LIVE DIARY--FOLLOWING THE SPREAD OF AI ON MY INVESTMENTS

 

DAIRY ON THE SPREAD OF AI

16/2/26

I am writing this as an ad hoc diary as we progress through the world of AI’s impact on the investing markets. I plan to add weekly, but it could be anytime. The impacts of AI are difficult to accurately assess, and this diary, in part, is to monitor how my views change over time. The impacts on the portfolio are already large, volatile and surprising.

Over the past few years, most of the gains in the portfolio have come from exposure to what I describe as “light” AI, being the hardware that builds the FABs and the hyperscalers that monetise the activity of token manufacture and delivery into use cases. The largest holdings have been TSMC, GOOG, AMZN, MSFT and ASML. The second driver has been specific Australian growth companies where the business is growing quite well and will certainly be much larger in 5 years, but the SP usually incorporates full value for these companies. Now and then, there is significant volatility, which allows opportunities to add or sell some. These companies include PNI, HUB, LOV, RMD, REA and to a lesser extent, NCK, CAR, ALL and TNE. The broad sectoral exposure is considered good. The rest of the portfolio has been a wash at best and a waste of time, probably more accurately.

Recently, with the software sell-off, I have added some exposure. These are the front line against AI, with the fear that software becomes redundant and all activity is generated by AI agents interacting between the client and the LLMs, leaving out the software provider. The AI fear, that is, that business models will become redundant and replaced by AI agents, has spread across various sectors. These include various forms of proprietary information providers, platforms that rely on network effects and even insurance brokers; in aggregate, this has taken a large toll on the portfolio. By its very nature, AI agents will attack high-margin information flows or services. The portfolio is, by choice, exposed to high-margin protected and growing businesses.

Market movements have also thrown up an inconsistency in logic, which is most probably due to extreme uncertainty over the outcomes. One possible, as they are all just possibilities at this stage, is that the inconsistency is in having the hyperscalers increase their capex budgets enormously. They have done this in response to immense and ongoing demand, and after seeing attractive returns on the older vintages of capex, as I described in the big tech talks. The market has been very cautious on this spend and has marked the SP down, indicating poor returns on the capex. At the same time, some of the apparent victims of this capex have been marked down as well. So everyone loses. Although that is an unlikely outcome, anything may be possible. IMO it exhibits the market's fear of uncertainty and prices that in illogically, until further evidence is shown.

There are a lot of circular discussions that can go on without end, as the outcomes at this stage are too wide to determine. So instead, I will briefly sketch out the base investment thesis and see how that changes over time.

The infrastructure builders have been the largest beneficiaries, and understandably so, as the huge capex spend ultimately ends up in their bottom lines. These include portfolio holdings, ASML and TSMC. In a broader sense, they include the chip designers, the networking manufacturers, the power suppliers and any specialist items needed in a FAB. The largest companies are NVDA, AVGO, AMD, etc. These are clearly the short-term investment plays. How long is short-term? Well, it could be quite a while if AI is a secular change across the whole economy. Ultimately, margins will probably spread across the chain, whereas at the moment they are concentrated in the hardware, but some exposure here is the lowest risk as long as capex continues to expand.

The next are the DC owners and operators, or hyperscalers, defined as businesses in storing and moving data, where more of this activity is AI-generated for themselves as well as third parties. Over time, the third-party business is expected to be a much larger part of the business. The big question here is ROI holding as the business scales up to enormous amounts of capital. The three largest are GOOG, AMZN and MSFT, all having extraordinary financial power which is required in this case. The thesis is that token usage grows enormously as AI spreads and that the returns on capex become apparent and are very lucrative. There is a good chance of an effective oligopoly forming here, although that is not in the interest of participants that dominate other parts of the chain, eg NVDA, who will actively try to undermine any competitive advantage in parts of the chain in which they do not play (e.g., fund competitors).

The next layer is the applications run by LLMs. The race for LLMs has some way to go, but it is becoming clearer that three are pulling ahead, being OpenAI, GOOG and Anthropic. The Chinese are an unknown potential, and other models such as the Musk group, Meta, and other smaller models appear behind, but this space is enormously unpredictable. The LLMSs are becoming large customers of the DCs, given their enormous usage of tokens. Enterprise, as opposed to consumer, is becoming the successful business here, but the ability to integrate into underlying businesses is critical. That is, make the LLMs useful in the real world.

The application layer is expected to be the main game over time. This is where the LLMs interact with the real operating businesses. Almost all players mentioned will have some involvement, and this is where the uncertainty of the incumbents arises.  The basic threat is the LLMs, and the agents developed from them effectively circumvent the incumbent software business, either making them redundant or effectively a low-margin facilitator. That is the risk that is being reflected in many businesses.

In theory, agents may run everything, and they become instrumental in all transactions and actions, and human involvement is nothing more than monitoring and maybe having some kind of veto. We do not know. However, theory and real business can deviate. The challenge for software businesses is to adapt and be the point of interaction with the client, and not lose that to a third party.

Below are the main issues and my current view,

Many in the market have pointed out proprietary data as a defence for the incumbents, and that is a reasonable argument. It rests on the agents not having access to the data. As long as that data stays proprietary, the software company has the upper hand. The data, however, does belong to the client, and if it opens that data, does that level the playing field? To me, this looks like a medium-term shield, maybe giving the incumbents some time to integrate and make client shifting less likely. There may be margin compression here.

Another issue is the size of loss on a wrong call by the AI agent, or the consequences of an error. AI agents work on probabilities, which in a lot of cases is close enough. If it is a low-grade administrative function, an error may be tolerable; if the activity can result in a possible business failure, even if a low risk, there is a better chance for the incumbent. I have a lot of time for this argument. Theory and business reality can differ. If something is cheaper and faster but has a slightly higher chance of error, and that error has devastating consequences, then using AI agents more readily is less likely. I would look to tax, insurance, and life-threatening work as examples. Of course, where that line is drawn, how many clients consider the risk devastating or not, is important. Im willing to bet here.

The challenge to the network effect is another issue to consider. The online marketplaces are a reasonably large part of the portfolio, and the moats here have been strong. Will clients move en masse to create another marketplace? There have been many attempts at this, and AI is another attempt. Getting volumes is critical. More eyes lead to more eyes. The moves by Amazon to limit AI crawlers on its site are instrumental. A deal with these crawlers will probably have to be met with some type of fee sharing. Following traffic will be critical here. The collapse of platforms is considered quite remote at this stage.

Another issue that is causing a lot of consternation at this stage is the seat-based models that many SaaS companies operate. As the volume switches to AI users, presumably being fewer but more active, while human seats slowly (or quickly) disappear, will promote a change in charging. This does not look like an insurmountable change to me, but it depends on execution and transferring the client base without large leakage. Probably a case-by-case outcome.  

Payments are another area that has come under some scrutiny, as AI traffic increases at a very low level. Most of these are moving to incorporate agents as customers, early days here. The uncertainty is impacting stock prices.

The portfolio is not exposed to many SaaS companies, with the larger holdings including MSFT, which has other large businesses, and TNE is the largest. The main exposure for the portfolio is the hyperscalers, which are yet to prove the returns on capex, and as noted above, the market is quite cautious here and also cautious on the “losers” of this capex generating ROI. The platform companies are in larger positions, and we can see how they keep customers.

As can be seen, the portfolio is exposed, mainly, to the second layer of companies that could be affected. Hyperscalers, platforms, insurance brokers and payment companies. The outcomes for these companies and whether they are AI “losers’ is more nebulous than the directly exposed SaaS companies. Overall, I think the portfolio is not overly exposed to the “pointy” end of potential AI disruption, but has large “secondary” exposure, which I think is reasonably defensible at this stage.

 

20/2/26

Anthropic raised $30B at a $380B valuation. Since Anthropic was the main initiator of fear in the market and was about to go into a major raise (maybe the biggest raises ever, until OpenAI comes again), it was highly incentivised to talk up use cases, interesting to see if some heat comes out of this.

 

24/2/26

A viral doomsday report on the consequences of AI on various corporate profits and economic activity has been released with unusual attention by the market. The market is very skittish.

2/3/26

Watched a podcast interviewing Robert Smith Vista, s/w investor fund. Stated the following were interesting. You need software with a deterministic focus, not probabilistic, which is how LLMs work. Where near enough is good enough, you are vulnerable. Require industries where high compliance and accuracy are critical. S/w should bring the model to the data and not the data to the model, as that risks data leakage. Do not invest in software companies that rely on data that is available outside the enterprise, which LLMs can be trained on. Smith also said the successful software companies will significantly grow as they expand their scope and replace various costly outsourcing activities for clients that require manual, repetitive, simple tasks.

6/3/26

(SM post) i watched with interest some changes in substantials from growth investors, like Hyperion. The business pressure to "do something" on these firms is enormous, and something, thankfully, we really don't have to do as retail investors (and im happy for that, i would not want to be in some of the meetings that woudl have taken place over the last month--been there, done that).

Some of these changes will be unavoidable, such as outflows as punters leave growth and go wherever, usually into resources in OZ, that's the easy move given benchmark weights, ok thats they’re choice. That means forced selling for the growth funds. Secondly, with everyone watching returns implode over the last month or so, the fundies will have to do something intelligent, but can't abandon their investment philosophy (as they shouldn't), so they will de-gross risk. that is, take chips off the table, reduce outsized bets etc.

Of course, the short sellers will be all over this and front-running the process as much as they can. ok thats allowed under the rules, fair enough.

The point is we see a knee-jerk reaction in SPs, and i think we are at the end of that. We now see some directors buying shares, reports of shorts covering saas expsoures, and the funds have made their initial moves.

What happens next? well we need more evidence one way or another. The shots fired so far look to be exhausted. usually there is a return to some normality. BTW, historically these types of reactions typically are great buying opportunities. It all depends on the trajectory of LT earnings.

We shall see.

i wonder if super intelligence AI (where AI does everything and most service companies are stuffed) can be added to the underlying equity risk that we continually live with, like thermo nuclear war or cannibalistic zombie pandemic, etc, a small % of happening but huge negative outcome, and we just have to know it exists, and get on with picking stocks.

15/4/26

One thread of my thinking was that OpenAI and Anthropic were coming into raising and that would promote a series of eye-catching headlines about the power of their models, and then the announcements would subside post those raisings, giving some relief for s/w stocks' prices. Now we have stories that both are running into potential IPO’s this year, which will prolong the shock news release timetable. With this background, Anthropic released a new model, Mythos, which again crunched software stock share prices. There has been no discernible impact on software profitability, so far. In fact, it makes me think what the data point would be that undermines the bear thesis here. I can't think of one. Usually, markets panic over the short term, war, tariffs, pandemic, etc., etc., but this panic is long dated, which is unusual and by definition impossible to disprove. At the moment, it is a lopsided fight. We just do not have enough information to prove or disprove that the LLMs are God and will destroy all in their path, at this stage. However, it is unlikely that the markets will be willing to sit with a long-term view for long, IMO.

There has also been more data on the economics of LLM’s, and they are complex. The pain point is that as queries and output increase in size, the cost to deliver output for the LLMs is exponential. This is due to the prompt having to be reread the whole prompt for every letter, so a one-sentence prompt is a minuscule cost compared to a very long prompt. Is that a problem? Well, it means that flat rate charging will be gamed, and the cross-subsidisation is not currently working. Either prices need to rise, demand caps need to be introduced, etc., or the LLMs need a surge in paying, low-use customers, or some other event. This conundrum probably needs to be sorted by IPO. As the unit economics will become of critical interest.

For the software companies incorporating AI, through using LLM models, the question is probably more: where does the value chain end up? That is the big unknown, and will the LLM owners advance into the domain of the software companies if they have a cost advantage? The cost advantage comes from having the model at cost, while the software companies will be using it with a gross margin on top. To me, that is the big unknown. And that goes back to the beginning and the bear point, we have to see where the abilities of the LLMs without the integrated position of the software companies stand in terms of usefulness and economics. Clearly, the software companies do not want a monopoly in LLMs, which would be a negative, but it is still unproven how and if the LLMs can or will move into the space.

Where does that leave us? Unless something pops out of left field that undermines the LLM’s use cases in the real world that is relevant for the software business, this is an open-ended problem. Maybe the IPO documents may show a different view of the LLM’s and unveil issues that give a data point to the bear argument. Certainly, the market is already pricing in the winners on little information.

How to play this. I am prepared to take on some risk here. IMO, your overall portfolio weight should be capped to something you can stomach. Secondly, be in stock where the story is as simple as possible. Unlikely the market will like the possibility of an existential risk with a complex operating or other agenda as well. I would think these management teams have enough on their plate as is.

Below eleven software stocks SPs, in order of descending returns over the FY: CRWD, CRM, TNE, ADBE, VEEV, INTU, NOW, CSU, WKL, XRO and WTC.