Funnily enough, I’ve gone down this torturous path three times already.

• In 2020, before AI was a thing, I started with LinkedIn…

  

  

• I did it with Tinder a few years later. (I keep telling my friends I should do a live presentation on this, but I only ever get pushback. I wonder why?)

  • I won’t be showing that one as getting banned isn’t high on my priority list. But suffice to say, I averaged (receiving) two DMs a day for three months straight once it was fully optimized.

• And, as you probably know, I’ve been constantly tweaking Substack since 2023/24.

About a month ago, I decided it was time to tackle Twitter / X.

So, if you want to follow along, here’s what I’ve learned so far and how it’s going.

I’m going to break down the algorithm and what I’ve been testing. Don’t expect an overnight “zero to hero” transformation; I’m committing to at least a 6-month test to see if X is actually a viable discovery engine.

The theory behind this is vetted, but real testing takes time. If you want to see where things stand right now, scroll to the bottom; you’ll find a one-page summary of all of my metrics.

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So let’s start with some borrowed knowledge and the basics. I won’t bore you with how it was started in 2006 by a group of smart individuals (Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams) or how it was controversially taken over by none other than Elon Musk a few years ago.

I think we should skip the blah blah and jump right into...

How the Recommendation Pipeline Flows

Every time you pull down to refresh, X takes a pile of ~500m daily posts and shrinks it down to a custom batch (around 1,500), and then ranks them and slaps them on your screen (browser/app) in under 200ms.

The system running the show for this “For You” page is the Phoenix engine.

While it streamlined a lot of the old multi-step pipeline, the core journey hasn’t changed much and is now integrated into Grok (X’s AI).

Here’s how it actually works:

• Where does your feed come from? Half of what you see comes from accounts you follow. The other half comes from total strangers across global Twitter.

• How does it pick the strangers? Phoenix takes your past activity, turns it into a digital profile, and uses some very smart math (vector matching) to find random posts that match your taste(s).

• The main engine. Phoenix doesn’t just guess if a post is “good” or “bad” for you. It calculates the exact probability of 19 different actions at once in the process predicting whether you’ll like, reply, repost, bookmark, or ignore it.

• Evaluating posts in a vacuum. To keep things fair, the algorithm rates each post strictly between you and that specific post. That way, a viral, super-mega-spicy tweet won’t completely drag down a mediocre post sitting next to it in the pipeline.

• In-Network distribution. In addition to Phoenix, the system has memory in the form of the Thunder algorithm, which stores 48h of interactions and prioritizes what in-network posts your followers receive.

Exactly How Content is Weighted

Once Phoenix predicts the probability of how you will interact with a post, a Weighted Scorer combines those probabilities into a final number.

Notice the massive gap between passive and active engagement.

A post with 100 passive likes (+50 total score) is actually worth less to the algorithm than a post with just two replies where you engage back in a real conversation (2 x +75 = +150 score).

Naturally, every action has a ripple effect. A repost, for example, triggers the algorithm all over again, pushing the post to that person’s followers. But for now, let’s stick to the core scoring system.

Golden Rules of the Algo

If you are trying to maximize reach, the open-source code reveals four strict boundaries you must navigate.

Velocity

When you post, X doesn’t show your tweet to all your followers. It drops it into a test pool, a slice of your audience plus a tiny handful of strangers. The subset of followers varies, but the figure can reach up to 10%-ish.

Then, the algorithm watches what happens next like a hawk.

The first 15 to 30 minutes are the golden window. The algorithm re-scores your post in real time based on speed and reaction:

• High engagement predictions —> X pushes your post to a broader audience.

• Low engagement predictions —> The post gets deprioritized immediately.

The Amplification Threshold (~10 Engagements)

You have to know that reach doesn’t scale linearly. There is a bit of compounding based on velocity:

• ~3 quick engagements (first 10 mins): Proves that what you’re posting is not spam. This pushes the post to 2–3x more of your existing followers.

• ~10+ engagements: Crosses the threshold to push your post out-of-network into non-followers’ “For You” feeds.

• 50+ engagements: This would be the viral loop trigger, which means broader distribution.

But as you know, not all engagement is created equal.

Quality beats quantity.

Like we pointed out before, two real replies carry way more weight than dozens of passive likes. Even better, your own replies as the creator count heavily. Replying to early comments is essentially free algorithmic fuel.

What Happens if a Post Flops (<3 Engagements)?

If a post gets zero traction early on, it stays trapped in the test pool. It will still sit in the chronological “Following” tab for people who actively scroll past it, but it’s dead in the water for discovery.

It doesn’t matter if you get a ton of engagement 2h after you post; organic distribution will not be triggered.

Most of what I’ve done sits in this no distribution pool, and it’s frustrating as hell.

External Link Suppression

The platform aggressively discourages you from sending users elsewhere.

Why Links Tank Your Reach

X’s ranking engine wants one thing, and that’s to keep people scrolling on the app. All of these social networks heavily rely on ads, and those are a product of eyeballs.

For you, external links cause users to leave, which tanks your post’s dwell time. The algorithm notices people clicking away and immediately stops showing your tweet to new audiences.

Note that recent changes make it so that even if you click links, you don’t leave the app itself… but you’re still penalized

Does Premium help? Paying for Premium gives you a baseline reach boost that cushions the hit. Our take is to stick to X posts. These will outperform link posts every time.

Workaround (A Classic)

So say you really want to funnel people towards your latest and greatest post on Reddit. How would you go about it?

1. Post pure native content first (text, images, or a short thread). Let the main post gain momentum and engagement on its own.

2. Put your external link in the very first reply.

Pro tip: don’t post your comment immediately, wait 3-5 mins as it will count towards improving engagement more.

This lets your main post get full algo distribution while still giving interested readers a direct link right below it.

Btw, despite periodic claims that X has “fixed” how links are treated, our testing and that of people we know proves the link penalty is still very real. And it’s especially true if you aren’t paying for Premium (it turns into a double whammy).

So treat any external link in your main post as a reach killer, and always stick to the reply thread strategy.

Text and Native Video over Threads

Going beyond the obvious, X’s ranking system takes into account dwell time (how long people pause on your post), conversation depth, and keeping users inside the app.

If you can hold someone’s attention, X will reward you.

Naturally, that’s changed how we format posts.

I haven’t really found any great guides on short-form formatting, but this document has a bunch of proven, formula-style templates that usually work:

Twitter Growth Blueprint

12.2MB ∙ PDF file

Download

Download

What I will say though is that…

So, in the old Twitter, multi-post threads were king. Splitting a story into 10 tweets gave you multiple chances to hit the feed and pull readers through a chain.

However, with new changes, single long-form posts (up to 25k characters for X Premium) now routinely outperform equivalent threads for broad reach.

• Unified Engagement. Instead of 50 likes scattered across 10 separate tweets, every like, reply, bookmark, and share hits one single post. That concentrated signal tells the algorithm…

“This post is hot and you should totally show it to more people.”

• Dwell Time Bonus. Opening a long post and reading for 60-120 seconds gives X a massive dwell time signal.

• Zero Drop-Off: Readers don’t have to scroll through 12 nested replies to get the full story, eliminating thread fatigue. But do note that (as to the prvious point) time counts, so expanding long form with replies is always encouraged.

My question is…

Are threads dead?

Well, it seems like the answer is… No?… Not entirely at least.

They still work great for specific posts like step-by-step tutorials or interactive Q&As. But if you want maximum reach for a story or essay, it’s way better to stick to a single long-form post.

Native Video Rewards High Completion Rates A Ton

I’ve yet to get this one right, but I’ve seen this story play out multiple times. X is pushing video hard to compete with TikTok and YT Shorts, and all that good dopamineic 💩.

However, in the case of X, completion rate matters way more than view count.

Here’s my testing so far:

• Watch time drives distribution: A 45-second video where 60% of people watch to the end gets boosted far more than a 3-min video where everyone swipes away after 5 seconds. Note that I still need to test with more lengths between 30 secs and 2 mins before I can conclude what the ideal length is.

• Always upload natively: Never post a YouTube link. Upload the raw video directly to X so it plays inline. These perform way better

Pro tip: If you want to embed someone else’s video on X (instead of just sharing a plain link), long-press the video and tap Post Video.

Alternatively, you can add /video/1 to the end of the post’s URL to get the same result.

Example… combine [x.com] + [/YelaHRMT/status/2078858399132106920] + [/video/1]

• Hook them in FAST (3 secs): As with any other short form stuff, you must use bold visuals, add captions (most people scroll on mute), and cut out zero-value intros if you want to get rewarded.

TweepCred

Note: This is probably the most important section of the entire text. Read it carefully.

X calculates a daily PageRank-style score (from 0 to 100) for every user, called TweepCred. If your score falls below 65, the algorithm caps you.

Specifically, only 3 of your posts will even be considered for out-of-network distribution per cycle, effectively locking you out of the “For You” feed.

Let’s break this down slowly, but before we do… I know what you’re thinking: what the hell is a cycle?

Well, before we dive into TweepCred, here’s a bit of context for you.

TweepCred Update Cycle = 24 Hours

The batch job on X’s servers (historically called the UpdatePageRank job in the Scala code) runs once every 24 hours.

The User Session Cycle = Instantaneous (<200ms)

When the code says “accounts with a TweepCred below 65 have only 3 tweets considered for distribution per cycle,” it refers to a User Feed Refresh Cycle.

Every single time a user pulls down to refresh their “For You” feed, the pipeline spins up.

If your TweepCred is under 65, the algorithm will pull a maximum of 3 of your posts into the candidate selection pool for that specific user’s refresh session.

The rest of your posts are completely locked out of that generation cycle.

The Post Lifespan or Decay Cycle = 6 to 24 Hours

X applies an aggressive time-decay multiplier to posts.

• 6-Hour Half-Life: A post loses roughly half of its score weight every 6 hours. By score, I mean the sum of all effective engagement for distribution.

• 24-Hour Death: After 24 hours, a post’s algorithmic score drops to effectively zero. No matter how viral it went on day one, it will stop appearing in the “For You” feeds of out-of-network users.

Estimate Your TweepCred (Max 100 Points)

You can grade your account across the six vectors X’s code uses to build your reputation score.

Again, the target is 65 or higher to escape the algorithmic throttle; however, note that this is algorithmic, so the higher the rank, the exponentially better the distribution.

Follower-to-Following Ratio (Max 30 Pts)

X wants to see that people find your content valuable enough to follow you back.

If you follow more people than follow you, you get penalized. If your “following” list is over 500 and your following-to-follower ratio is greater than 0.6, the code triggers an exponential penalty.

From our testing:

• High ratio (1.5 or 2.0 to be safe) = 25-30 pts.

• Flat/negative ratio = 0-10 pts.

Example

• Followers: 10,000

• Following: 600

• Is Following > 500? Yes (600)

• Is Ratio > 0.6? No (600 / 10,000 = 0.06, which is safely below 0.6)

Maximum points (25-30 points). X would trust this account because they have high authority but keep a highly curated timeline.

Engagement Quality (Max 25 Pts)

This is your average interaction rate per post (likes, replies, bookmarks, etc.).

If your followers passively ignore your posts, X assumes you are a low-quality or broadcast-only account.

Platform-wide averages are typically lower (around 0.1–1.7%, varying by account size and calculation method), but large creators can still see solid algorithmic distribution at 0.5-1% with consistent quality signals.

We’ve seen good distribution with 0.2% to 0.6% visual engagement (which only accounts for the public engagement metrics).

Our findings include:

• Consistently getting replies and bookmarks relative to your follower size = strong positive signal (20-25 pts).

• High follower count but dead silence on posts = weak signal (5-10 pts).

Example

For every 1,000 impressions, you need 10 total engagements of any kind (clicks, likes, replies, bookmarks, or reposts).

What the post actually looks like:

• 6 Likes [1x]

• 2 Profile clicks (people clicking your name to see who you are) [2x]

• 1 Reply [8-13x]

• 1 Bookmark [8-10x]

• Total Engagements: 10 (1% rate)

• Total Weighted Score: ~29

This is a baseline, acceptable post. It’s not viral, but it’s not spam. The algo will see this and think:

“We will keep distributing this creator’s content normally.”

If we made this 50 engagement on those same 1,000 visualizations, we would see the out-of-network distribution get triggered.

Again, note that engagements are not created equal; we want to encourage behaviors like dwell time and replies to max out the weighted score.

Account Age (Max 15 Pts)

Trust is built over time to prevent spam bots.

Brand-new accounts are heavily penalized. The full age trust benefit kicks in once your account crosses ~30 days old.

We’re not entirely sure whether reactivating a dormant account has the same effect, but from our baseline calculation is should not. This seems like a pretty straightforward if/then command.

Score works pretty simply:

• >30 days old = 15 pts.

• New account = 0–5 pts.

Pay-to-win Premium Subscription (Max 16 Pts)

A direct, artificial boost to bypass the cold-start problem.

It works out the following way:

• Premium+ = +16 pts

• Premium = +10 pts

• Basic = +4 pts

• Free Account = 0 pts

What this essentially means is pay-to-win is active now. Arguably, having it creates normal conditions, but not having it creates a major debuff to distribution.

Activity & Consistency (Max 10 Pts)

X rewards active daily participants.

Posting at least 1 to 3 times a day and actively replying to other accounts in your niche boosts this score. You can easily max this out, but it usually takes about 2 weeks of daily posting.

We would recommend 2/3 posts and 8+ replies per day.

Here’s the math for the TweepCred score:

• Daily activity = 10 pts.

• Posting once a week or ghosting the platform = 2–4 pts.

Pro tip: You should not spam as all of these are also filtered through the engagement metric.

App Usage (Max 4 Pts)

As a last side note, X’s code gives a tiny trust bump if you use the official mobile app.

• If you post and scroll from the iOS/Android app = 4 pts.

• If you only use scheduling tools or third-party APIs = 0 pts.

1 or 2 sessions a day will do the trick. A session is classified as 2-5 mins of active engagement (scrolling feed, liking posts, replying to commments, etc.).

My TweepCred Score Explained

Well, by the time I started tracking this figure, I’d already gone through several rounds of optimization, including pruning followers, deleting low-engagement posts and replies, and grabbing Premium.

My best guess is I was sitting around 20 when I took over the account.

Also, fair warning: I get ridiculously obsessive with this stuff. And yes, it was 3 a.m. when I took this screenshot. Stop judging my sleep schedule, close this tab, and go optimize your own profile.

My latest score as of the posting of this on July 22nd is:

Here is my estimate, though it may not be perfectly accurate.

My Follower-to-Following Ratio (Max 30 Pts)

Probably very high with a 0.3 ratio, plus about 20% of them are verified. However, given the low count, it’s probably penalized, so +20.

My Engagement Quality (Max 25 Pts)

Even though the number is not updated after the pruning, 0.8% is okay, the low absolute numbers do work against me, so +7.

I will note that the average engagement rate shown in the dashboard right now should sit between 2-3%, once updated (in 2 weeks-ish) this component will receive a massive boost (+20 expected)

My Account Age (Max 15 Pts)

Room for improvement but high, so +13.

My Premium Subscription (Max 16 Pts)

We added +10 with this category and reduced max TweepCred from 100 to 94.

My Activity & Consistency (Max 10 Pts)

Room for improvement but high, so +7.

My iOS App Usage (Max 4 Pts)

Room for improvement but high, so +3.

TweepCred Score Takeaways

There’s not much left unsaid here. Perhaps, to highlight two major things: using your phone for a few sessions can be the difference maker to get to 65, and you can tweak most of the score with some smart high-quality engagement.

However, the system is a bit "pay to win," especially given how the score acts as an exponential trampoline for your content.

Note that I do pay about 5 bucks per month at the moment (got an entry offer thingy), which makes me Premium, and Premium is a must if you want distribution.

I’ll likely upgrade to Premium+ eventually just to test it. Early on, the math really works in your favor.

Past 500k followers, it’s probably not worth the money, but for early growth, it makes total sense (on paper).

Distribution In and Out of Network

Beyond TweepCred (which, btw, is intrinsically connected to this metric), your scale is sadly a product of size, and there’s nothing you or I can do about it.

How you can play around this... well, individually you can do basically nothing. If you’re a small creator, you cannot overcome this gate directly.

What you can do, though, is ask larger creators to vouch for you in the form of reposts and engagement. Reposts will retrigger the distribution calculation, as the author’s followers will get notified and shown the post.

We won’t get into monetization, which requires 500 followers and 5M views in the last 90 days, but eventually, I’m sure I’ll get to test that out.

To be candid, it won’t be a priority for me at any point. Direct revenue isn’t my goal here; what matters to me is respect and the ability to execute on my main project: Hermit Ventures.

On this completely parallel topic… In my view, having resources is just like having more soldiers in your army; it helps with the mission.

In any case, as my former McKinsey boss used to say:

“We’ll cross that bridge when we get to it.”

I can literally hear him in my head, and it reminds me of how much I detested when he said that. 🤢

Shadowbanning Explained (w/ an example)

While X prefers polite corporate euphemisms like “visibility filtering” or “reach limitation” (under their motto “Freedom of Speech, Not Reach”), the reality is the same.

Your account looks fine to you, but X has quietly turned down the volume on your voice. Speech suppressioooooon activated!

What It Looks Like

So yes, I’ve tested this, of course I have. And it sucks, for the most part.

When X puts you in the penalty box, your content gets buried. You are actively hidden or deprioritized.

There are zero notifications associated with it.

Impressions and engagement just suddenly drop off a cliff, usually by 50% or more, and it becomes almost impossible for new people to discover you. So yeah, profile visits and new followers fall.

Common Ways X Chokes Your Reach

The Grok-powered algorithm scores you in real-time, looking for “safety signals.” If you trigger the algorithm, it usually results in one of these restrictions:

• Search Ban: Your posts don’t show in search results, and your @handle won’t autocomplete in the search bar. You can actually test for this, but it’s easier said than done. Since X rarely allows you to search without logging in, you’ll need a friend who’s logged in to check it for you.

• Reply Deboost: Your replies get hidden from non-followers. This is a reach killer for authors trying to network in comment threads. Testing this also requires another person’s account, as you can’t see comments without it.

• General Throttling: X simply demotes your content across the board, ensuring it reaches far fewer people. So out-of-network distribution effectively becomes zero.

How Did This Happen?

It’s almost always the algorithm flagging “bot-like” behavior, not a manual penalty. Big triggers include:

• Spammy habits: Blasting identical posts, repetitive replies, or mass follow/unfollow sprees.

• Link spam: Putting too many external links in main posts (stick to the reply-thread strategy!).

• Controversial topics: Content that gets frequently reported by other users. This includes faulty language.

• Low follower quality: If your audience looks like fake bots or you’re part of engagement groups.

How to Fix It / Recover

The good news is that most restrictions are temporary and will lift if you stop the bad habits:

• Go silent: Pause major posting and replying for 24 to 72 hours. Stubborn cases might need a week of inactivity.

• Delete and scrub: Get rid of recent spammy posts or link spam, but do it carefully and methodically (more on this later).

• Build better habits: Once you return, stick to consistent, high-quality content and zero external links in main posts (at least for a while).

• Engage authentically: Focus on conversational replies rather than rapid-fire “great post” comments.

Recovery time varies. It took ours 48h to recover, which probably means mild cases resolve in days, but repeated offenses take longer.

Not every drop in reach is a shadowban. Normal algorithm shifts or audience fatigue can also reduce visibility.

If you are experiencing this, I really don’t know what to tell you other than… quality wins with this algo.

On that note…

Don’t Delete Everything at Once

This is how I got shadowbanned. Pruning is good, but the algo considers you spam if you delete more than 50 posts/replies every 15 mins.

So don’t go ham. Just check every two to three days for stuff that didn’t perform past the 48h mark, and you’re good.

Each cycle (24h) is important, but the overall one- to two-week metric is really what determines your score, so if you keep that high, you’re golden.

If you want to go on a pruning session, I recommend you split it up into daily sessions and delete approx 5-10 posts every day just to keep the spam bot flag away. Maybe making it a habit is a better practice, but again, I’m still testing.

Just don’t delete 100 posts in about 2 hours. That’s what I did, and it’s definitely not a smart move.

Other Useful Stuff We’ve Learned So Far

Replies outpace posts, at first

Because your initial algorithmic distribution is going to be pretty rough, you really only have one option:

You’ll probably notice that all of these examples are from the last few days, and that’s no coincidence.

Like most things, as you iterate, you get better at it. I filtered these by impressions here, but ideally we’d filter by profile visits.

That’s the north star metric for understanding follower conversion.

Optimize your profile

On that note, when people visit your profile, they need a proper place to land… and that comes down entirely to you.

Using your real name is generally recommended since you want to build trust.

Your profile picture and header image should be curated for your specific audience (in my case, finance professionals and people interested in finance).

Same goes for your bio: it should establish authority quickly and, more importantly, clearly state what you write about so people can decide within 5 seconds if following you is worth their time.

Your pinned post matters a lot, too.

While many creators pin a post that went viral, I chose one that breaks down my main project in detail. This deepens engagement and gives readers instant confirmation of who I am and what I do.

Treat your profile as a top-down funnel (literally) where people skim from top to bottom for anything that catches their eye.

Note: Take my link, for example. It used to be hrmt.substack.com, but it’s moving to www.hermitresearch.com (by the time you read this, it probably already has).

This is a prime example of serial optimization. If I hadn’t pointed it out, you likely wouldn’t have noticed, and this single change isn’t a game-changer on its own. But as these tiny upgrades stack up over time, the overall jump in perceived quality becomes very obvious.

Reposting a successful post 24h later

Okay, so look at the first 48h of this post.

When you restack your own post after 24h, you get a little bit of that distribution boost again.

A self-repost mainly pushes the tweet back to the top of the feed for your existing followers and helps revive the decay mechanic that we discussed previously, but it doesn’t trigger fresh out-of-network distribution.

Deleting non-engaged works

Okay, so here’s what actually happens when you delete posts from the last 14 days.

On your frontend dashboard (the one you see on the analytics screen):

• Cumulative stats (Impressions, Likes, Replies, Reposts) will drop. X’s analytics is basically a simple calculator. So, when you delete a post, it subtracts those impressions and engagements from your 14-day total (there’s a delay).

• Engagement rate (like your 0.8%) will usually improve. You’re removing silent impressions. Denominator effect. The ratio goes up.

On the backend algorithm (the part that actually matters for future reach):

The algorithm cares only about density and consistency.

Every ignored post signals “low-interest content,” which debuffs your future reach. By removing them, you clean up your history and reset your profile so the algorithm starts seeing you as a higher-quality creator again.

Don’t stress if your total impressions or likes dip a bit after cleanup; overall, that’s just vanity metrics. You’re trading a little historical volume for better future distribution.

Expected timeline:

• Daily recalculation. X updates your reputation (TweepCred) roughly every 24 hours. The refresh occurs at 00:00 UTC plus a lag of up to 4h.

• Partial real-time effect. Some positive signals can show up quickly.

• Full recovery. Expect 3-7 days of consistent high-quality posting to really see the difference in reach.

What I’d do is delete the dead-weight posts methodically. Remember, slow and steady wins the race here.

Then spend the next few days feeding the algorithm fresh and engaging content. By next week, your baseline reach should start opening up nicely.

Views are just a vanity metric

To continue on the point above, like with most things, views are just a way to boost your ego. Losing views by eliminating low-engagement content is a massive net positive.

Views really only act against you via the denominator effect, so take that as a lesson.

Narcissism and all that good stuff.

Replies don’t count, but they count

So on the one hand, good or bad replies aren’t really reputation-damaging on their own, because most of how X classifies you has to do with quantity, frequency, and good faith.

However, there is one metric that gets affected… and that’s engagement. Remember, poor engagement does hurt your stats and therefore your TweepCred.

This is especially true with negative feedback loops meant to filter spam:

• Downvotes & Hiding: X uses downvotes on replies as a training signal. If your replies are consistently downvoted, hidden, or ignored, your account reputation takes a major hit.

• Mutes & Blocks: If you leave spammy, copy-paste, or overly aggressive replies that cause people to mute or block you, it triggers strong negative penalties that tank your TweepCred.

My best guess is anything with an engagement rate lower than 1% should be removed (though this is still a guess; I haven’t fully tested the exact threshold).

Engagement and time matter here, so aim for at least 1-2 Likes or 1 Reply from someone else within 48 hours. If you don’t get that, just prune it once the time has passed.

Blue checkmarks rule the tweetosphere. Period.

If your followers have high TweepCred, that’ll increase your visibility as well, especially by maintaining a strong verified follower ratio.

5% to 15% — Good general interest
Active creators in broader niches (e.g., general tech, fitness, lifestyle).

15% to 25% — High-Trust Expert (I’m at around 20%)
Highly specialized, professional niches (finance, micro-caps, investing, SaaS).

Note: All serious B2B accounts have check marks, so catering to these Premium and Premium+ massively boosts your authority within the network at the expense of probably having a lower follower count.

Reply Triggers, Bookmark-Bait formatting, and Dwell-Time optimizations

Direct replies are nice, but multiple back-and-forth exchanges are great as they compound time spent. If someone starts a session and comes back later, the clock doesn’t reset, so you can hit that valuable 2-minute mark much easier.

Example

Someone reads your post and replies (35 seconds). You reply, they come back and spend another 60 seconds. The algorithm sees 95 seconds of dwell time + bonus signals from the two replies (including your reply to their reply).

Smart self-replies are also powerful, but use them smartly to add context, follow-up thoughts, questions, polls, or links.

Do not just post a comment that will be ignored, as again replies don’t count… but they do.

Again, these increase both engagement and dwell time. Just be smart about it. Quality always matters.

Ad processing and placement is not very good

If you want to boost a post, use one that already performed well during the first 24h, but never sooner than that.

I haven’t tested this enough though to have a opinion on what do do to maximize it. However, I’m quickly learning about what not to do.

Overall, it doesn’t work out too well, especially for niche creators, because targeting just blasts your stuff to a broad audience. This often depresses your engagement metrics.

Also, ads are freaking expensive. I’ve spent about €200 testing. So far, they have not worked nearly as well as just asking a bunch of my buddies to engage with the posts… especially the high-TweepCred ones.

Closing Thoughts and Insights

Please note that after reading all of this, I hope you put it to practice and actually go down the trial-and-error rabbit hole.

Note that I am in no way an expert on this topic. I’ve still got a lot to learn. But after reading through the Phoenix engine procedures and now Grok’s smart positioning, I’d say I’m probably in the top 90%.

The question is… what has to happen to get to the top 99%?

Well, that’s simple. It’s just more iteration.

Expect a post in a few months where I show you results based on TweepCred improvement and overall distribution gains.

I’ll likely opt for the pay-to-win strategy and succumb to our lord and savior Elon Musk for that beautiful instant +6 point boost. We’ll see.

For now, my metrics look like this:

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One-Pager — Visual Summary

The cover page with key metrics, valuation, and scenarios all in one image. Built for anyone who wants a quick look at the thesis fundamentals.

Executive Thesis

• The Bearish Consensus and Why It’s Wrong

• The Bullish Thesis (& Competitive Advantages)

• The Numbers Challenging The Narrative

• Three Scenarios for 2030 and Target Price

• Our Action Plan

Full Thesis (Downloadable PDF)

One-Pager

When the market punishes high-quality companies based on a narrative that the data contradicts, you get as an output a set of excellent opportunities for the coming decade.

Key Atlassian data points:

• The product continues to win enterprise deals at record levels

• The Teamwork Graph is a demonstrable AI differentiator with quantified ROI

• The capital allocation has already changed; buybacks exceed SBC in FY26

• The founder continues at the helm with 20% economic and 87% of the vote

• The culture has led to the adoption of hard but critical decisions (10% layoffs) to accelerate GAAP profitability

• The new CFO has elevated “durable profitable growth” to an explicit strategic priority

The 10 pillars that support the thesis:

What worries the market and what do we think about it:

Executive Thesis

• The Bearish Consensus and Why It’s Wrong

• The Bullish Thesis (& Competitive Advantages)

• The Numbers Challenging The Narrative

• Three Scenarios for 2030 and Target Price

• Our Action Plan

The Bearish Consensus and Why It’s Wrong

The bearish thesis the market holds today regarding Atlassian can be summarized in a single sentence:

This narrative has driven the stock down more than 60% from its highs of a year ago, as part of what has been dubbed the SaaS-pocalypse.

Mike Cannon-Brookes, CEO of Atlassian, listed this bearish thesis word for word at the Analyst Day on May 6:

His literal response:

The problem with the bearish thesis is that it stems from an empirically false premise.

Note: Less than 50% of Atlassian’s product users are developers.

And the data they provided at the Analyst Day is highly revealing:

• Confluence: 7 out of 10 users are non-developer knowledge workers (70%)

• Jira Service Management: more than 75% are non-developers

• Jira (the alleged “dev tool”): approximately 2 out of 3 users are non-developers (HR, finance, legal, marketing, operations, design)

Quality Value (QV): From personal experience using these tools in private companies developing software for millions of users, Atlassian’s products are the best in the market for collaboration. And that’s not just for developers, but for any role within an organization.

This market confusion is precisely what creates the opportunity we are going to look at in detail today.

The Bullish Thesis (& Competitive Advantages)

Core thesis: the software explosion demands more organization

We already shared this core thesis in our analysis of Nagarro and the IT sector, where practically identical dynamics and the same AI narrative apply as with Atlassian.

Our view is based on the prediction of British economist William Stanley Jevons.

The Industrial Revolution had dramatically improved the efficiency of steam engines: the amount of coal needed to produce a unit of work fell steadily for decades.

The “obvious” prediction was that total coal consumption in the UK would drop. The exact opposite happened: coal consumption skyrocketed. When a productive input becomes radically more efficient, its effective price drops, unlocking use cases that were previously unfeasible. Aggregate demand explodes.

The software footprint that the world needs to maintain, integrate, secure, and redesign is expanding faster than ever.

AI is dramatically lowering the barriers to creating software while simultaneously improving product quality.

Mr. Cannon-Brookes expressed this in provocative terms at the Investor Day: he argues that there will be more developers in the AI era, not fewer.

The current triad of Product Manager + Developer + Designer (and this is our opinion), will evolve into a single multidisciplinary profile, supported by AI, with a vision for product, business, and design. The engineer of the future orchestrates agents, validates outputs, and makes product decisions.

To support this view, look no further than the Bloomberg Software Labor Demand Index (INDDSFTW): it hit record highs in February 2026 (71.4 vs. 62.9 a year ago). This is sector-wide data, not company-specific. It is the best quantitative refutation available against the bearish software thesis.

Atlassian Will Reign in the Chaos

If software creation accelerates, the associated organizational chaos multiplies.

Every new feature generates tickets, decisions, dependencies, specifications, conversations, reviews, tests, deploys, incidents, and postmortems.

That work needs to be channeled and organized, and that is exactly the problem Atlassian has existed to solve since 2002.

The company is not a victim of AI disruption; it is critical infrastructure for the AI era.

Cisco’s testimony at the Analyst Day articulated this perfectly.

Cisco manages 25k-30k networking engineers on Atlassian’s platform. They saw a 5% productivity uplift just by adopting the System of Work, and a 40% reduction in the Total Cost of Ownership (TCO) of managing the platform itself.

The platform is not becoming less relevant; it is becoming mission-critical. And this, as we will explain later, is one of Atlassian’s key competitive advantages.

The Part Where We Talk About Quality

Pricing model: from pure per-seat to a mix of seats + consumption

The nuanced objection of the bear case is legitimate: if tickets previously handled by a developer are now done by an AI agent, the customer theoretically needs fewer human seats.

The most compelling data point from the Analyst Day:

Mr. Cannon-Brookes showed a chart of cloud seats excluding all migrations, aka pure organic demand. The curve continues to accelerate quarter over quarter.

It is the most direct visual proof against the narrative that “seats are going to evaporate.”

Our hypothesis is that the AI agent will end up being just another user, a virtual seat with its own recurring cost.

Atlassian is already building this hybrid future with four pricing models operating in parallel:

• Traditional per-seat remains the primary model because it is what most customers prefer, at least for now. This is especially true when they migrate to the cloud.

  

• Growing consumption-based Rovo (their chatbot) credits, CSM resolutions, Bitbucket Pipelines, Assets, Forge Compute, Forge SQL.

• Hybrid pricing w/ Teamwork Collection bundles credits with seats.

• Flex-based commitment model (new, presented at the Analyst Day) for customers with >$1M in commitment, allowing them to move between seats/applications/collections with usage certainty.

Ultimately, at the Analyst Day, Cannon-Brookes confirmed that this pricing flexibility is deliberate, not improvised. It allows them to adapt to whichever direction the AI market takes without having to reinvent the model.

Revealing metric: customers using MCP grow at 2x

A metric from the Analyst Day that deserves attention: customers using MCP server grow their ARR at 2x the rate of those who do not use it. This suggests that the “agentic + consumption” direction works economically. It is much more than just a theory, as it already has demonstrable data backing it up.

Note on MCP (Model Context Protocol): an open standard created by Anthropic in 2024 that acts as a "USB-C for AI agents", allowing any AI tool (Claude, Cursor, ChatGPT, etc.) to access Atlassian's Teamwork Graph in a standardized way. Every external agent that queries the Teamwork Graph via MCP reinforces the centrality of the graph in the customer's workflow.

The strategic paradox: by opening the Teamwork Graph via MCP, Atlassian increases its moat instead of reducing it. Every external agent that reads from the graph also writes back, creating classic network effects. The company positions itself as neutral infrastructure for the agentic era, not a walled-off garden.

The Data Validating Our Vision

The data published by the company points in a single direction: more AI usage on the platform leads to more consumption and higher revenue.

Here are some of the key data points validating this thesis:

• Customers adopting Rovo grow their ARR at 2x the rate of non-Rovo customers.

• AI credit usage is growing +20% month-over-month. That’s an exponential curve, still in a very early stage of adoption.

• 75% of the Fortune 500 already has Rovo activated (Analyst Day data).

• Teamwork Collection customers use 2x more AI credits and have 2x more agents.

• 1,000+ customers and 1M+ seats in the first 6 months of the Teamwork Collection.

• Service Collection crossed $1,000M ARR, growing +30% YoY, with enterprise growing +50% YoY.

• Service Collection customers using AI resolve incidents 13% faster and handle 20% more total incidents.

• Customers using MCP server grow ARR at 2x the rate of those who don’t, as previously mentioned.

The Real Impact of AI = Efficiency, Not Cannibalization

It would be naive to say that AI has no impact on Atlassian.

It does, but the vector is different from what the market perceives: it is an increase in internal efficiency, not a drop in external demand.

The 10% staff layoffs in March 2026 (1,600 employees, ~$224M in restructuring charges) are the proof: Atlassian itself is using AI to do more with less staff.

The rationale behind the latest acquisition: seeking efficiency

One of the most revealing recent acquisitions is DX (~$1,000M).

Management’s rationale was exactly this: how to measure whether investments in developer experience tools (including AI) produce real ROI.

DX becomes the tool that justifies spending on all other AI tools, including Rovo itself.

Every company investing millions in Copilot, Cursor, Rovo Dev, etc., wants to know if they are getting a return.

At the Analyst Day, Brian Duffy (CRO) shared a revealing data point: 75% of DX transactions are associated with AI and the ROI of agents specifically. It is the lever that every CFO is asking for.

Competitive Advantages: Product, Suite, Data, Switching Costs

QV: This is the most important part of the thesis.

As we have discussed in several articles, the 7 competitive advantages of intangible businesses against AI are the filter we apply before investing in any company potentially disruptible by AI.

Before investing in a business that could be disrupted by AI, we must see which of those 7 advantages it possesses. If it doesn’t meet at least one broadly or two complementary ones, we are looking at an uninvestable business today.

In our opinion, Atlassian checks 2 of these boxes broadly and another 4 to a lesser extent, but they all complement each other. Let’s break each of them down in detail, followed by our conclusion.

The Product is the King

The most important thing for any business is to have a great product. If you have a great product, everything else, at some point, becomes easier.

And Atlassian doesn’t just have one great product; it has several great products, multiple products that generate billion-dollar businesses.

In the Q3 FY26 shareholder letter, Cannon-Brookes summed it up with the most quoted line of the quarter:

Atlassian’s Individual Products · A Quick Guide

For anyone not familiar with the ecosystem: Atlassian structures its catalog into individual apps, which are then grouped into “collections” (optimized bundles with a discount and extra AI credits):

Collections (5 bundles with discounts + extra AI credits)

• Software Collection: Jira + Bitbucket + Compass. For engineering teams.

• Service Collection: JSM + Assets + Rovo. For IT, HR, and customer service.

• Teamwork Collection: Jira + Confluence + Loom + Rovo (10x more credits than stand-alone). The main AI monetization motion.

• Strategy Collection: Goals + Focus + JPD. For the C-suite.

• Product Collection: JPD + Feedback. For product managers.

And beneath everything runs the Teamwork Graph: the data layer that connects all information from all products into a single knowledge graph that feeds Rovo and external tools via MCP. It is the differentiating component… and we analyzed it for you in depth.

Interconnected suite and upselling lever

It’s not one product, but 10+ deeply interconnected ones. The individual scale of each one separately is very significant, as you can see from the following data:

Three billion-dollar+ businesses running on a single platform.

The data generated by each product enriches the others, opening up powerful upselling: the customer who entered with Jira for devs ends up buying the Service Collection for HR and IT, the Strategy Collection for the C-suite, and Compass for SRE.

Ultimately, as of today,

But also, at a very attractive price…

In the service management category, JSM charges ~$55-85/agent/month on its Enterprise tier (with Rovo AI included in Premium and Enterprise), compared to ServiceNow ITSM Pro+’s ~$160+/fulfiller/month plus its additional modules.

For the time being, in our opinion, Atlassian has the best collaboration product on the market at a dramatically lower price. It is a rare combination in enterprise software.

The Teamwork Graph · The AI Moat

Atlassian has valuable data, it’s just different in nature compared to other major enterprise vendors:

• Salesforce has the customer context.

• ServiceNow has the incident and CMDB context.

• Microsoft has the document context.

Atlassian has end-to-end work context. From the initial idea in Jira Product Discovery, through the sprint in Jira Software, the commits in Bitbucket, the documentation in Confluence, the incident in JSM, and the strategic decision in Goals.

The ROI Demo · The Most Compelling Part of the Analyst Day

At the Analyst Day, Atlassian showed a live demo of the Teamwork Graph’s ROI, which is the best proof of a moat they have ever presented.

Using the exact same complex engineering task and the exact same agentic harness (Claude Code):

• Without Teamwork Graph: $2.68 per task

• With Teamwork Graph: $1.32 per task

And the math becomes even more favorable the more complex the tasks are.

Mr. Cannon-Brookes tied this directly to their competitive moat with a categorical statement about the enterprise AI era: there won’t be 20 or 30 SaaS vendors capable of doing this.

But what is context and why is it valuable?

First, let’s explain what context actually means in this scenario:

Context: all the information an AI model needs to have in front of it to understand what you are asking and provide a good response.

An AI model on its own is like a newly hired expert: brilliant in general, but knows nothing specific about your company.

It doesn’t know your projects, your code, your past decisions, who did what, when, or why. “Context” is precisely that specific information passed to the model in each interaction so it can reason about your concrete situation.

A Practical Example….

Imagine asking Claude:

“Why is this deployment failing?”

The difference in the quality of the answer is radical, and it comes 100% from the context, not the model itself.

Why Atlassian Talks So Much About “Context”

Cannon-Brookes put it this way at the Analyst Day:

Large language models (Claude, GPT, Gemini) are converging in capabilities; they are all getting smarter, and the gap between them is narrowing. But they all suffer from the exact same problem: they don’t know anything specific about your company.

Whoever provides the best context for your work wins, because it multiplies the model’s general intelligence by highly relevant, specific information.

Raw intelligence is a commodity; context is scarce.

Atlassian’s Strategic Argument

Atlassian claims to hold a unique advantage in context because it covers the entire work lifecycle:

Other vendors have vertical context: Salesforce has customer context, ServiceNow has incident/CMDB context, and Microsoft has document context.

Growth via Enterprise Clients

Atlassian’s enterprise engine relies on a different sales model than ServiceNow or Salesforce.

Traditionally, Atlassian was the poster child for Product-Led Growth (PLG): the product sells itself, without a sales team, via self-service.

Even today, 55% of new customer acquisitions still come through self-service.

On top of that PLG foundation, they have layered:

1. A global network of certified channel partners for mid-market clients.

2. Enterprise direct sales (”enterprise advocates”) for complex deals.

In short, they are going after larger clients, bigger deals, and larger budgets because they see a massive market opportunity to capture.

The Analyst Day added an important piece: for the first time in Atlassian’s history, they have signed partnerships with Global Systems Integrators (GSIs)… Accenture, Deloitte, and PwC.

Brian Duffy explained that these GSIs are opening doors to the C-suite on an unprecedented scale.

• Mercedes-Benz (a customer that took the stage yesterday) came to Atlassian via Deloitte.

• Infosys launched a dedicated Center of Excellence specifically for Atlassian.

• Accenture is generating a material and trackable pipeline.

Mr. Duffy also detailed the sales team’s transformation: 4 years ago, Atlassian had 117 quota-carrying reps. Today, it has 400.

Sales rep productivity is “best-in-class” according to management; the next lever is scaling sales headcount while maintaining this high productivity.

Q3 FY26 numbers quantify this progress:

• Deals >$3M: +79% YoY

• Deals >$5M: +54% YoY

• ASP (Average Selling Price): +22%

• Small deals: +32%

They aren’t sacrificing any segment, at least for now.

Growth via Cloud

The migration from Data Center to Cloud is the most undervalued growth driver by the market. This is where Atlassian is playing a brilliant strategic game, making decisions that might hurt short-term investor perception but are highly beneficial in the long run.

Why is Cloud the structural direction for the long term?

All recent innovations like Rovo, Teamwork Graph, MCP server, all collections, and AI integrations run exclusively on Cloud.

Data Center still works, but its features are essentially frozen. This means any customer wanting to leverage AI, graph context, or new pricing models has to be in the Cloud; there is no real alternative.

Atlassian offers deployment flexibility within Cloud for complex enterprise cases, as they highlighted at the Analyst Day:

• Standard Commercial Cloud on AWS (for most customers).

• Isolated Cloud for regulated clients requiring complete tenant isolation.

• Government Cloud on Google Cloud, certified FedRAMP Moderate.

All of these run on the same codebase. Any update from Atlassian is deployed to everyone simultaneously.

This is why they are winning customers in banking, defense, government, and healthcare. All of these are sectors that traditionally couldn’t adopt standard SaaS due to strict compliance rules.

The Cloud flywheel doesn’t stop after migration

Mr. Cannon-Brookes emphasized this:

“Cloud is a flywheel, not a one-off project.”

A customer gets onboarded, discovers Rovo, activates the Teamwork Collection, sees the ROI, adds DX, connects their graph, integrates MCP, and within 3 years, they are spending 2x more than they did on migration day.

Takeaways

Everything we’ve discussed points to one conclusion: Atlassian has broad and powerful competitive advantages against AI.

What follows is the core thesis for investing in Atlassian:

First is the empirical proof of how having end-to-end work context, where all the data from day-to-day work interactions, improves AI efficiency in terms of quality, speed, and cost savings.

In other words, this context and data are incredibly valuable in an environment where AI agents are doing the work. While it’s not the same as standard proprietary data, it offers a highly differentiating value.

If we start with the premise that AI will supercharge software production and trigger organizational chaos as companies change how they operate, then all that work has to be channeled and organized. And that is exactly the problem Atlassian has been solving since 2002.

While it might not offer a life-or-death, “mission-critical” service like processing payments or money transactions, it does offer a vital operational mission for any company that wants to keep up in today’s fast-paced, non-stop innovation environment.

On top of that, as we’ve seen, it provides massive value to customers at a low price. The gap between value and price is huge. Plus, for large companies in the enterprise sector, Atlassian’s cost is marginal. It really is just a very tiny drop in their overall budget.

If Atlassian’s collaborative product is currently the best, brings high added value to a key company process, and comes at a marginal cost, the question is:

The sole reason would be if a much better product came along offering dramatically higher value. But as customers get deeper into Atlassian’s ecosystem with more and more of their products, the likelihood of them switching drops even further.

There’s a common thread running through all these advantages that shapes the final ones: the product. A great product ecosystem builds brand power and trust with customers, creating and scaling a powerful network effect. If using Atlassian makes a company work better, faster, and cheaper, every business will want to be on it.

The key, as always, is the product, and that’s the one thing that could break this virtuous cycle. Because of this, the CEO and the company culture are vital to maintaining their competitive edge.

Here’s a little infographic for you:

In our opinion:

• Atlassian has broad and strong competitive advantages against AI. The only box it doesn’t check is regulatory lock-in because it doesn’t operate in heavily regulated industries; everything else is locked down.

• If AI explodes software production and organizational chaos, that work needs to be streamlined and organized, which is exactly the problem Atlassian has been solving since 2002.

• The company is not a victim of AI disruption; it is critical infrastructure for the post-AI era.

• The 6 advantages are fully interconnected: great product → brand & trust → network effect → ecosystem → stickier customer → more context data → better product.

• The key to keeping this virtuous cycle spinning is the product, and by extension, the CEO and company culture, which we’ll look at next.

CEO, Culture, Ownership, and Stock-Based Comp

On top of the competitive advantages we’ve discussed, there’s another key factor in the AI era for software and intangible-asset businesses:

Atlassian is still a founder-led company with real skin in the game.

Mike Cannon-Brookes (sole CEO since September 2024, after Scott Farquhar moved to the board) and Farquhar together own about 40% of the company’s equity and control 85–87% of the voting power through a dual-class share structure (Class B shares have 10 votes each, compared to 1 vote for Class A). Individually, each owns around 20% of the economic interest.

These aren’t professional, hired-gun managers who rotate out every four years with short-term compensation packages. These are two founders whose personal wealth is heavily concentrated in the company they run. During times of massive industry shifts, having founders at the helm with this much skin in the game and voting control is a huge structural advantage.

Founders think in decades, not quarters.

You can see it in their capital allocation decisions:

• The opportunistic $610M acquisition of The Browser Company.

• The $1B bet on DX (linked before).

• The $991M in opportunistic buybacks in Q3 FY26 at a depressed share price.

• Their willingness to take short-term pain (like the 10% layoffs) to speed up their path to GAAP profitability.

The ownership culture they’ve built is the other side of the same coin.

During major tech shifts, top-tier engineering, product, and design talent with real vision and judgment becomes scarcer and more expensive.

As the software business model shifts toward outcome-based pricing and AI agents executing tasks, every employee needs a personal incentive aligned with customer results.

This only happens when the team literally owns the work. Companies with significant employee stock ownership, broad equity plans, and a culture of “you are not just a resource, you are a co-owner” simply operate differently.

The Stock-Based Compensation (SBC) debate

In this light, Atlassian’s SBC (around $1.7B annually, or ~26% of revenue) stops looking like a purely dilutive cost and starts looking like alignment infrastructure.

There is still real dilution, but in our view, the right move isn’t to slash SBC dramatically. Instead, they should moderate it and offset it with aggressive buybacks.

And that is exactly what they are doing: in FY26, buybacks will outpace SBC for the first time ($1.8B vs. an estimated $1.75B), with the diluted share count expected to shrink by about 2.5% for the year, according to management’s guidance.

Note: It’s not the best, but it’s already uncommon in a software business.

Sustained and Consistent Growth

Atlassian is not a growth story that is just starting out. They have spent over a decade demonstrating high double-digit revenue growth with remarkable consistency across different macroeconomic cycles:

6-year CAGR: ~26%. The company has quadrupled its revenue in 6 years, maintaining a growth rate above 20% even after crossing the $5B mark, something very few companies in the history of SaaS have achieved.

The quality of this growth is what is most impressive:

• Multiple business lines scaling individually: Jira $2.5B+ ARR, Confluence $1.5B+ ARR, Service Collection $1.0B+ ARR.

• Confluence and Jira continue to accelerate in the Cloud: Cannon-Brookes emphasized this at the Analyst Day because the market believes the exact opposite.

• Cloud seats (excluding migrations) are accelerating quarter over quarter: Pure organic demand, not just migration optics.

• Cycle-resistant: Growth remained at 20%+ during the 2022–2023 bear market when the rest of the sector was slowing down.

• Backed by RPO growing at +37% YoY: This is outperforming recognized revenue, signaling future acceleration.

• Sustained NRR above 120% for years.

TAM updated at the Analyst Day to $140B

It is up from the previous ~$67B, recalculated as follows:

• Software development: $17B (+9% annually)

• Work management: $35B (+14% annually)

• Service management: $24B (+13% annually)

With a $7B revenue run-rate, Atlassian captures barely 5% of its relevant TAM. That’s a runway that keeps it firmly in the “early innings” zone.

Market Structure: A Practical Oligopoly with Growing Demand

In its main categories, Atlassian operates in highly concentrated markets where only a handful of credible vendors compete for major enterprise deals.

And here is a core point of the thesis: while supply is structurally concentrated, demand is about to expand massively.

Think more companies building software, more teams generating digital outputs, more organizational complexity needing coordination, and more IT services needing ticketing.

Our Synthesis

In Atlassian, we have a clear leader in collaboration tools operating within a structural oligopoly, right at a tech turning point where software demand is set to skyrocket.

The 10 pillars of our thesis:

The Numbers Challenging The Narrative

Atlassian just published its Q3 FY26 results, showing accelerated growth across all lines. At the May 6th Analyst Day, management doubled down on their strategic shift toward profitable growth. Let’s dive into the data.

For a company that’s web-rumored to be cannibalized by AI, the Q3 data tells a completely different story:

History: FCF Minus SBC Has Been Negative

The market’s main criticism is valid, and the data backs it up: Atlassian doesn’t generate true FCF once you subtract SBC (stock-based compensation).

What’s Changing: The Inflection Point in Q3 FY26

Three data points from the quarter that just closed mark a clear shift in regime:

1. Buybacks as the new capital allocation policy.

In Q3 FY26 alone, they bought back $991M in shares. That’s more than in all of FY25 ($781M). There is still $2,200M left in remaining authorization. Management has explicitly guided that the diluted share count will drop by 2.5% in FY26. This is the first year in company history where buybacks outpace SBC in absolute value.

2. The share count is actually going down.

In 9M FY26, net dilution is at -$231M (buybacks are outpacing SBC issued). Diluted shares are expected to drop from 261M at the end of FY25 to 255M by the end of FY26. This “new capital allocation” isn’t just rhetoric; it’s more like the recent hard accounting.

3. Structural cost cuts are already executed.

The $223.8M in restructuring charges in Q3 FY26 are the result of the 1,600 layoffs in March 2026. For Q4 FY26, the GAAP margin directly benefits by 7 percentage points from this restructuring, lifting guidance from -2% to +4.5%.

The most important piece: The commitment at Analyst Day

James Chuong (the new CFO who joined this quarter) closed his presentation by making it clear that they are accelerating the path to GAAP profitability with fiscal discipline starting in FY27.

How They Will Achieve It: Two Levers

(Lever #1) Aggressive buybacks as the new capital allocation policy.

In Q3 FY26 alone, they bought back $991M in a single quarter.

We expect a significant chunk of the generated FCF to keep going toward buybacks. This acts as a permanent shield against dilution, especially while the stock price remains depressed.

Net dilution in FY26 will be or, at least, should be negative.

(Lever #2) Structural cost cuts and R&D moderation.

The March 2026 restructuring (1,600 layoffs and a $224M charge) will generate ~$200M in permanent annual savings starting in FY27.

On top of that, Cannon-Brookes explicitly confirmed a key piece of the puzzle: R&D doesn’t need to grow at the same pace as revenue.

The massive platform investments, like multi-cloud, FedRAMP, and isolated cloud, are already done. From FY27 onward, R&D will grow slower than revenue, automatically boosting margins.

Three Additional Levers

Management highlighted three extra levers during their recent earnings calls (Q3 FY26 in April 2026 and Q2 FY26 in January 2026) and reinforced them at the Analyst Day:

• Continuous Cloud optimization: Non-GAAP gross margin rose 3 percentage points YoY to 88% in FY26, despite a massive spike in AI traffic.

• A slower hiring pace: The “self-funding model.”

• Classic G&A leverage: Scaling revenue over a semi-fixed cost structure.

Current Multiples and Some Historical Context

The Two Ways to Read a Software Company’s Books

Before looking at the multiples, let’s break down the differences between GAAP and Non-GAAP, and why all SaaS companies report in Non-GAAP.

Every software company publishes its results through two distinct lenses.

Here are the 4 reasons why ALL SaaS companies report in Non-GAAP, and why the market relies on this lens for valuation:

Current Valuation

An Honest Take on the Multiples Table

Through the Non-GAAP lens traditionally used to value SaaS companies, TEAM is currently trading at all-time lows.

The NTM P/FCF of 10.56x sits well below the range it sustained over its 12 years as a public company. Between 2016 and 2024, this multiple stubbornly held between 30x and 80x.

Current levels represent the practical rock-bottom for the company’s stock price. The same goes for the NTM P/E: 14x today versus a historical average of 110x, compared to levels that historically always sat between 50x and 150x.

Even when adjusting for any accounting bias, current multiples are clearly at the bottom of any reasonable range.

Through the GAAP lens, the numbers still look high (a forward P/E of 101x on consensus EPS of $0.88) because the company is not yet sustainably GAAP-profitable.

Their Bridge into Profitability

Before looking at the projected P&L, we need to understand a critical dynamic affecting the revenue trajectory for FY26–FY28. Management revealed this during the Analyst Day, and consensus estimates still haven’t modeled it correctly.

FY26 revenue is inflated; FY27 is going to look weak on the surface, but the underlying business is actually accelerating.

The Ascend Program

Atlassian has two versions of the same software: Cloud (a monthly SaaS subscription with smooth, predictable revenue) and Data Center (fixed-term licenses that companies install on their own servers, which recognize a lot of revenue upfront).

The Ascend program is the end-of-life (EOL) plan for Data Center. In September 2025, they announced that Data Center will cease to exist as a product on March 31, 2029.

Originally, they were going to announce this in September 2026, but the maturity of their enterprise Cloud allowed them to pull the timeline forward by a year.

This forces all Data Center customers to make a choice: migrate to the Cloud before March 2029 or churn. So far, 93% are choosing to migrate, and they are landing on Premium or Enterprise tiers (not Standard).

How ASC 606 Works and Why It Creates a Pull-Forward Effect

ASC 606 is the US revenue recognition standard that dictates how a contract’s revenue is split between the signing date and the following months or years. For Atlassian’s historical Data Center contracts:

• 20% of the value was recognized upfront (on the day of signing), that the “license” itself.

• 80% of the value was spread out linearly over the life of the contract for ongoing support and maintenance.

When Atlassian announced the Data Center EOL in September 2025, ASC 606 forced a recalculation: the “future support” component loses accounting value because it now has a hard expiration date. As a result, upfront revenue recognition jumps from 20% to 50%. A lot more revenue is recognized right at signing, leaving much less to recognize in the outer years.

Additionally, with the EOL set for 2029, it no longer makes sense for clients to sign 3-year deals. Atlassian moved almost all Data Center customers to 1-year contracts, giving them three distinct opportunities to close the Cloud migration instead of just one.

The Pull-Forward: The Main Effect

Knowing the product is going away, many Data Center customers decided to lock in one last large contract in FY26 before migrating. Management explicitly stated during the Q3 FY26 earnings call that they saw significant pull-forward activity shifting revenue from FY27 into FY26.

Why Atlassian Dropped the 20% CAGR Target and Pivoted to ARR

In 2024, Atlassian gave public guidance stating it would grow at a 20%+ CAGR for three years (FY25–FY27). That guidance was issued before they accelerated the Ascend timeline. Due to this pull-forward effect, FY27 reported revenue isn’t going to hit that 20% mark.

That’s not because the business is doing worse, but because accounting rules shifted that revenue forward into FY26.

If they kept the target without context, it would look like an operational miss. That’s why they withdrew it and pivoted to subscription ARR, which normalizes the ASC 606 distortion and shows the true story: three consecutive quarters of operational acceleration.

Showing the Inflation: Reported Revenue vs. Real Business Signals

Here is proof that FY26 revenue is artificially inflated. Looking only at total revenue makes it seem like Atlassian is growing faster than normal.

But if we look at metrics unaffected by Ascend, we get a much clearer picture of reality:

• Cloud at +26.5% is the metric that best reflects the actual business. It’s accelerating compared to ~25% last year, not slowing down.

• Reported Data Center at +21.5% (vs. a historical trend of ~10%) reveals roughly 11 percentage points of accounting inflation. Without Ascend, it would have grown around 10%.

• RPO at +37% is the strongest indicator. It reflects signed contracts that will be recognized as future revenue. It’s growing much faster than reported revenue, pointing to future acceleration, not a slowdown.

• The new metric management uses, subscription ARR (combined Cloud + DC), has now accelerated for three straight quarters.

• If we normalize FY26 revenue by stripping out the pull-forward effect (an estimated ~$200–250M), “clean” growth would be around 20–21%, not 24%.

The Insight for the Thesis

This was one of the most revealing moments of the Analyst Day. When Cannon-Brookes started explaining how the accelerated shift to Cloud impacts reported revenue, he laughed on stage: “And now the bears are going to come out and say we’re slowing down and the business is struggling.” He said it half-jokingly because he knows GMT algorithms and casual investors will misread the data.

This isn’t an actual tangible business weakness, but more so a product of accounting (timing). Subscription ARR keeps accelerating, Cloud is growing at 29%, and RPO is at 37%. However, upcoming reports will show lower reported revenue growth, and most of the market will just read the headline and sell without understanding the underlying mechanics.

QV: That is the exact opportunity. When the market misreads the wrong metric, those of us who have done the homework get to buy at a discount. If reported revenue disappoints in FY27 and the stock drops while ARR keeps accelerating, that’s basically the perfectly wrapped up gift we’ve been waiting for.

Three Scenarios for 2030 and Target Price

Projected P&L year by year to FY30

This is our base case scenario, and in our view, it is conservative:

On the valuation lens: how we handle SBC (the waterfall method)

A key question when valuing a SaaS company with stock-based compensation (SBC) at ~27% of revenue: how do you bake that equity compensation into your valuation? SBC shows up in two ways: as a non-cash expense on the P&L (which gets added back to FCF) and as share count dilution. The trap is either completely ignoring it or double-counting it.

The “zero times” trap means capitalizing gross FCF (including SBC) while assuming a falling share count. The “two times” trap means subtracting it from FCF and also modeling dilution.

Our solution is an explicit waterfall that accounts for it exactly once: we start with gross FCF, subtract the buybacks needed to neutralize SBC dilution (roughly equal to the annual SBC), and arrive at “clean cash” (FCF − SBC). We then capitalize this clean cash and keep the share count flat at ~258M.

The first two approaches give you the same value per share; the third mistakenly gives credit for buybacks without paying for them. Regarding the flat share count: the buyback plan (~$9B over 6 years) at today’s depressed prices essentially offsets SBC dilution, keeping shares right around 258M. We aren’t modeling any extra share count reduction because that would require spending the exact same distributable cash we are already capitalizing.

That’s why we track “net dilution” as a kill criterion: if buybacks stop offsetting SBC, the share count will start growing, and distributable cash per share will erode. This metric directly tells you if SBC is eating away at shareholder value.

The 4 levers driving the +22pp margin expansion (GAAP)

The four levers we used for this margin expansion are:

Three scenarios through FY30

Philosophy behind the 3 scenarios:

• The Bear case penalizes growth, margins, and multiples all at once. It’s our worst-case benchmark where the bear thesis plays out completely.

• The Base case tracks our core thesis and the data already playing out, framed conservatively.

• The Bull case captures the collapse of the “SaaSpocalypse” narrative and a re-rating to multiples typical of companies growing at 20%-.

Why the Bull weight (35%) is higher than the Bear (25%):

Data from Q3 FY26 and the May 6th Analyst Day point much closer to the bull side than the bear side:

• Revenue acceleration (+32% in Q3 vs. +20% in FY25)

• RPO +37% (the most powerful forward-looking signal)

• AI credits +20% MoM

• Rovo customers growing 2x

• Largest quarter of ServiceNow displacements in history

• New GSI partnerships (Accenture, Deloitte, PwC)

• Teamwork Graph ROI demo

• Public commitment to GAAP profitability in FY27

We give the Bull case the highest weight (40%) because of how re-ratings work: the market moves irrationally, and the moment the AI narrative shifts from being seen as a threat (like today) to a catalyst, the upward swing will be massive.

The exit multiple in the Base case: 25x on clean cash (≈11x gross FCF)

We capitalize the FY30 base case clean cash ($1,512M = $3,402M FCF − $1,890M SBC) at 25x: resulting in an equity value of $37,800M and a price of $147 based on 258M shares.

That 25x multiple on clean cash is equivalent to just ~11x on gross FCF, a quality compounder multiple that remains highly conservative compared to industry peers (which are measured on gross FCF or gross profit):

Weighted target value for 2030

Our Action Plan

Realism about the short term

But the reality is that, at least in the short term, stock volatility is going to be very high. AI uncertainty will hang over the company for months or even years, and we need to be ready for it. Right now, the market simply doesn’t know Atlassian’s terminal value… and that’s exactly where this volatility comes from.

As you’ve probably gathered throughout this thesis, we estimate its future value will be very high and believe it’s a great investment. But let’s be clear: it’s not risk-free.

Let’s be honest here: even though the data shows we’re likely right, we are still dealing with an investment tied to innovation uncertainty. Because of that, at least for now, we cannot give it a heavy weight in the portfolio.

With all of this in mind, and even though we’ve covered the company extensively, there’s still more to it. If you want to continue reading, you can check it out here…

Quality Value Research

Tesis Atlassian: cuando el contexto lo es todo para la IA.

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23 days ago · 7 likes · Quality Value Research

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The views, thoughts, and opinions expressed in this presentation belong solely to the author and are not necessarily indicative of the views of Equity Focus FIL, Hermit Ventures Ltd., 25Delta E.A.F., their management, affiliates, or employees. This presentation may contain forward-looking statements, which are based on beliefs, assumptions, current expectations, estimates, and projections of the author. Such statements are subject to risks and uncertainties that could cause actual results to differ materially.

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We were warned this would hurt our short-term conversions, but this project has never been a volume game; it is a search for aligned, long-term partners.

Our work is built on institutional-grade primary research. The monthly reports we publish are not recycled news, AI blabber, or basic financial modeling.

They are built from exhaustive 1-on-1 private interviews with key industry stakeholders: executive management, current and former employees, direct competitors, suppliers, and clients.

These proprietary conversations allow us to build concrete projections and gather raw field data that public markets completely overlook.

However, the nature of our work is shifting, and our reporting must evolve with it.

We recently raised a few million dollars at what we believe is the peak of a market bubble. Consequently, we are focusing our capital on negotiating and closing controlling stakes (51%+) in private enterprises.

Because our upcoming monthly reports will now detail live, highly sensitive private acquisitions and deal mechanics, we can no longer share them broadly.

Window for Alignment: For those ready to commit to our long-term horizon, we are offering a final alignment window.

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Secure Annual Partnership & Upgrade

Operational Update. Deploying Capital in a Bubble

On the operational side, we have some significant updates.

Last week, we closed our first private deal. While local bureaucracy means official registration will take 1 to 2 months, the transaction has been secured.

Behind it, we have two additional deals in the final sprint of our pipeline, and we expect to close at least one more before the end of the year.

We understand that committing capital in this environment is difficult.

Many of our partners have been burnt by the sheer incompetence and friction of legacy banking and regulatory structures under our previous setup. We watched partners spend months just trying to register or liquidate funds for no logical reason.

When the system makes you jump through those kinds of hoops, committing capital is understandably hard. Because of this, many of our partners choose to test the waters with a £100k commitment before scaling up.

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Unlock the 50-Page Deep Dive

Investment Thesis is our core series where we break down standout public companies so you understand exactly how they make money, where the risks lie, and why the opportunity exists long before the market agrees.

This content is intended for informational purposes only and should not be taken as investment advice. The author does not represent any third-party interest, and he may be a shareholder in the companies described in this series.

Please do your own research or consult with a professional advisor before making any financial decision. You will find a full disclaimer at the end of the post.

Context

• Business Overview

• Origins

• Industry Overview and Competitive Landscape

The Business

• M&A Business Model

• Business Model

• Management

• M&A Track Record

Investment Case

• Comprehensible Financials

• Catalysts Explained

• Valuation

• Risk Assessment

🔍 Business Overview

Imagine taking a legacy software monopoly, injecting it with the capital allocation DNA of Constellation Software, and letting it loose on the oil patch.

Introducing youuuur heavy oil chaaaaaaaampion of the woooooooorld…

Historically, this company has been a publicly traded university research department… chasing cool scientific anomalies instead of tangible commercial returns $$$.

But a recent pivot has turned this quiet Canadian cash cow into a weaponized software rollup engine.

If you appreciate capital discipline, negative working capital, and asymmetric tech bets, this is a business you can’t afford to ignore.

At its absolute baseline, CMG has earned itself a pretty solid moat in upstream oil and gas reservoir simulation.

When supermajors need to figure out how multi-billion-dollar thermal or chemical enhanced oil recovery (EOR) projects will behave underground over a 30-year horizon, instead of guessing, they’ll use CMG’s core physics engines (STARS, GEM, IMEX).

Once an operator populates a reservoir model with decades of historical data, ripping that software out is pretty close to a nightmare.

CMG operates on a beautiful negative working capital model. E&P clients pay for their software licenses 100% upfront on day one.

And.. they gets to hold that advance cash like an interest-free loan to fund operations before even delivering the full service.

Under the new CMG 4.0 strategy, management has pivoted from a single-product footprint into a serial vertical market software (VMS) acquirer.

To fund this new playground, the CEO made the ultimate alpha move: slashing the legacy dividend by 80% and reusing cash in much more efficient ways by allocating optimally into M&A, R&D and buybacks.

They’ve stacked the board and executive team with heavy hitters straight out of the Constellation Software ($CSU) and Roper Technologies ($ROP) ecosystems to run a highly disciplined M&A playbook.

Between late 2023 and early 2026, they deployed over $90m to buy four founder-led software targets, systematically expanding their grip across the entire upstream workflow.

The following post is a hyper-detailed deep dive that has been cooking for about three months. It includes firsthand perspectives from a wide range of participants, some of whom will remain anonymous for confidentiality.

Inside, we’ve incorporated exclusive insights from 18 first-hand interviews with executives of supermajor (clients), primary competitor employees (and their feedback), and direct commentary from executive management, including but not limited to:

• Pramod Jain, CEO

• Kim MacEachern, (extraordinarily competent) Director of IR

As always, the bottom third of the post is dedicated to diving deep into the financials, risks, and value-unlock catalysts for this company, but here is what you should know upfront:

• Top-tier leadership: Their CFO comes straight out of Constellation Software, and they are executing a phenomenal M&A playbook across the simulation software value chain.

• Aggressive buybacks: They are heavily buying back their own shares.

• A game-changing launch: Just a few weeks ago, they rolled out a major new software deployment targeting unconventional reservoir modeling that changes everything.

🌱 Origin Story

There’s nothing like starting with a bit of an ideological rant. We all love a good underdog story where government intervention actually creates some good.

Let’s call this The Hermit 0, Gov of Alberta 1.

CMG began in 1978 within the Chemical and Petroleum Engineering Department at the University of Calgary.

Dr. Khalid Aziz, a prominent professor and researcher, founded the organization using a research grant from the government of Alberta to build a digital simulator capable of modeling complex subsurface oil reservoirs.

For the first 19 years of its existence, CMG operated strictly as a non-profit research foundation. Its primary mandate was to solve the complex scientific challenges associated with the thick, geologically hostile heavy oil reservoirs and tar sands of Western Canada.

The inception math and fluid mechanics engines were built by top-tier scientific minds during this era, including long-term pioneers like engineer Long, who spent 47 years deep in the core physics code before his recent retirement.

As the energy landscape shifted during the 1980s and 1990s, easy-to-extract primary oil reserves began to deplete globally. Operating companies were increasingly forced to deploy secondary and tertiary enhanced oil recovery (EOR) techniques (this is important), such as thermal and chemical injections, to extend the life of their assets.

CMG, right time and right place, capitalized on this transition by introducing commercial software products in the late 1980s that could enforce complex physics across underground systems with unmatched precision.

This high-science approach turned CMG into the de facto gold standard for advanced reservoir simulation, as international operators realized that miscalculating subsurface dynamics could instantly ruin a billion-dollar asset.

Seeking the capital necessary to scale its global footprint, the organization officially transitioned into a publicly traded corporation in 1997, listing its shares on the TSX.

Throughout the late 1990s and 2000s, the company expanded its core engine into global energy markets and leveraged its thermal simulation expertise to build an early, dominant position in adjacent environmental workflows like carbon capture and storage (CCS) simulation.

Note: Carbon capture may sound like nothing, but it’s one of those technologies that’s very important, especially in a Biden-like era.

Despite decades of consistent profitability and reliable cash generation, the company historically behaved more like a publicly traded university than an aggressive software growth enterprise.

This operational framework shifted dramatically in 2019 when Mark Miller, a veteran enterprise software executive celebrated for his role in building Canada’s preeminent serial acquisition machine (Constellation Software), joined CMG’s Board of Directors.

Miller assumed the role of Chairman of the Board in 2022 and recognized that CMG’s deeply entrenched customers, high pricing power, and massive switching costs formed an ideal foundation for a software rollup platform.

IR: […] CMG’s history is that it was a carve out from the University of Calgary. (It) was originally a research foundation, but in many ways it was still being run as a research foundation.

So there was lots of pet projects going on, really smart people solving really hard problems, but with no commercial lens on it.

So we apply that same return lens or IRR lens on an internal initiatives as we do to M&A.

In May 2022, the board appointed Pramod Jain as Chief Executive, intending to institute “institutional” capital discipline and starting the era of what is now known as the CMG 4.0 Strategy.

Under the CMG 4.0 mandate, executive leadership pivoted the business model from a single-product footprint into an acquisition-focused software aggregator targeting specialized tools across the entire upstream energy workflow.

To fund this pipeline, Mr. Jain executed an aggressive capital allocation pivot in August 2025, slashing the company’s long-standing quarterly dividend down to a nominal token to unlock cash flows for R&D and buybacks.

This internal architecture was deepened in July 2025 with the hiring of Vipin Khullar as CFO. Khullar, who brought eight years of disciplined acquisition and shared-services experience from (Perseus) a prominent Constellation Software subsidiary, focused immediately on constructing a repeatable M&A diligence playbook.

Further strengthening this rollup governance, CMG appointed Christopher Wright, not the US minister but a thirty-five-year board veteran of the elite vertical software acquirer Roper Technologies ($ROP), to its Board of Directors on February 10, 2026.

Note: I know, that’s a lot of names and links. The jist of it is that $CSU is very embedded into the current final form of CMG (dragon ball reference right there).

These aggressive capital deployment initiatives have completely transformed the business model of company. Between late 2023 and early 2026, CMG successfully deployed over $90m to acquire four distinct software targets:

1. Bluware in September 2023 for cloud-native AI seismic interpretation;

2. Sharp Reflections in November 2024, led by CEO Bill Shea, for high-performance computing pre-stack seismic analysis;

3. SeisWare International in July 2025, led by CEO Murray Brack, for integrated geological mapping and 3D well design; and

4. Rose Subsurface Assessment in March 2026, led by Managing Partner Peter Carragher, to capture the market for probabilistic risk assessment and resource estimation.

We’ll discuss these targets thoroughly one by one in the business model segment.

Moving on to more recent milestones, CMG has rolled out a few massive platforms, including CoFlow and their brand-new ShaleSim, which is making its official debut as we speak at the URTeC conference (yes, I scheduled this a few weeks ago).

We want to treat these products for what they really are: the future of the company. We’ll expand on them in the Business Model section.

To wrap things up, we’ll look back at a highly insightful, older presentation from the exact moment the company transitioned its core engines into the cloud.

What they uncovered during that initial migration lays the blueprint for how they are setting up to corner the market today..

🌐 Industry Overview and Competitive Landscape

We already covered the broad strokes of upstream, midstream, and downstream oil and gas in our Spartan Delta piece, but today we’re focusing on the upstream software market.

Before we jump into the specific companies, it helps to look at how the entire value chain fits together. This is a multi-billion-dollar network split into four core phases:

1. exploration

2. appraisal

3. field development, and

4. production operations

It’s an extremely interconnected ecosystem where one or two giant players dominate massive slices of the market, yet they frequently rely on smaller, specialized niche tools to actually make their workflows function.

Here’s a quick infographic summarizing each phase and the key players we’re about to break down:

Upstream Software

Phase #1: Exploration & Geophysics

This is the land of Seismic Processing & Interpretation. Sound waves are pumped into the ground, captured as raw data, and cleaned via high-performance computing (HPC) into 3D subsurface images. Geoscientists then manually or algorithmically interpret these images to map geological faults, stratigraphy, and identify potential hydrocarbon traps.

SLB controls the undisputed lion’s share of this segment. Its flagship platform, Petrel, commands an overwhelming 90% to 95% market share inside the geosciences workflows of the world’s major oil companies and national oil corporations.

Large enterprise alternatives include Halliburton’s DecisionSpace and Emerson/Paradigm.

Small-to-mid-market operators or independent consulting shops frequently deploy lower-overhead solutions like S&P Global’s Kingdom (IHS Kingdom) or SeisWare (part of CMG*).

Note: If you want to read further about this topic, here’s a really good article explaining the topic thoroughly.

Phase #2: Appraisal & Geological Modeling

Once a prospect is discovered, engineers collect well logs, physical core samples, and fluid data to build a static 3D digital model of the reservoir’s exact shape, rock property distributions, and internal boundaries.

Because geological modeling bridges the gap between seismic interpretation and dynamic engineering, SLB (Petrel) seamlessly flows its dominant position down into this phase. Halliburton is the secondary enterprise heavyweight.

Niche specialized tools like Rose Subsurface Assessment (part of CMG*) capture a highly specialized subset of this market by layering probabilistic risk metrics and resource estimation directly over static structural datasets.

Phase #3: Field Development

We’re now hitting close to home with Reservoir Simulation & Surface Engineering. This is the highest-stakes computational step in the chain. Dynamic reservoir software applies physics-enforced laws of thermodynamics and fluid mechanics over 15-to-50-year projection lines to simulate how oil, gas, and water will actually flow under different drilling and recovery strategies.

This is a tightly consolidated duopoly/oligopoly market. SLB (ECLIPSE and INTERSECT) and Computer Modelling Group (IMEX, GEM, STARS) own the dominant tier-1 enterprise installations globally.

SLB definitely has the horizontal reach across the board, but CMG holds the dominant market share in highly complex high-CAPEX assets.

To be very specific, CMG dominates in areas related to controlling heavy oil, thermal composition modeling (SAGD), and complex chemical Enhanced Oil Recovery (EOR) workflows.

The primary modern challenger taking market share from legacy installations is Rock Flow Dynamics’s tNavigator, which has grown aggressively in regions like Western Canada.

Phase #4: Production Operations, Reserves & Economics

Once wells are drilled, software takes over to automate trajectories, monitor active well flow rates, manage surface processing pipelines, track depletion, and dynamically certify proven reserves for corporate balance sheets and regulatory compliance filings.

Production engineering surveillance is dominated by major service providers like SLB Avocet and Baker Hughes. Again, SLB proves to have an end-to-end solution.

The final accounting, asset valuation, and macro-portfolio planning layer is highly fragmented, led by large aggregators and business software ecosystems like Enverus and Quorum Business Solutions (these guys also absorbed Aucerna, which used to be a competitor in the space).

We know it’s a bit confusing for non-technical people.

Plus, we are, after all, bombarding you with information… so here’s the industry map set up by CMG to help you better visualize the space.

Major Players

SLB (formerly Schlumberger)

We eed to dive a bit into these guys. SLB is the undisputed heavyweight of the digital oilfield.

Their core leverage starts with Petrel, which, again, commands an overwhelming 90-95% monopoly over the global static geological modeling market.

Because geoscientists build the initial 3D subsurface asset models inside this Petrel environment, SLB uses it as an immediate entry point to push its own dynamic simulation engines, ECLIPSE and INTERSECT, which historically dominate conventional reservoirs with a 60% market share.

If an engineering team wants to deploy CMG’s software instead, they are forced to deal with the severe operational friction of manually exporting and converting historical models out of the SLB ecosystem.

This conversion process is incredibly difficult, causing deep pain and aggravation for asset teams who risk losing consistency and accuracy across their predictive models.

What makes SLB an omnipresent threat is their ability to shift the commercial conversation away from grassroots technical performance toward top-down corporate packaging.

Because they handle massive physical oilfield service contracts globally, they can leverage deep corporate partnerships that a specialized software firm simply cannot match.

SLB regularly packages their software tools into broad enterprise-level licenses, pitching all-in bundles directly to procurement departments and chief financial officers. When an oil company signs a sweeping master agreement with SLB, standalone invoices for independent point-solutions suddenly look standalone invoices for independent point-solutions suddenly look redundant and inefficient to executives.

This aggressive, top-down strategy played out directly in CMG’s recent major contract loss with a massive customer in the Middle East. On the ground, the asset teams ran a rigorous software evaluation, and CMG actually won the technical bid because its engines handled complex subsurface workflows far better than the competition.

The field engineers had been happily using CMG software for a long time and fought hard to keep it. However, SLB completely bypassed the technical users and went straight to the C-suite with an aggressive corporate pricing strategy. The decision was ultimately forced from the top down, mandating an enterprise-wide switch to SLB regardless of what the engineers on the ground preferred.

Despite these aggressive tactics, SLB’s land-grab strategy has created a natural defense mechanism for specialized players like CMG. Because operators fiercely guard their proprietary asset data and fear losing pricing leverage, most standard oil and gas companies refuse to be completely locked into a single mega-vendor like SLB.

It is standard industry practice for operators to maintain licenses with more than one simulator concurrent with SLB, using alternative engines to check model accuracy and protect themselves from vendor leverage.

Even when SLB wins a top-down corporate contract, converting highly complex models can take years with no guarantee of accuracy, leaving frustrated engineering teams actively advocating to bring CMG’s trusted math back to the field.

Rock Flow Dynamics (RFD)

RFD is a nimble, fast-growing tech firm that poses a direct structural threat to CMG’s legacy geographic dominance.

Their core product, tNavigator, is an integrated software suite built from the ground up to utilize modern GPU and High-Performance Computing (HPC) hardware architecture.

It combines geological modeling, PVT properties, and reservoir simulation (black oil, compositional, thermal) into a single user interface. RFD is applying severe competitive pressure on CMG’s core market share, particularly in mature basins like Western Canada.

Halliburton (Landmark) [NYSE:HAL]

Note: Do yourself a favor… if you haven’t seen Adam McKay’s Vice, go watch the trailer right now.

It’s highly educational, incredibly sharp, and fun as hell. Absolute masterpiece. Plus you’ll understand a lot about Halliburton.

Like SLB, Halliburton competes by wrapping software tools into its massive corporate oilfield services bundle.

The company has two core products: Nexus and VIP (Multi-component reservoir simulation software).

Nexus supports full subsurface asset asset-modeling configurations. Halliburton targets enterprise operators who prefer a single, diversified generalist vendor to handle broader asset development over standalone, specialized tools.

ResFrac

ResFrac is a highly focused niche player operating primarily in the North American shale market.

Their core product is a coupled hydraulic fracturing and reservoir simulator.

Historically, CMG’s legacy simulators struggled to model the complex, rapid physics of hydraulic fracture networks, allowing ResFrac to win market share among unconventional operators in tight shale plays.

Rather than paying an expensive valuation multiple to buy ResFrac, CMG built its own tool, ShaleSim, which officially launched to directly target and reclaim market share from ResFrac across emerging global unconventional plays like Argentina.

Stone Ridge Technology

A boutique technology vendor targeting extreme parallel computing workloads.

Their core product, ECHELON, is an ultra-fast GPGPU-based reservoir simulator built explicitly for (massive) data-heavy structural processing runs.

It targets specialized asset groups, prioritizing pure raw computation speeds for immense grid-block fields.

What They’re All Missing

Despite billions of dollars in digital oilfield investments, the upstream software stack still suffers from deep, legacy design gaps. These represent massive pain points for operators, but, more importantly, they are clear entry points for specialized tech players.

Understanding exactly where these cracks are is vital. With AI now accelerating software development, the ROIC in this space can be massive, both through organic, in-house builds and by acquiring nimble players who are reimagining how supermajors approach these problems.

But is the rest of the industry actually competitive, or are they completely asleep at the wheel?

It all comes down to the following critical structural blind spots that current players are either failing to address or ignoring...

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We not-so-briefly introduced our quarterly update on Wednesday, so if you missed that, I highly recommend checking it out.

Don’t mind me, but I think we did a great job breaking down a massive wall of data into a digestible (audio) format, plus there’s a quick 5-minute written breakdown for anyone who just wants to skim the highlights.

Today’s business is one that pays a very handsome “fair share” to retain its tech talent, handing out a substantial $177m in stock-based compensation to keep its software engineers locked in.

Yet, despite that heavy equity package, the business is an absolute cash machine. It is printing sustainable FCF of $323m. With the market cap currently sitting right at $3.3bn, and backed by a fortress balance sheet holding $1.5bn in raw cash and net cash position of over $1bn.

That’s the math we loooove to see… 15%+ net cash-adjusted free cash flow yield.

On top of that, the capital allocation from the company is on point. The board authorized an $870m share repurchase program… roughly 26% of the entire market cap. They have already aggressively executed $554m in Q1 2026.

That buyback volume is more than 4.5x the dilution coming from the employee equity comp.

Throw in an exceptional return on invested capital crossing well over the 25% mark, and the operational excellence speaks volumes of what we’re looking at.

Let’s break it down.

As we now do every week for transparency and to provide full value, here is our list of Pick of the Week posts and their returns since publishing:

• Equally weighted picks: +15.83%

• Fund-allocated picks: +35.34%

Quick notes before we move one:

1. Looking at our broader track record, you will also notice RedViolet (#24) listed on the board.

We have a deep institutional report ready for that one too, but out of fairness to our ongoing private capital raise (formally closes on July 31st), we are keeping it under wraps until the funding window officially locks.

I appreciate your patience on that front, but I can already tell you it’s amazing. Collecting prime data in an era of AI is literally shovels during a gold rush.

2. We also use the closing price of the previous day as our indicative price at the time of reporting. For this specific breakdown, we are using the July 1st price, as that is exactly when we disclosed it.

Pick of the Week. A curated series of high-conviction research on companies currently under our microscope. We screen for specific dislocations where the market has mispriced the balance sheet or earnings power.

None of the following should be construed as investment advice. Please consult a financial advisor before making any investment decision. You will find a full disclaimer at the end of this post.

📊 Monday.com Ltd. ($MNDY)

⭐ Overall Grade: 9.5 / 10

Founded in 2012 under the name daPulse, platformed in 2014 and then rebranded in 2017, monday.com is a customizable cloud operating system (“Work OS”) built on a highly flexible, schemaless database infrastructure called mondayDB.

At its core, monday.com is a little bit more than just a glorified task list, a spreadsheet, or an off-the-shelf project tracker.

Instead of locking teams into static, pre-packaged software layouts, it gives non-technical professionals a blank canvas of building blocks like columns, statuses, automation recipes, and application integrations.

Users can click and drag these pieces together to sculpt entirely bespoke software architectures that mirror how their specific business actually operates day-to-day, all without writing code.

Because of this structural open-endedness, the platform seamlessly warps to fit thousands of entirely unrelated industries.

A production manager in a heavy manufacturing plant can use monday.com to run raw supply chain logistics, while right down the street, a creative director uses the exact same platform base to manage a marketing video edit cycle.

And, over this foundational infrastructure, the company offers tailored vertical setups.

However, because all of these custom tools share a unified codebase, corporate departments can easily share mirror links, aggregate cross-board reporting dashboards, and deploy background automations instantly.

Ultimately, monday.com functions as the centralized operational system of record and custody layer for an enterprise. It replaces the fragmented mess of disconnected emails, rogue WhatsApp groups, and chaotic spreadsheets with a single, visually clear space where human workflows, system permissions, and history are governed.

It represents a flexible work database that naturally absorbs a company’s unique tribal habits and internal communication loops, embedding itself so deeply into the organization’s daily mechanics that extracting it becomes a major corporate headache.

If you want to understand all of its features, you should check this video out:

In a single sentence… monday.com is a highly profitable, founder-led global Work OS platform that orchestrates enterprise workflows through a flexible, multi-product codebase, providing a heavily insulated data custody layer that transforms the threat of AI into a compounding financial vector.

Unlock the Full Report

SaaS-Pocolypse and CAIR

Before we crack open each product line, and given how much we’re talking about this, we have to address the elephant in the room: the SaaS-Pocolypse narrative.

The market is violently de-rating these stocks based on a ridiculously simplistic fear. They all think corporate clients will just use LLMs to “vibe code” their own custom internal tools, or that agentic AI will kill seat-based software by letting one employee do the work of ten.

But that logic completely ignores how human beings and enterprise organizations actually adopt technology. Assaf Elovic (now Nebius) at monday.com and Harrison Chase at LangChain put out a brilliant framework called CAIR (Confidence in AI Results).

It looks like this:

When a raw AI agent commits a mistake in isolation, the denominator explodes. If an untethered agent hallucinates an action, deletes data, or emails a client the wrong quote, it causes chaos.

Let’s look at some examples:

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I’m truly honored to have you by my side on this journey.

This update is less numerically driven and more about the precise direction we’re taking with both our holdings and our project.

As with every update, we’ve included a TL;DR summary that you should be able to blast through in <5 minutes.

None of the following should be construed as investment advice. Please consult a financial advisor before making any investment decision. You will find a full disclaimer at the end of this post.

It is currently a brutal 47°C (116°F) in Europe, and Paris apparently had to ban alcohol because people were getting drunk, overheating, and melting into the pavement.

That’s why we’ve swapped out our usual cozy fireplace background for a much more appropriate virtual beach.

You’re welcome for the teaser! 😂😂😂

Engine #2: SaaS-pocalypse & Oil

Most oil and gas dependencies have been sold off (Saturn, Yangarra, Constellation). The oil cycle is getting twitchy due to macro sanctions and an impending supply wave (nuclear pivots in Germany, new Canadian West Coast gas pipelines targeting Southeast Asia).

Ongoing Favorites

• ASG: A major write-up went out last Friday on the company. Zero competition in a niche space, stellar management, and a highly probable medium-term acquisition catalyst by a medical giant.

• AEP: A slow-burning 10-year cyclical play handling construction and housing material logistics. They are perfectly positioned to profit from global housing supply shortages.

• CNO: A high-quality micro-cap that has been stalling due to shifting military/aerospace contractor priorities. They recently pivoted into nuclear cash flows. There is no immediate short-term catalyst, but the management team is young and highly competent.

Write-Up Pipeline

You have screened 2,000+ companies so far this year. The cream of the crop is dropping soon in a super detailed format:

• Computer Modeling Group: High-margin Canadian reservoir simulation software. They have acquired 4 companies in 33 months and should return to their historical 40% EBIT margin. A massive deep-dive report is completely ready to drop in the next week-ish.

• Blackline & Monday: The SaaS-pocalypse narrative (AI replacing software) has created absurdly cheap valuations. Blackline is positioned to take over the back-end auditing work of the Big Four accounting firms. Monday.com has 33% of its market cap sitting in pure cash and is aggressively buying back shares.

• Portillo’s: Juicy Chicago hot dogs and beef sandwiches. Debt is a bit high and the share structure is slightly weird, but they hit every single operational metric they set for themselves.

• Smart Organic: A €200m Bulgarian (Eastern European) organic protein food company. Exceptional quality and cost leadership. An interview with the CEO is scheduled for tomorrow, with a write-up following shortly after.

Engine #1: Our Private Ecosystem

We are no private equity firm flipping companies for a quick buck. As prviously discussed, we are building a permanent home for small businesses generating sub-€1m in EBITDA.

Closing Notes

• Possible PropTech Bets: We are exploring 10–20% minority checks into early-stage AI software game-changers like Kausa and Fragua to capture massive productivity tailwinds. More on this soon.

• Subscriber Submissions (Rubo + Genius Sports + BeWhere): We looked at them, but they failed a few of our tests. No major price dislocations, no scalability, and zero catalysts to make them massive home runs.

As a closing thought, we are officially re-addicted to Chess.com after a multi-year hiatus.

We are looking to learn and welcome anyone who wants to challenge us.

Tag = alyela

Hope to see you around!

Disclaimer

This presentation is provided for informational purposes only and is not intended to be, and should not be taken as an offer or solicitation to buy or sell any securities or to invest in any investment strategy. The information contained in this presentation is not investment advice and should not be relied on as the basis for any investment decision.

The views, thoughts, and opinions expressed in this presentation belong solely to the author and are not necessarily indicative of the views of Equity Focus FIL, Hermit Ventures Ltd., 25Delta E.A.F., their management, affiliates, or employees. This presentation may contain forward-looking statements, which are based on beliefs, assumptions, current expectations, estimates, and projections of the author. Such statements are subject to risks and uncertainties that could cause actual results to differ materially.

Readers should consider the investment objectives, risks, charges, and expenses of any investment carefully before investing. The author does not guarantee the accuracy or completeness of the information presented, which is subject to change without notice. The author is not responsible for any errors or omissions, or for the results obtained from the use of this information.

This publication does not take into account the investment objectives, financial situation, or specific needs of any particular participant. It is not to be construed as legal, tax, investment, or other advice, and the author recommends that investors seek independent financial, legal, and tax advice before making any investment decision.

Investments involve risks, including the loss of principal. The value of investments and the income from them can fluctuate, and investors may not get back the amount originally invested. Past performance is not necessarily indicative of future results.

This presentation is confidential and is intended solely for the use of the person or entity to whom it is given or sent and may not be reproduced, distributed, or transmitted without the prior written consent of the author. By accepting this presentation, the recipient agrees to be bound by the foregoing limitations.

Note that this is a material change and the information has been vetted by 3 independent sources.

As always, we’ve included our usual short overview of Aurora Spine to start, but if you already know the company, feel free to skip straight to the major update.

Access the Inner Circle

Priority Briefings. Investing is dynamic, so our research never sits still. We don’t file a report and walk away; we maintain an active, ongoing dialogue with the key stakeholders and management teams of our past, present and future holdings.

Think of these briefs as your “insider access” to those calls. We strip away the corporate speak to bring you the direct highlights and actionable updates. You’ll see exactly how the business is evolving in real-time, long before it hits the quarterly report.

This series is exclusively for 🧙‍♂️ Inner Circle members

None of the following should be construed as investment advice. Please consult a financial advisor before making any investment decision. You will find a full disclaimer at the end of this post.

Aurora Spine Corporation ($ASG) Brief

Aurora Spine is a medical technology disruptor turning the massive $10bn spinal implant market on its head. While they are technically a Canadian-listed entity (trading on the TSXV), they hang their hats in Carlsbad, California, operating as one of the few hardware designers of minimally invasive surgery devices.

Unlike the old-school one-size-fits-all metal hardware that treats every spine the same, Aurora has developed smart (regenerative) implants that mimic a patient’s actual anatomy.

They are precision engineers of the human back looking to fill in the void for interventionalists and surgeons combined.

Product Overview

Aurora focuses on high-tech platforms designed to make surgeries faster, safer, and way less painful:

• DEXA Platforms: Traditional spinal implants are often too stiff, causing them to sink into the bone over time. Aurora’s DEXA implants are 3D-printed to match a patient’s specific bone density. They just secured their 4th US Patent for this tech in May 2026, and they are launching the expanded DEXA-L (for the lumbar spine) in Q3 2026!

• SiLO & SiLO-TFX: These systems target Sacroiliac (SI) joint pain. Instead of massive, muscle-tearing surgeries, doctors use a tiny incision to stabilize the joint, resulting in lightning-fast recovery times.

• Zip & Aurora Biologics: Launched in late 2025, their new biologics division is already a rockstar, posting a 124% sales bump in Q1 2026. They now pair these regenerative biologics with their implants for better bone healing.

Recent Financials

Aurora spent over a decade building its IP fortress, and the business has officially shifted into commercialization, making the company self-sustaining since 2025:

• (Juicy) 62.6% Margins: By aggressively swapping out third-party products for their own proprietary hardware, gross profit margins climbed to an impressive 62.6% in early 2026.

• Free Cash Flow (FCF) Nearing Breakeven: For Q1 2026, operational cash burn slowed to a crawl at just -$13k. When you drop in their capital expenditures ($22k for equipment and tech additions), their Q1 FCF sat at -$35k. Compare that to the painful -$320k FCF burn in Q1 2025, and it's clear they are right on the cusp of self-sustainability (especially considering they pulled in a sweet total $373k in positive FCF for FY2025).

• Cash Management: In March 2026, the company pulled off a savvy financial move by renegotiating an insider loan, slashing the interest rate in half (from 9% to 4.5%) and pushing the due date out to 2029 to keep cash in the bank.

Ready for the full backbone of the story? Catch our complete long-form dive into Aurora's 2026 outlook right here:

Now onto the…

Major Update

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By now, I’m sure you know that the stock market, at least in its short-term moves, is pretty rigged. You can easily check the volumes to understand the price fluctuations.

As shareholders in several companies ourselves, this saddens us deeply.

But reality is what it is. We cannot escape the truth, but we can choose the games we participate in.

That is why I choose not to play the game of SpaceX. Let me explain exactly why.

Quick pause before we move on. If you want to join our community of investors, this is your chance.

Subscribe now

A few days ago, the single biggest initial public offering in human history officially hit the stock market. Space Exploration Technologies, ticker symbol “$SPCX” made its public debut on the NASDAQ, raising a jaw-dropping $85 billion in a single morning.

To put that into perspective, the previous all-time record holder, Saudi Aramco, had to pump oil for 80 years and possessed $20 trillion in proven reserves before orchestrating its legendary listing.

SpaceX achieved this while bleeding billions in cash.

If you ask the financial media, they’ll tell you this is the corporate event of the decade. They’ll show you cinematic footage of booster rockets landing vertically on ocean platforms and talk about the democratization of space travel.

But if you actually sit down and read through all 300 pages of the SEC prospectus (which we did), a completely different story starts to emerge.

That’s a story where the rules of Wall Street were bent at the last minute, where the physics of the universe are fighting the valuation, and where millions of everyday people are about to find out they bought into this hyper-speculative asset without ever hitting the “buy” button.

This isn’t just a story about rockets. This is the structural anatomy of a trillion-dollar corporate trap.

Breakfast. Bending the Listing Rules

Let’s start with Svetlana (I know a very Slavic name, you’re welcome).

Svetlana doesn’t really pick individual stocks. She doesn’t spend her weekends analyzing dual-class share structures or operating margins. She simply contributes a few hundred dollars every single month to her retirement account, trusting a passive index fund like the QQQ to grow her savings safely.

Historically, Svetlana was safe from sudden, speculative IPO manias. Under long-standing Wall Street rules, a newly listed company had to undergo a mandatory three-month “seasoning period” before it could even be considered for inclusion in the premier NASDAQ 100 index.

That buffer exists for a vital reason: it gives the free market time to digest the initial hype (and manipulation), look at the actual balance sheet, and figure out what the company is truly worth away from the cult of personality surrounding its founder.

But SpaceX and its executive team didn’t want to wait three months. They wanted trillions in passive institutional capital flowing into their stock immediately.

Because the NASDAQ and the New York Stock Exchange were locked in a desperate, behind-the-scenes bidding war to land the historic listing, SpaceX held all the leverage. Missing out on the largest IPO in history would mean a lot of exchange executives getting fired. So, NASDAQ gave in first.

Effective May 1, 2026, NASDAQ quietly introduced a brand-new corporate loophole called the “Fast Entry Requirement”. Under these amended rules, an extraordinarily large company can bypass the three-month seasoning period entirely and gain entry into the elite NASDAQ 100 within just 15 trading days after its IPO.

Lunch. The Forced Buyer Loophole

Think about the terrifying downstream implications of this rule change.

The NASDAQ 100 is more than just a list of names. For starters, it is tracked by hundreds, maybe thousands, of massive investment products globally, including the QQQ ETF, which commands over $600bn in assets under management alone.

When a stock is fast-tracked into that index, every single fund manager, pension protocol, and automated ETF tracking the index is legally obligated to buy that stock.

Keyword here: Required.

Our take: SpaceX essentially bullied a major stock exchange into rewriting its regulatory handbook. By forcing fast-track index inclusion, they created an artificial, multi-billion-dollar demand floor.

They didn’t need to convince seasoned asset managers that their valuation made sense; they just needed to trigger the automatic purchase algorithms built into the global passive investing framework.

A move equal parts smart and scammy.

Dinner. The Retail Allocation Red Flag

This brings us to THE anomaly of this listing: retail investor allocation.

In a typical, high-quality corporate IPO, institutional giants (sovereign wealth funds, massive endowments, and pension funds) fight tooth and nail to grab every single share available.

They want to buy (and, allegedly, hold) these cash-generating assets forever. As a result, everyday retail investors are usually left with a tiny fraction of the offering, typically sub 5%.

They, however, flipped the chessboard.

They reserved an unprecedented 30% of their total issued stock specifically for retail investors.

On the surface, the business media framed this as a magnificent act of financial democratization. I hate that word. Got to love a populist gift from the tech-emperor himself.

But if you look at it through a cold, analytical lens, it screams a warning of weak institutional demand.

For a company seeking a multi-trillion-dollar valuation, a measly 2x non-binding oversubscription rate is a massive flashing red light.

It indicates that smart, institutional capital refused to fully back the listing price, forcing the underwriting investment banks to launch aggressive digital advertising campaigns and rely on retail brokerages to absorb a staggering $22 billion chunk of the offering.

What’s the phrase? When something’s free, you're the exit liquidity… or something like that.

Regular retail investors aren’t being invited to the Alex Hormozi wealth-generation party.

They are being used as an exit strategy and insurance for early insiders who negotiated unique governance clauses that allow them to sell off their equity far faster than traditional market norms permit.

Part #2. Rollercoaster-ing

If you want to understand just how fragile the hype driving this entire market is, you only have to look at what happened during the first few days of trading.

When SpaceX first went public at that pre-set price of $135 a share, it triggered an absolute retail feeding frenzy. Within just four days of hitting the market, the stock went vertical, soaring all the way to a peak of $225.64 per share.

At that exact moment, on paper, this loss-making aerospace company was temporarily worth more than both Microsoft and Amazon.

Let that sink in.

A business that is actively bleeding cash was being valued higher than the foundational infrastructure of the modern internet.

But reality has a way of catching up to hype. Almost immediately after hitting those dizzying highs, the momentum broke. The stock reversed course and plunged for three consecutive days, culminating in a brutal 16% single-day crash on Monday.

These swings are… normal. But then again, maybe not so much for something that will equal roughly 4% of the entire U.S. total market index (unadjusted for free float).

The $20Bn Debt Trap

So, what caused the floor to fall out from underneath the world’s most celebrated IPO?

It wasn’t a rocket explosion or a satellite failure. It was a filing that exposed the massive financial strain hidden beneath the surface.

On the morning of the crash, SpaceX blindsided the market by announcing a massive $20bn bond offering.

The stated goal of this massive debt raise?

To pay off an outstanding bridge loan the company used to fund its recent acquisition of xAI, Elon Musk’s chatbot startup.

Now, to an amateur investor, refining short-term bridge debt into long-term investment-grade bonds sounds like standard corporate plumbing. But to seasoned analysts, this announcement triggered alarm bells.

BS (Balance Sheet) Paradox: According to its pre-IPO marketing, SpaceX claimed to be sitting on a war chest of over $100bn in cash on its balance sheet.

If the company is genuinely flushed with that much liquidity, why on earth are they rushing to capital markets to raise $20 billion in high-interest corporate debt just days after a historic equity raise?

The answer points to the fact that SpaceX’s actual day-to-day capital needs are astronomically higher than investors were led to believe.

The Massive Cash Burn Rate

When you look past the cinematic launch videos, the underlying numbers are a financial black hole. In its own prospectus, SpaceX explicitly warns investors: “We have a history of net losses and may not achieve profitability in the future.”

The sheer velocity at which this machine burns through capital is staggering:

• The company lost $4.6bn in 2023.

• It lost another $4.9bn in 2025.

• In just the first three months of 2026, it posted a net loss of $4.3bn.

That means SpaceX is burning through money at a rate of $2m every single hour, day, and night. In total, the company has accumulated over $41 bn in losses so far.

I know what you’re thinking…

What a fantastic tax asset investment.

Perhaps not.

Seriously though, the overwhelming retail base that bought in at $200-plus a share is beginning to realize that Starlink (satellite internet arm that generates 70% of the company’s revenue) is carrying the financial weight of an incredibly expensive, multi-headed corporate monster.

What are you actually paying for?

Part #3. Space Data Centers

How exactly do you justify an $1.8tn market cap for a business that loses $2m every single hour? The answer is simple: you don’t build a valuation model based on what the company is doing today. Instead, you sell an absolute blockbuster dream about where the entire global economy is going.

According to the SpaceX S-1 filing, the company claims its ultimate total addressable market is a staggering $28tn.

No biggie… It’s only the size of the entire US economy.

And an overwhelming 93% of that figure, or $26.5tn, rides completely on one sector: Artificial Intelligence.

The pitch Elon Musk is making to Wall Street is beautiful in its simplicity. On Earth, AI is running headfirst into a catastrophic physical wall: land, power, and water. A single, large-scale AI data center devours as much electricity as 100,000 normal households.

Worse, to keep those servers from melting, evaporative cooling systems must swallow between 5,000-9,000 gallons (multiply by 4x for liters) of fresh water every single day for every single megawatt of power.

Because of this, data centers are actively draining local grids, driving consumer electricity bills up across America, and drying out water tables.

SpaceX’s solution?

Move the data centers into orbit. In space, solar panels trap sunlight 40% more effectively than they do on Earth because there are no clouds, no weather, and no atmosphere to get in the way. It sounds like a sci-fi masterstroke: infinite free energy, zero terrestrial water footprint, and unlimited space.

But this is where the laws of physics collide violently with corporate marketing.

Cosmic Thermodynamics Oopsie

Every investor piling into this IPO because of the AI hype is ignoring an elementary school science reality: Space is a vacuum, and a vacuum is a perfect insulator.

Think about a standard thermos. It keeps your coffee boiling hot for hours by utilizing a double-walled vacuum layer that prevents heat from escaping. An orbital data center is facing that exact same trap… on a planetary scale.

On Earth, computers stay cool because heat can hitch a ride on moving air or water molecules via conduction and convection. But in the empty void of space, there are no molecules. The only way for heat to escape a satellite is to radiate away as infrared light.

To make that happen, you need massive, hyper-expensive cooling structures called radiators. And the engineering math on this is absolutely brutal:

• The International Space Station produces a modest 70 kilowatts of heat, yet its thermal radiator arrays are massive, weighing a whopping 7,000 kilograms (7 tons).

• Scale that up to a commercial, 1-megawatt AI data center cluster. To cool just 10 tons of advanced AI processors, you are forced to launch 100 tons of dead-weight radiator panels into orbit with it.

The Starship Bottleneck

This extreme weight penalty brings us directly to the single most critical number in this entire financial equation: $200 per kg.

Right now, utilizing a standard Falcon 9 rocket, launching a payload into orbit costs roughly $2,700 per kilogram. At that rate, the astronomical weight of the required radiator systems pushes the cost of building a space data center to over $32 million per megawatt.

Compared to the $12 million to $15 million it costs to build on land, the space data center is completely dead on arrival.

The entire trillion-dollar valuation of SpaceX hinges on the unproven promise that their new, gargantuan rocket system (aka Mr. Starship) can successfully drop that launch cost down by 93%, hitting that magical $200 per kilogram threshold.

Note: I won’t go on about that idiot index here, but you should totally google it.

But as tech and aerospace analysts point out, Starship is still an experimental vehicle currently grinding through a highly volatile flight test campaign. To unlock those rock-bottom prices, SpaceX has to achieve a series of engineering milestones that have never been pulled off in human history:

1. They have to achieve 100% rapid reusability of the upper Starship spacecraft, catching it mid-air using giant launch tower mechanics.

2. They must master automated, orbital cryogenic refueling, meaning they have to pull off high-stakes, super-chilled propellant transfers in the middle of a vacuum just to get these massive payloads out of low Earth orbit.

If SpaceX misses these targets by even a fraction, the cost of space launch stays high, the orbital data center dream falls apart, and $26.5tn of AI addressable market vanishes into thin air.

Part #4. Mars… the Broken Promise

To understand the true cost of this IPO, you have to look past the financial engineering and look at the cultural soul of the company itself. Because for more than a decade, Elon Musk sold a grand, civilizational mission.

And he gave one specific, unwavering promise to his employees and followers: SpaceX would never go public until human survival on the Red Planet was guaranteed.

Back in a famously leaked 2013 internal email, Musk explicitly warned his team about the dangers of Wall Street. He wrote:

“Creating the technology needed to establish life on Mars is and always has been the fundamental goal of SpaceX... If being a public company diminishes that likelihood, then we should not do so until Mars is secure.”

For years, that single-minded focus was SpaceX’s greatest superpower. It allowed them to ignore quarterly earnings reports, out-engineer massive government contractors, and attract the absolute best aerospace minds on the planet.

But as the 2026 prospectus makes glaringly obvious.

Instead of waiting for Mars to be secure, SpaceX engineered a corporate pivot. They swallowed xAI whole, grabbed AI coding tools like Cursor, and repositioned themselves to catch the trending AI venture capital wave.

The dream of making humanity multi-planetary has been effectively traded in for the immediate satisfaction of an $85 billion stock market payday.

The $7.5tn Joke

If you want to know how the company’s new Wall Street backers view the Mars mission, you only have to look at how they structured Elon Musk’s new compensation package buried deep inside the S-1 filing.

According to the legal prospectus, Musk is slated to receive a historically unprecedented $7.5tn pay package.

Note: he only unlocks the full amount if SpaceX successfully establishes “a permanent human colony on Mars with at least one million inhabitants.”

Now, to a casual fan, that looks like ultimate alignment with the core mission. But corporate finance experts aren’t buying the hype.

Jay Ritter, a prominent finance professor at the University of Florida who directs the IPO Initiative market research program, flatly told the media: “These are just jokes. I can’t think of any other example.”

While including a literal punchline inside a legally binding SEC financial prospectus is wild, it serves a deeply calculated corporate purpose.

It acts as a clever marketing smoke screen, a bit of narrative nostalgia to assure the public that the old “North Star” still exists, even while the actual cash flows are being aggressively diverted into terrestrial AI infrastructure and corporate debt management.

The Fanbase Fracture: Space Purists vs. Stock Bros

This radical shift from deep-space exploration to speculative financial engineering has triggered a massive identity crisis within the SpaceX community.

Online forums and space subreddits, which spent the last 15 years obsessively tracking every minor engineering update to rocket heat shields and engine nozzles, have devolved into absolute battlefields.

The community has fractured into two bitter, opposing camps:

• The Space Exploration Purists: Hardcore space advocates who feel fundamentally betrayed, watching their beloved engineering powerhouse get distracted by the next shiny Silicon Valley tech trend.

• The Meme-Loving Stock Bros: A new wave of speculative retail investors who couldn’t care less about Martian atmosphere physics and are purely here to flip shares for quick post-IPO profits.

The despair among the old guard is tangible. One viral post on a prominent SpaceX subreddit, which quickly amassed over a thousand upvotes, captured the mood perfectly. It featured a classic four-stage meme of a person slowly applying full clown makeup.

The caption read simply: “My journey as a SpaceX fan the last 15 years.”

The Lunar Pivot

If you think the internet forums are overreacting, you only have to look at Elon Musk’s own public statements. In a telling post on X, Musk openly admitted that the company’s near-term operational focus has been quietly stripped away from Mars and locked onto something much closer to home. Musk wrote…

This sudden shift in priorities isn’t a natural evolutionary step; it’s a response to intense political and financial pressure. Back in 2021, SpaceX secured a crucial $2.9bn lunar lander contract with NASA.

As the global space race heats up with China aggressively targeting the lunar south pole, Washington has been publicly turning up the heat on SpaceX to pick up the pace and deliver on its hardware promises.

SpaceX simply cannot afford to alienate its biggest government client while burning $2m an hour on an unproven AI play.

So, Mars gets pushed to the back burner, the Moon becomes the immediate target, and the ultimate vision of making humanity a multi-planetary species gets indefinitely delayed by the harsh realities of public market expectations, geopolitical competition, and corporate survival.

Conclusion. Siphoning in Space

So, what is the ultimate truth of the SpaceX IPO?

If you look past the flag-waving corporate cheerleading and the breathless media headlines, you arrive at a reality that is as brilliant as it is terrifying. SpaceX is no longer a space exploration company. It hasn’t been for a long time.

Today, SpaceX is a giant, trillion-dollar financial siphon designed to do two things: transfer structural investment risk away from early venture capitalists and onto the public balance sheet, and use a profitable satellite monopoly to keep Elon Musk’s bleeding AI dreams on life support.

The most controversial part of this entire machine? You don’t even have a choice.

But by forcing the NASDAQ to rewrite its handbook, SpaceX successfully tapped into the global passive investing framework.

Whether you are a hardcore space enthusiast or a conservative retiree who thinks colonizing Mars is a childish delusion, your hard-earned money is now directly weaponized to bankroll Elon Musk’s corporate empire.

The Ultimate Venture Capital Dump

This isn’t the democratization of finance. It is the institutionalization of a meme stock.

When early insiders and institutional giants realize a capital-intensive project is getting too expensive to fund privately, they look for an exit door. Historically, they had to wait for a company to prove its profitability. But in 2026, all you need is a massive cult of personality, a 300-page prospectus filled with unproven sci-fi initiatives, and an exchange willing to fold under pressure.

SpaceX may very well succeed in landing hardware on the Moon, and they might even find a way to make space data centers a viable business down the line. But as the stock’s immediate, violent post-IPO whiplash proves, the market is beginning to realize that peak hype has a brutal expiration date.

If Starship hits that magical $200 per kilogram target, this will be remembered as the greatest corporate gamble ever won.

But if it misses, if the physics wins and the AI bubble bursts, millions of everyday savers like Svetlana are going to wake up and realize they were forced to pay for a set lunch menu they never ordered, holding an empty bag while the techno-emperor keeps the stars.

The chaos of the SpaceX IPO is officially open. Watch your pockets.

If you found value in this analysis, please hit the ❤️ button and share your thoughts in the comments. It helps us bring institutional-grade research to a wider audience.

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As always, we’re trying to improve the formatting for you guys. Let us know in the comments if you like this new infographic and chart layout (below)!

As we now do every week for transparency and to provide full value, here is our list of Pick of the Week posts and their returns since publishing:

• Equally weighted picks: +14.00%

• Fund-allocated picks: +38.30%

Pick of the Week. A curated series of high-conviction research on companies currently under our microscope. We screen for specific dislocations where the market has mispriced the balance sheet or earnings power.

The Selection Criteria:

• Asset Arbitrage: Trading at a discount to tangible liquidation value.

• Backlog Disconnect: Future contracted revenue ignored by the market.

• Hidden Margins: Structural profitability masked by temporary noise or CAPEX cycles.

The Structure:

• The Business: A concise operational overview.

• The Dislocation: The specific structural reason the opportunity exists.

• The Valuation: A stress-tested snapshot including downside risks (”Red Flags”) and our proprietary score.

None of the following should be construed as investment advice. Please consult a financial advisor before making any investment decision. You will find a full disclaimer at the end of this post.

🧮 BlackLine, Inc. ($BL)

Ever wonder who’s actually destroying all of those corporate IT management, consulting, and auditing jobs over at Accenture?

If you aren’t familiar with the space, BlackLine operates as a SaaS platform positioned right inside the office of the CFO.

While legacy ERP giants are great at logging raw daily transactions, they completely fall apart during the final mile of corporate accounting, specifically when it’s time to handle the high-stakes world of financial closing, account reconciliations, and multi-entity consolidations.

BlackLine acts as the automation-heavy orchestrator sitting on top of a company’s complex IT infrastructure, sucking in data from disjointed ERPs, banking networks, and internal spreadsheets to ensure total compliance and prevent catastrophic financial misstatements.

Momentum and quant models are aggressively dumping the stock because they are misinterpreting superficial headcounts and surface-level revenue deceleration, creating an incredible valuation disconnect where a highly sticky cash cow with an untouchable 25-year operational data moat and a built-in SAP distribution engine is being priced like a dying business.

We see this ending quite well for them, and we’re willing to bet they’ve got a rerating potential of 71%+.

In a single sentence… BlackLine is a mission-critical financial automation powerhouse with an untouchable operational data moat and a cheat-code SAP partnership, currently mispriced by a short-sighted market that is mistaking its deliberate shift toward high-value enterprise accounts for structural decay.

Unlock the Full Report

The Business Model

We’re going to examine their product suite, but more importantly, narrow down what it actually does.

We’ve filled the commentary with key terms you can Google to check directly what they mean for both BlackLine and their clients.

It’s easily the simplest and most effective way to help you understand what they do, while simultaneously describing how they’re replacing legacy software and the more manual human-hour services.

Core Infrastructure

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Again, I prepped these posts in anticipation of my interns arriving. It’s partly meant to train them, and partly to make sure they don’t break anything during days one through five.

The first post covered debt and how to evaluate bonds. This second part will dive straight into due diligence.

If you haven’t checked out part one yet, go give it a read (it’s free):

To keep things practical, I’ll be using simplified examples of actual deals I’ve analyzed and real due diligence I’ve conducted in the past, together with some live transactions I’m working on right now.

Due diligence sometimes sounds like a monumental, Herculean endeavor… like one of those vast, soul-crushing tasks that takes a lifetime to complete and requires a massive supply of coffee.

And NGL, sometimes it really is a massive, multi-headed beast. But regardless of how terrifying it looks from a distance, the secret to surviving it is simple: you just have to chop it up into smaller, bite-sized, digestible subtasks… Substack?

Wait, what?

See what I did there?

Smooth transition. Now go ahead and subscribe:

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TBH, the difference between doing proper due diligence and just winging it is the difference between a massive payday and a catastrophic, loss-making end.

Think about it like buying your first house. You wouldn’t just look at the shiny kitchen countertops and sign the papers. No, you’re going to look into every single nook and cranny. You’re checking their latest termite testing, their insurance coverage, the exact date the garage burnt down because Grandma allowed cousin Jake to handle the electrical wiring… You want to know everything.

Logically, people should do the same thing when purchasing a company (aka buying a stock), right? And yet, they completely drop the ball.

Due diligence is your operational floor; it is your ultimate financial security line.

Why the average investor refuses to extend this basic level of care to their portfolio is honestly beyond me. It’s usually a toxic, cocktail combination of pure greed and intense FOMO.

I guarantee you the average person is losing absolute heaps of money out there because their version of “deep research” looks less like a thorough investigation and more like casually checking out a ticker on Yahoo Finance for two minutes, getting a rush of adrenaline, and then immediately sprinting over to Robinhood to grab a few titles.

Don’t be that person. Treat your investments like a house you actually have to live in, or prepare to watch the roof cave in!

#1. Desk Research & Hard Financials

Before you even think about putting on your detective hat and stepping out into the wild, you have to survive Phase I.

This is the absolute foundation of your valuation journey, where you anchor your initial numbers and see exactly what this company looks like when it’s trying to behave on paper.

This is the mythical land of the Excel Warrior.

You are essentially locked in a room with a mountain of spreadsheets, tasked with staring down historical balance sheets, income statements, and cash flows until they start telling you the absolute truth. It’s a deep dive into the corporate anatomy to see where the cash actually flows, where it gets trapped, and whether the company’s past matches the beautiful story management is trying to sell you.

Quality of Earnings and Other Adjustments

Once you’ve cataloged the raw data, it’s time to start scrubbing the corporate makeup off the face of the financials with an aggressive Quality of Earnings (QofE) review. This is where you play financial myth-buster: you pull apart the numbers to normalize EBITDA, audit sketchy revenue recognition policies, and map out the net debt and working capital bridges.

Scan the Income Statement and footnotes for “Non-Recurring Expenses,” “Restructuring Charges,” or “Other Operating Income.” Pull these out and recalculate a truly Normalized (real) EBITDA to see how the core, everyday business actually performs without the helpful accounting cosmetics.

Your entire goal here is to strip away the one-off accounting tricks, creative adjustments, and management’s overly optimistic spin to ensure the baseline numbers are completely real before you build anything on top of them. You are checking for luck, but also checking for cyclicality, seasonality, and mistakes.

Here’s a list of useful tasks of a similar nature you should perform:

• Our Cash Flow Lie Detector Test: Calculate the Cash Conversion Ratio by dividing Operating Cash Flow by EBITDA over a three-to-five-year period. If EBITDA is growing beautifully but OCF is flat or shrinking, management is recording profits that aren’t actually turning into cold, hard cash.

• Revenue Recognition and Reconciliation: Compare the quarter-over-quarter growth rate of Accounts Receivable (A/R) against the growth rate of Total Revenue. If A/R is skyrocketing significantly faster than sales, it’s a massive red flag that they might be stuffing the channel by booking aggressive uncollected revenue at the end of the year to hit their targets.

• Working Capital: Calculate Days Sales Outstanding (DSO), Days Inventory Outstanding (DIO), and Days Payable Outstanding (DPO) to map out the exact Cash Conversion Cycle. This shows you exactly where the cash is getting trapped, whether it’s tied up in dust-gathering inventory or slow-paying customers.

• CAPEX vs. D&A: Track Capital Expenditures (Capex) relative to Depreciation & Amortization (D&A) over time. If Capex is consistently lower than D&A, the company might be underinvesting in its physical assets to artificially boost short-term free cash flow, leaving a massive operational bill for you to pay later.

• Footnote Sherlock Holmes: Comb through the audit notes specifically looking for changes in accounting policies, shifts in inventory valuation methods (e.g., LIFO to FIFO), or sudden adjustments to useful asset life assumptions. These subtle tweaks are the classic way companies stealthily manipulate their earnings per share.

Preliminary Valuation

Note: if you jump straight into the valuation model without actually understanding the underlying numbers, you’re just building a house on quicksand.

This holds double-true if you’re feeding data into your favorite AI and asking it to do the heavy lifting for you. Especially with smaller companies, AI models have a nasty habit of hallucinating or outputting absolute nonsense if the inputs are messy.

It’s the golden rule of data: garbage in, garbage out.

So, do yourself a favor and comb through those financial statements yourself first. You need to be the one to spot the traps before you let the machine try to solve the puzzle.

Once you’ve beaten the historical data into an honest state, you get to play architect and lay down the initial valuation model framework.

Using management’s baseline assumptions as a starting point (while keeping a very skeptical eyebrow raised), you can build out your core DCF models, comparable trading multiples, or asset-based valuation sheets.

• Discounted Cash Flow (DCF) Models: Imagine a magical machine that spits out a $100 bill every single year. How much would you pay to buy that machine today? A DCF calculates the answer by looking at all the cash a company is expected to make in the future and translates it into today’s dollars. Because a dollar tomorrow is worth less than a dollar today, a DCF discounts those future cash flows to figure out what the whole business is worth right now.

• Comparable or Precedent Trading Multiples: If you want to sell your house, you look at what similar houses in your neighborhood recently sold for. Multiples do the same thing for businesses. You look at similar, publicly traded companies or recent business buyouts in the same industry. If competitors are selling for 10 times their annual profit, you multiply your target company’s profit by 10 to get a ballpark value.

• Asset-Based Valuation Sheets: Imagine a business completely fails, goes bankrupt, and has to shut its doors tomorrow. What’s left? This method ignores future profits and simply adds up the value of everything the company physically owns (e.g., factories, real estate, inventory, and equipment) and subtracts any debts they owe. It’s essentially calculating the liquidation value, sometimes even at fire-sale prices.

This isn’t the final, bulletproof version of your thesis, but rather a digital sandbox where you map out how the business is supposed to work based on the hard numbers you’ve just validated.

You’re setting the baseline math reality so that when you finally step away from the screen to do some real-world investigating, you’ll know exactly which assumptions need to be pressure-tested.

#2. Commercial Validation

The goal here is to take management’s beautiful Excel-based dreams and violently smash them against market realities to see if the deal thesis actually holds water, or if it’s just a fragile house of cards waiting to collapse at the first sign of competition.

You kick things off by putting the market’s Total Addressable Market (TAM) and the company’s unit economics on the chopping block. It’s time to verify if the market size and growth runways are actually as vast as advertised, or if management is hallucinating their growth potential.

Now, we don’t recommend you go completely ham on this end and get bogged down in endless macro-forecasting; just do enough to ensure that management isn’t selling you a total pipedream.

You dig deep into the gritty operational metrics, auditing Customer Acquisition Costs (CAC), Customer Lifetime Value (LTV), true pricing power, and customer churn. You’re essentially checking to see if they are buying revenue at a loss or if they have a sticky, loyal customer base that will actually stick around.

Unit economics like these are crucial, and most of the time, they provide the first real hints about the company's true economic moat or competitive advantages.

Regulatory Review

Next, you dive headfirst into the regulatory and compliance jungle, mapping out the invisible tripwires that can instantly kill a deal.

You are screening everything from supply chain routing and payment flows to transaction counterparties, looking out for any hidden circumvention risks that might invite a regulatory hammer down the line.

Whether you are auditing specific regional compliance frameworks, like navigating complex EU-specific ESG mandates, labor standards, or legal/tax structures, you are ensuring the company isn’t accidentally importing a massive, ticking legal liability.

You want to make sure their supply chain is completely clean and that their cost-effective suppliers in Vietnam aren’t operating in a global penalty box.

Test it out

Finally, you take all these messy macro and regulatory realities back to your digital sandbox for a brutal round of scenario modeling and stress-testing.

You take that pristine baseline model from Phase I and intentionally try to break it. You map out aggressive, base, and absolute nightmare-fuel worst-case scenarios, simulating what happens to the valuation if inflation spikes, a critical supplier goes bust, a new competitor triggers a price war, or a major regulatory shift slashes their margins.

By the time you finish this phase, you’ll have a battle-tested framework ready for the ultimate test: going out into the field to gather real-world intelligence.

#3. The Scuttlebutt Layer

What we’re all here for… our beloved Scuttlebutt.

This is where we throw the spreadsheets out the window, leave the comfort of our climate-controlled offices, and step directly into the wild to find out what is actually happening on the ground.

You are looking for the unvarnished truth that never makes it into an investor relations PPT or news release, ensuring your valuation is backed by operational reality rather than corporate fiction.

Note: We usually talk to a large number of external players to help us validate information. This includes the likes of suppliers, clients, industry experts, ex-employees, and many others. It’s actually only at the very end of our research cycle that we reach out to discuss things directly with management.

However, we want to streamline things for you here by giving you a simplified approach that you can easily pull off as an individual investor, especially if you push through and leverage your local edge (visit local companies).

Going through Management

One of the key targets is the management team. However, instead of sitting through another rehearsed, glossy slide deck, you are looking for raw, unscripted human behavior.

You kick things off with corporate culture and management assessment, treating every interaction as an amateur psychology study to evaluate executive alignment, genuine integrity, and operational execution style. You don’t just want to hear the CEO’s grand vision; you want to look at how they react when things go off-script.

Pro tips incoming:

• Ask about their first jobs.

• Ask about their latest interactions with low- and mid-level employees.

• Ask about their failed products.

• Ask about decision-making in tough times (e.g., COVID).

• Ask about the competitor they hate the most.

• You can even go personal and ask about their family, their kids, or the values they grew up with.

But most importantly, always end every single questioning line with a “Why?”

Know that you are quietly taking the temperature of middle management and tracking organizational turnover rates, because if the talented people who actually run the day-to-day operations are sprinting for the exits, that beautiful margin expansion you modeled in Phase I is nothing but a fantasy.

On-Site Visit

Sometimes, especially for manufacturing-based companies, you must put on your steel-toed boots and head straight into the operational value chain for some mandatory on-site inspections.

You are doing physical asset walks and factory floor tours, checking capacity utilization with your own eyes and watching inventory management to see if the physical reality matches the capital expenditure projections on the balance sheet.

While you’re there, you practice the fine art of chatting up frontline workers, floor staff, and local engineers. These are the people who actually know which machines are constantly breaking down, where the real bottlenecks live, and whether safety standards are a priority or just a poster on the wall.

All of these insights will spot operational risks months before they ever show up as a line item on an income statement… plus, it really helps develop and expand your circle of competence.

What better way to learn than by asking a niche specialist with a ton of experience directly about it?

Note: Right now, we’re conducting due diligence on an elderly care facility.

Checking the building, the gardens, the town, and the rooms (plus chatting with staff) is absolutely essential in this case.

There is no subsitute for your gut feeling on-site.

Well-rounded Intelligence

To build a 360-degree intelligence, you must zoom out and interview the entire ecosystem surrounding the business.

Pro tip: Don't just do a single interview per group, aim for a few instead. The wisdom of crowds and the law of averages will give you a much more complete picture.

Because we want to make it concise we’d suggest interviewing customers to find out if they actually love the product or if they are secretly planning to jump ship the moment a cheaper alternative arrives. This may be easier than you’d think especially for B2C businesses.

You can always like a Coke consumer if they like New Coke.

You can also call up suppliers to verify payment terms and supply chain stability, and you can talk directly or shadow competitors to see if the company’s supposed “pricing power” survives out in the open market.

If you can, you should also hunt down ex-employees for a little post-mortem debrief, because nothing illuminates hidden cultural toxicity, operational vulnerabilities, or structural flaws quite like a candid conversation with someone who no longer relies on the company for a paycheck.

Note: On this last point, make sure those former employees aren't just disgruntled or incompetent, as they'll give you a pretty misleading picture.

#4. Synthesis, Calibration & Pricing

It’s time to bridge the gap between what management said was happening and what you know is happening, turning our assumptions until now into a bulletproof valuation to make a decision.

For starters, you should go through a brutal round of assumption calibration. Take that digital sandbox model you built in Phase I and start hacking away at it with your newly acquired Scuttlebutt scalpel.

Did that competitor interview reveal that the company’s moat is actually a shallow puddle?

Down go the terminal growth rates.

Did the factory floor walk reveal a bottleneck that will require a massive tech upgrade?

Say goodbye to your optimistic margin expansion expectations and crank up the projected Capex.

You are adjusting the cost-of-capital parameters and growth levers based on what is actually happening in the real world, ensuring your model reflects physical truth rather than corporate hope.

By the end of this process, you should be able to adjust the finer details of your assumptions. Do note that this is more a matter of adjusting within a range of accurate assumptions than of pinpointing a precise valuation.

If you are spending too much time there, it’s probably a big fat NO.

The disconnect between value and price should be obvious once you make approximately accurate assumptions and stakeholders confirm these.

Margin of Safety

Next, you apply the ultimate investor shield. This is a calculated buffer built directly from the operational, legal, and cultural red flags you unearthed during your due diligence.

If the ex-employees warned you about a toxic middle-management culture or the suppliers hinted at upcoming pricing pressures, those risks get priced right into the final valuation range.

You are intentionally building a financial shock absorber into your target price, ensuring that even if the business hits a few hidden potholes post-acquisition, your investment returns won’t go flying through the windshield.

We usually apply a 50% discount to the valuation your model outputs, and that should be your target entry price.

SPA Drafting (if Private)

Note: This applies mostly to private deals, not so much to buying a regular stock in the market, though it could definitely apply to stock purchases using derivatives.

With your battle-tested valuation range in hand, you step up to the negotiation table for final deal structuring. This is where you determine how that price is paid.

If your Scuttlebutt investigations revealed some lingering uncertainties around customer churn or regulatory shifts, you don’t just walk away; instead, you get creative.

Pro tip: This is going to sound super fancy. We’re confident we could do an entire lecture series on these, but for now, we’ll just provide a quick overview and the key items to look at.

You map out clever earn-out structures, clawback provisions, and strict corporate governance protections based on your unified findings. By the time you sign on the dotted line, you haven’t just bought a company based on a spreadsheet; you’ve structured a deal that perfectly aligns the final price with the ground truth.

But how do you actually lock these terms and conditions down so the seller can’t wiggle out of them? Enter the Share Purchase Agreement (SPA).

You can visualize this doc by thinking of a prenuptial agreement for corporate acquisitions. It’s there to ensure that what you thought you bought is exactly what shows up at your door.

Core components of a battle-tested SPA include:

• The Price Tag & Payment Mechanics: This is where you outline exactly how and when the cash moves. You don’t just write a single check; you bake in those Scuttlebutt-inspired earn-outs (making the seller earn their payout based on future performance) or deferred payments to keep them cooperative post-closing. You also establish whether you’re using a “Locked Box” mechanism (fixing the price on a past balance sheet date) or “Closing Accounts” (adjusting the final price on the exact day you take the keys based on working capital).

• Representations & Warranties (R&Ws): This is the seller playing a high-stakes game of “truth or dare” under oath. You force them to make definitive statements of fact about the state of the business—swearing that the taxes are paid, the intellectual property is theirs, and no major clients are secretly planning to dump them next month. If they lie, your R&Ws are your legal ticket to claw back your money.

• Indemnities (The “You Broke It, You Fix It” Clauses): While warranties are general promises, indemnities are your laser-targeted, heat-seeking missiles. If your due diligence uncovered a specific, ticking time bomb—like an unresolved legal dispute or a sketchy tax position—an indemnity forces the seller to cover the bill dollar-for-dollar if that bomb explodes post-acquisition.

• Conditions Precedent (CPs): The ultimate deal-stoppers. This is the checklist of absolute prerequisites that must happen before a single dollar changes hands. If the company fails to secure a critical regulatory green light, or if a vital supplier refuses to sign off on the ownership transition, the CPs allow you to walk away with your capital completely intact.

• Restrictive Covenants: The classic “don’t copy my homework” clause. You want to make sure the former owner doesn’t take your hard-earned cash on Friday and open up an identical competing shop right across the street on Monday. This legally bars them from competing with you or poaching your newly acquired rockstar employees for a designated number of years.

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Most times, this is answered by one simple factor which de-risks every investment: price.

Today’s edition is another one of those underserved infrastructure builders that effectively acts as a toll bridge to both industrial development and the (now not-so-popular) green transition.

We think it’s a simple and efficient way to, through a German player with 18% of its market cap in (net) cash, finance and operate the main bloodline of businesses in the country.

It comes with a 32.2% ROIC, a consistent 5-year 9.9% EBIT margin (17.3% in the LTM), while trading at 6.45x FCF, offering a subtle 2% dividend yield, and boasting an even more subtle 0% dilution since 2021.

As we now do every week for transparency and to provide full value, here is our list of Pick of the Week posts and their returns since publishing:

• Equally weighted picks: +14.15%

• Fund-allocated picks: +42.11%

At The Hermit, we hunt for unloved, mispriced small- and micro-caps that institutional screeners miss because of dirty data, and this asset-light, cash-generative engine is completely disguised as a sleepy legacy market anomaly.

If you're ready to elevate your career and ride along with a rigorous, primary-data-driven research process…

Become a Member

Start benefiting from our takes and verify our institutional frameworks as we execute.

Pick of the Week. A curated series of high-conviction research on companies currently under our microscope. We screen for specific dislocations where the market has mispriced the balance sheet or earnings power.

The Selection Criteria:

• Asset Arbitrage: Trading at a discount to tangible liquidation value.

• Backlog Disconnect: Future contracted revenue ignored by the market.

• Hidden Margins: Structural profitability masked by temporary noise or CAPEX cycles.

The Structure:

• The Business: A concise operational overview.

• The Dislocation: The specific structural reason the opportunity exists.

• The Valuation: A stress-tested snapshot including downside risks (”Red Flags”) and our proprietary score.

None of the following should be construed as investment advice. Please consult a financial advisor before making any investment decision. You will find a full disclaimer at the end of this post.

Read more

I actually prepped these posts in anticipation of my new interns arriving next week, partly to train them, and partly to make sure they don’t break anything on day one. These two topics are the core fundamentals that explain pretty much everything I do.

1. This first post will cover debt and how to evaluate bonds.

2. The second will dive into due diligence.

Specifically, how to conduct a thorough, deep-dive forensic investigation of a business.

To keep things practical, I’ll be using simplified examples of actual deals I’ve analyzed and real due diligence I’ve conducted in the past (as well as live transactions I’m working on right now).

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Fascinatingly, surviving both bonds and acquisitions usually comes down to mastering a single, definitive document.

For bonds, that document is the prospectus. The bond prospectus essentially outlines the covenants, the legally applicable rules of the game.

These are usually massive, soul-crushing mountains of legalese that no sane person wants to read. But if you’re putting real money to work, you absolutely must read them.

Why?

Because buried in that fine print is every single way the issuer can legally screw you over. You want to be very, very aware of what’s hiding in those pages.

When it comes to due diligence on an acquisition, the holy grail document is the Share Purchase Agreement (the SPA).

The SPA is where deals go to live or die. No matter how brilliant your due diligence is, there will always be missing pieces or weird day-to-day operational quirks that you won’t truly grasp until you actually own the keys to the kingdom.

Therefore, drafting the legal conditions to handle those unexpected ‘unknown unknowns’ is the most critical part of the entire process. It’s a two-front war: not only are you studying the business meticulously so the “worst-case scenario” stays as small as humanly possible, but you are also using the contract to build a fortress for when the unexpected inevitably happens.

My name is Bondo, Jamesu Bondo

So going back to this post: bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds, bonds...

Can’t live with them, can’t live with them.

Bonds sort of explain the returns on everything we see in the financial markets.

On the one hand, highly popularized equities are sort of in vogue right now. But back in the day, say until the 50s or 60s, bonds were essentially everything that was transacted. Back then, everything else was considered way too risky to invest in.

That is, until a guy called Michael Milken, came over and unlocked the lower tiered bonds and essentially equities. He ended up cheating though, so he’s probably not the example of how to invest.

With that in mind, the same basic concept, applicable to bonds, applies to modern instruments. So once you understand it, you pretty much understand investing.

Debt. Feeding the Capitalist Machine

Bonds are the stable bedrock of the financial system. If the world has, let’s say, $100 trillion worth of equities (probably way more than that), you probably have 3-5 times that amount in debt for those same companies plus asset-backed private debt and non-backed business debt (not to mention derivatives). Think about everything including mortgages, bank loans for small businesses… stuff like that.

Debt is what helps everything run smoothly and, to be honest, accelerates projects. It makes things possible for people. You can think of it as the ultimate form of crowd-sourcing of resources.

Even when a venture capital fund comes over and backs a startup, that money is usually not equity at first, it often comes in the form of convertible debt. So that flexibility is exactly how things get started.

Same thing goes for buying a house. People don’t usually have a million bucks just sitting around. But if you have 20-ish% of that amount, you can lock down the house and finance the rest. The whole point is that you get the asset and you get the ball rolling. In essence, debt is the rocket fuel for capitalism. And in that sense, it’s great.

But obviously, there is a dark side. Taking out a loan to purchase a depreciating asset or a consumable is a terrible choice. The brutal thing about debt is compounding interest. It compounds beautifully for the person lending to you, but if you’re the borrower, it compounds against you… effectively cannibalizing your future cash flows.

So depending on what you’re using it for, debt is technically ‘good’ or ‘bad’.

The same applies to the terms of the debt.

I’m sure you’ve seen people get trapped by bridge loans, or what we call Cofidis style predatory loans in Spain. Those small-time lenders will gladly hand you cash, but they’ll charge you a 30% annualized interest rate for the privilege. Same goes for credit cards charging 20% to 30%.

Taking these out is financially indefensible. Sure, people use them out of necessity, but the hurdle rate to pay them back is just insane.

NEVER FORGET THAT.

It’s one of those silent killers that will destroy you as an individual, and even faster as a corporation. By definition, that is really, really, really bad debt.

Mechanics of a Bond

Now, let’s look at the actual mechanics of institutional debt. Here, we live at the intersection of cash flows, predictability, and risk management.

When you buy a stock, you’re basically praying that the company grows, margins expand, and the market rewards them (via multiple expansion). A bond is a much more calculated, cold-blooded move. It’s essentially a hard legal contract. You are handing over capital, and that capital must be paid back to you at a specific rate, at a specific point in time, with a specific interest.

You’re going to have four major components to debt that you must understand.

Deconstructing this, first you’re going to have the (#1) face value. The face value is just the amount you lent out (also called the principal amount), and usually, bonds are issued in blocks of a thousand bucks ($1,000 per bond).

Then you have the (#2) coupon rate. The coupon rate is just the interest percentage of that face value that the issuer pays you every year.

Note: As the secondary market price of the bond changes, that fixed coupon plus alternations in price will give you a different yield. Who is giving out the bond matters immensely here.

We’ll talk more about this in the future, but it’s not the same to hold Uncle Sam’s debt as it is to hold debt for a municipal highway. Nor is it the same to hold debt from a speculative micro-cap company versus a massive blue-chip giant like Google. Corporate bonds are definitely not created equal.

But the unique thing about bonds is that they have a strict issuance date and a (#3) maturity date. Maturity is when you get your principal payment back, and issuance is just when that money first goes out.

Because that’s the case, whatever happens to market interest rates in the meantime heavily affects the trading price of these bonds.

If the Fed chairman decides that interest rates go up, any newly issued bond will have to pay a higher interest rate to attract investors. In relative terms, already existing bonds with lower coupons will immediately be valued at less money, because you’re holding a worse deal compared to the new bonds hitting the market.

With that in mind, do remember that the face value is a constant at the point of issuance and at the point of maturity. If you lent out a million bucks in blocks of a thousand, that exact money has to be given back to you at or before the end of the period. Whatever happens in between is just a product of interest rate fluctuations and market moves (speculation).

The initial value and the end value are the same. But in the meantime, you can buy or sell that bond at a discount or a premium.

A discount is when the bond sells below face value (say, $900), and a premium is when it sells above face value (say, $1,100).

Again, going back to the fundamentals, that coupon rate is fixed. If a bond is put out at 5%, it’s going to give you that fixed $50 a year over a $1,000 face value. But that fixed $50 keeps getting divided by a different market price every single day between issuance and maturity, and that math gives you your current (#4) Yield to Maturity (YTM) or yield for short.

Yield is something we talk a lot about, particularly for government bonds. It’s a shifting factor that accounts for the difference between the face value and the current market price. Let’s say a bond was issued at $1,000 and is now priced at $900. That $100 discount, combined with the regular coupon payments, will give you what is known as the yield.

Note: Let’s look at the quick math. If this 5% coupon bond has 3 years left and trades at $900, your total return comes from two places:

• You get $50 in cash every year.

• You make a $100 capital gain at maturity ($1,000 face value minus the $900 you paid). Spread over 3 years, that adds roughly $33.33 of extra value per year.

Add those up, and your average annual return is $83.33. Divide that by the average value of the trade, essentially initial and final value, ([$1000 + $900)] /2 = $950), and you get a back-of-the-envelope yield of 8.77%.

(The mathematically exact YTM using a spreadsheet is 8.92%, meaning this quick shortcut gets you within striking distance in under five seconds)

YTM is the total return of the bond, combining both capital gains (or losses) and the coupon rate paid in the meantime. When institutional fund managers talk about yield, this is exactly what they’re discussing. This is the gold standard for valuing a bond or, more importantly, understanding its true value.

Just like we use 15% FCF as a barrier for entry, you can apply the same logic here to determine a minimum hurdle.

Risky Business

Risks on these? There are obviously a few additional nuances, but I’d say the two primary risks are duration risk and default risk.

Duration risk is also known as interest rate risk. It simply measures how sensitive a bond’s price is to changes in interest rates. Even though this duration risk is technically a temporary, paper-driven thing (until you sell the bond, everything is just an unrealized gain/loss), it can completely wreck institutional balance sheets.

Take banks, for example.

Banks have a lot of debt on their books, and duration mismatching is one of their biggest hazards. If a bank’s depositors need their money back in two years, but the bank invested that cash in 10-year bonds, they have a massive mismatch.

If interest rates spike upwards in the meantime, those 10-year bonds become far less valuable. If the bank is forced to liquidate them early to meet depositor withdrawals, they realize those massive losses, cratering the equity value of the bank. This creates a vicious spinning wheel, exactly like we saw back in 2023 when regional banks collapsed due to a mismatch of what timeline they should own versus what they actually own.

Again, if you hold a bond all the way until maturity, duration risk effectively drops to zero. So it really depends on what your balance sheet looks like. If you only buy bonds with cash and you don’t use leverage, duration risk is essentially a non-issue because you are just going to hold the asset and collect your principal back at the end of the contract.

The second risk is default risk, and this one is a threat to everyone. It’s also known as credit risk. This is technically monitored and graded by the big rating agencies. I’m sure you’ve heard of S&P, Moody’s, and Fitch. They operate as a natural oligopoly. The government handed out a ton of licenses back in the day, but the market naturally concentrated around these three major players.

What they created is a standardized trust scale for what is ‘good’ debt and what is ‘bad’. The two major categories are Investment Grade and High Yield (also known as junk bonds).

Anything rated BB+ or below is technically considered high yield. Everything above that (from BBB- all the way up to a flawless AAA) is considered investment grade.

For context on these ratings, the US Government sits at the top tier (around AA+ or AAA depending on the agency), while a small, highly leveraged micro-cap company will probably sit deep in the junk bond category.

Think of something like Aurora’s debt. According to the market, these smaller companies don’t have the immediate ability to guarantee repayment or control the macro environment around their debt. Therefore, you’re dealing with two completely different worlds of risk.

Credit or default risk refers strictly to the permanent loss of capital.

In the case of a major government, they can print their own currency, so it is technically impossible for them to default on nominal terms. You still face inflation risk (since printing money devalues its purchasing power), but the default risk itself is pretty much non-existent.

The exact opposite applies to a small company. They don’t have a printing press. You’re getting a much higher interest payment (higher yield) just to hold their debt, but if they hit a wall because of an imbalance in their assets versus liabilities, you’re the one holding the bag.

As bondholders, our upside is hard-capped. The maximum return we can ever get is our coupon plus our money back. Because our upside is limited, if the company goes under and can’t repay, you are going to get pennies on the dollar in bankruptcy court, and there isn’t much else you can do about it.

Curving the Yield

Lastly on this part, you have the concept of the yield curve. The yield curve is sort of... how should I explain this? Well, it maps out the difference in time, and time in this case really conditions risk.

Generally speaking, the longer the lifespan of a bond, meaning the more time between when it’s issued and when you get your principal back, the riskier it is.

Because of that, you’re usually going to see an upward slope, which we call a normal yield curve. The curve goes upwards because as you transition from, say, a 10-year to a 30-year debt instrument, you demand a higher interest payment to lock up your cash.

The future is unpredictable, and you face two major risks over long periods: inflation eating away at the purchasing power of your principal, and general macro uncertainty. More uncertainty means you demand a higher premium.

However, this curve can invert.

Sometimes, the yield on short-term debt becomes higher than long-term debt just because immediate, near-term uncertainty is extraordinarily high. An inverted curve is usually a stellar leading indicator of an incoming recession.

These inversions are heavily driven by supply and demand dynamics, often influenced by central banks. For example, when a central bank adjusts interest rates or a foreign central bank aggressively buys or sells short-term paper, that massive shift in demand heavily impacts the front end of the curve, the six-month to 24-month section.

These short-term outbursts of confidence (or panic) shake up the short end, while the long-term end stays relatively grounded in long-term economic growth expectations.

When that short-term rate spikes rapidly, it creates a very weird, upside-down situation where issuers are forced to pay more to borrow money for a few months than they are for 10 or 30 years.

Printing Presses vs. Productive Assets

Going deeper into credit itself, there are two major flavors: government bonds and corporate bonds. We just talked about the general trade-offs between the two, but understanding how they interact is really where you build your ability to specialize.

Note: For example, my specific edge lies in navigating niches like oil and gas corporate bonds, specifically on the junior mining, exploration, and tech-driven mapping side of the sector.

Different industries and issuers obviously command different interest rates, and that gap is dictated by what is known as a credit spread. The credit spread is a foundational concept you need to master; think of it as the risk-premium layer stacked on top of baseline interest rates.

To map this out, major government bonds (like US Treasuries) are typically treated as the ‘risk-free’ rate, which serves as the global benchmark for valuation.

Note: If you want to check out exactly where you country lies, in the industry we all use the Damodaran Resource. It’s the most accurate way of getting a pinpoint figure for discount rates.

However, once you look outside of rock-solid economies, you introduce country risk.

Take a country like Argentina, for example. If you want to price debt there, you start with the baseline risk-free rate, and then you have to tack on a plus, essentially an additional yield premium that international investors demand just to take on the geopolitical and macroeconomic risk of investing in that specific country.

Right now, that country risk premium for Argentina hovers around 5% (500 basis points) as they attempt market normalization, though historically it has spiked as high as 25% during major crises.

How does this impacts corporate bonds?

If you are analyzing a corporate bond issued by a private company operating inside Argentina, an investor is going to demand a stacked combination of yields.

Your total interest rate is going to equal:

So in practical terms, if the global risk-free rate is 4.5%, the Argentine country risk premium is 5%, and the specific operational risks of that oil exploration company require another 5% spread, the final interest rate on that corporate bond will be at least 14.5%.

Obviously, there are additional technicalities like liquidity and structural covenants that play into this (essentially making people demand higher rates), but that is the exact core architecture of how credit is priced globally.

Death and Taxes

Another core pillar to understand is the taxation on these bonds.

Just like any other financial instrument, when you make money, the taxman wants his cut. However, to incentivize investments within public works, a lot of municipal bonds (debt issued by local cities or states) are completely tax-free.

You absolutely should factor this into your math; sometimes, investing in a muni bond can be extra juicy because that tax exemption significantly boosts your net, after-tax yield compared to a fully taxable alternative.

This ties directly into the actual purpose behind issuing the debt.

There is a world of difference between a government raising money for public infrastructure, social programs, military spending, or just covering national deficits, versus a corporation raising money to build new factories, fund Research & Development, acquire competitors, or buy back their own stock.

(Or, in the case of a few tech companies nowadays, issuing corporate debt specifically to stack Bitcoin on their balance sheet).

In this regard, to be honest, government bonds are inherently lacking because most sovereign spending is non-productive in the strict capitalistic sense.

If you build a manufacturing plant with corporate debt, that factory produces a product, generates sales, and automatically becomes a cash-generating asset.

That is exactly how debt should be used, in my opinion.

When you borrow money to fund non-productive spending, you are adding structural risk to the system. At the end of the day, you need tangible cash flows to service and pay back debt. If you invest the borrowed principal into an asset that naturally multiplies cash flows, paying back the lender becomes a highly predictable outcome. If you don’t, things get complicated.

Liquidity

(I was going to use the official Liquidity Summit logo here, but I don’t want the All-In guys suing me. I can’t afford Chamath’s lawyers, and quite frankly, my incoming interns aren’t qualified to handle a deposition yet)

And I’d say the final major difference between these two worlds is liquidity.

A lot of those high-yield, wide-spread corporate bonds are incredibly illiquid. If you try to buy or sell a massive block of them, your transaction alone can drastically move the market price against you.

That is definitely not the case for major government bonds, purely given the staggering size of the sovereign market. It is virtually impossible to dump a few hundred million bucks of US Treasuries and noticeably affect the global price.

However, corporate credit is a completely different story, especially on the private debt side of things that has exploded in popularity recently.

In private credit, you don’t hold publicly traded papers; those bonds are entirely private. When you want to exit your position, you can’t just press a button and dump them into a public exchange. You have to find a specific buyer and negotiate the entire transaction on a one-to-one basis.

This introduces heavy counterparty friction and severe liquidity constraints. If you ever find yourself in a position where you need your money back fast, that structural illiquidity can be a total recipe for disaster. And I mean global disaster given the size of this market.

Bond Valuation

Bonds are beautifully straightforward. In my opinion, the way you value a bond is the exact same way you value an equity… it’s just that bonds have a massive layer of certainty baked into them.

At its core, it’s just a DCF (Discounted Cash Flow) model. You are simply summing up all the projected cash flows from right now until judgment day.

When you value a stock, you have to project a terminal value out to infinity because companies are theoretically designed to run forever. But with a standard bond, you don’t need to guess about infinity. There is a hardcoded beginning and an end. Unless you are dealing with perpetual bonds (which we’ll talk about in a second), you know exactly when the contract terminates, and that total return is beautifully captured in the yield.

To do the math on a bond DCF, you just take each scheduled cash flow and divide it by (1 + r)^t, where r is your required rate of return (effectively the yield).

This discount rate acts just like the WACC (Weighted Average Cost of Capital) you use in a regular corporate DCF. When you add all these discounted slices together, you get the fair intrinsic value of the bond.

Again, it’s exactly like evaluating a business, but your coupon payments are legally contracted. You know exactly how much they are supposed to pay you and when.

In my opinion, this concept should be literally branded into your brain because mastering bond mechanics is the absolute best way to truly understand how DCFs work and how to value any asset.

Without predictable cash flows, you aren’t really investing… you’re just speculating.

Apply this exact logic to the market, and the line between a calculated play and a blind bet becomes crystal clear:

• Gold vs. a Gold Mine: A brick of gold sits in a vault, generates zero income, and costs you storage fees. That’s speculation. You’re just praying someone buys it for more later. A gold mine is an investment; it extracts a commodity, sells it, and throws off actual free cash flow.

• A House: Buying a property just hoping the neighborhood appreciation skyrockets is speculation. Buying a rental property that collects a predictable, monthly cash check from a tenant is an investment.

• Cryptocurrencies: And, of course, the ultimate modern speculation. Crypto has no earnings, no coupons, and no cash flows to discount. Your entire thesis relies on the Greater Fool Theory, pretty much the hope that someone else will pay more for your digital tokens tomorrow (including supply/demand dynamics).

The fundamental difference between a bond and every other cash-generating asset on earth is simply the contractual certainty of its cash flows. And that certainty, of course, always commands a premium.

Getting Paid Today

As for return structures, the absolute classic is what we call a bullet bond.

With a bullet structure, you invest your capital on day one, you collect regular interest payments along the way, and then the issuer hits you with a single, massive bullet payment of your entire principal at the very end.

But that’s not the only way debt is structured. You also have amortizing bonds.

You can think of these exactly like a home mortgage. With every single payment, the issuer pays you back a mix of interest and a piece of the underlying principal.

By paying back the principal gradually, they are constantly reducing the total amount of money they owe you, which naturally decreases the size of the interest payments over time. You see this layout all the time in real estate mortgages, certain corporate bonds, and specifically in government debt when the market doesn’t fully trust the country.

Argentina, for example, has some very famous restructurings where they issued bonds denominated in USD. Because the market was highly skeptical of lending to them long-term, Argentina had to issue amortizing bonds.

This structure systematically reduces the counterparty risk (government defaulting at maturity) by forcing them to pay back chunks of the principal early. For a country that has defaulted on its debt multiple times throughout history, investors demand those kinds of hard assurances.

Another major variation is the zero-coupon bond. These bonds have absolutely no ongoing cash flows, they completely strip out the coupon side of things.

Instead, they are issued at a deep discount. So instead of buying the bond at a face value of $1,000 like a standard issue, you might buy it today for $600 or $700, with the binding promise that the issuer will repay you the full $1,000 par value at maturity.

Because you don’t receive any intermittent payments, zero-coupon bonds carry extreme duration risk. They are hyper-sensitive to interest rate fluctuations. Any slight shift in the market discount rate will violently swing the value of a zero-coupon bond, because your entire return is back-loaded onto the very last day.

These instruments are incredible tools if you are macro-trading a high-interest-rate environment where you expect rates to plummet, or if you are investing in a hyper-stable environment where you want to lock in a return without the operational hassle of managing monthly cash distributions.

The final category is perpetual bonds. These are bonds with absolutely no maturity date. They promise to pay you a fixed coupon from now until judgment day, but they never have to give you your principal back.

In my opinion, most historical perpetual bonds have been massive scams, think of old wartime bonds and the like.

Perpetual bonds suffer from a fatal flaw: over long periods, governments will inevitably print a mountain of money, inflating away the real purchasing power of that fixed coupon payment.

Unless you are buying an antique certificate to frame and hang on your office wall as a cool collector’s token, perpetual bonds have historically been a terrible place to store wealth across the centuries.

The Art of Living Off Contractual Cash Flows

To sort of wrap these things up, the total return for an investor in the bond market comes from three distinct elements.

First, and most obviously, you have the coupon payments. These are the regular interest payments sent over to you. Nothing changes here unless the bond completely defaults; they are rock-solid cash streams.

The second way you make money is through capital gains (or losses), which is simply the difference between the face value and whatever price you actually paid for the bond. If you purchase a bond on the secondary market at a discount, meaning you pay less than par, and you hold it all the way to maturity, the issuer must pay you back the full par value. That price convergence hands you a nice capital gain.

Now, it’s highly unlikely that this capital gain will be massive under normal circumstances because bond upside is strictly capped. Trying to chase explosive, stock-like returns in the bond market is incredibly risky.

If you are buying a distressed bond trading at, say, 20% of its par value, the market is screaming at you that the company is on the verge of bankruptcy. If they somehow survive and pay you back, sure, you make a spectacular 5x return. But chances are you will likely never see your money again.

I wouldn’t say those distressed bonds are completely worthless, but they are highly complex corporate turnarounds that you don’t want to mess with unless you are a specialized distressed-debt investor.

The final element, and one most people discard, is the reinvestment of your income.

The unique thing about bonds is that they are constantly throwing off cash, especially if you hold an amortizing structure. Because you are getting your liquidity back early and often, what you choose to do with that incoming capital is completely up to you.

Let’s do the math: if you buy a million-dollar bond paying a 10% interest rate, you are collecting $100,000 every single year. In the bond market, interest is typically paid out semi-annually. So every six months, a $50,000 cash chunk hits your account.

Unlike equities, where you often deploy a million bucks into a stock and have to wait years until you liquidate the position to realize a return, bonds unlock recurring liquidity. Unless a stock pays a high dividend, you rarely see that kind of consistent cash flow.

What you do with that $50,000 every six months is entirely up to you. But if you roll it over and reinvest it into new income-generating assets, it creates a powerful secondary compounding effect. This reinvestment rate is truly the hidden engine of the fixed-income world.

I hope you enjoyed this masterclass, I hope the real-world examples were useful, and most importantly, I hope you learned a ton about how the global debt machinery moves.

I look forward to seeing you all in part 2 next Wednesday, where we will be doing a deep dive into forensic due diligence.

This is the absolute master key behind the investigative scuttlebutt research we do to find hidden gems in the market, and we’ll use a private company as an example (as it required an even more thorough examination).

See you then!

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