the paid research goes deeper on individual names and real-time positioning.
Amazing!
Very well written and insightful. However, have a look at this viewpoint which offers a counter to your system of record argument being a SAAS bear-case savior. Not saying I agree with either, it's all too dynamic and I am just trying to look at this from all angles before taking a longer-term view on investments in SAAS hereon. https://x.com/zain_hoda/status/2019049069134417975?s=20
Thanks for the perspective.
The argument is elegant but it conflates data storage with data authority. Yes, an agent can clone your Salesforce instance in seconds. But a copy isn't a system of record, it's a snapshot. The moment two agents, or an agent and a human, write conflicting updates, you need an arbiter. That arbiter is the SoR. Multi-tenant concurrency, permissioning, audit trails, regulatory accountability, none of that disappears because an agent cached your database.
This article becomes materialistic on the simpler end of the spectrum, the Dead Zone names in our framework. If your entire value prop is 'we store your structured data and give you a UI to query it,' yes, that's getting hollowed out. That's exactly why we flagged project management tools and basic CRMs as vulnerable.
But the Fortress Zone thesis was never about data storage. It was about data authority in regulated environments. An agent caching Veeva's FDA validation trails doesn't make the agent the system of record. The FDA doesn't care where the data is cached. They care where it's governed.
Still, it's a dynamic worth watching. The line between 'governance layer' and 'platform' could blur faster than I expect. I'd rather be honest about that uncertainty than pretend the thesis is perfect.