Turtles Research Ceasing paywall in 6 months

Will continue writing for another 6 months

This blog has become a bit of a distraction from my other ambitions lately. And I don’t like the direction Substack is heading in anyway. And I am currently not really making enough with this newsletter for it to be worth my time anymore.

BUT I will continue writing up new ideas for another 6 months.

Then I will cease writing paywalled articles. I lowered my monthly price to 30 euro’s/month. Anyone subscribing or renewing annual or founding after today (January 12th) will not get their money back! You can do a charge back though if you read this post a few days from now and it renews. But I won’t refund it (I will lose >10% on that due to fees). So if you want to keep reading you can cancel your annual sub and just sub monthly for 30/month until June 2026. I will then pause subs entirely in mid June.

Then whoever still has an annual paid subscription running from before today (the 12th of January 2026) can get their money back for the remainder in July if they wish. After that I will still write the occasional free post.

As for the next 6 months, I suspect software stocks will be the main focus due to the narrative that AI will destroy software. Everyone on Twitter seems very bearish on them. As I am building a tool for myself using AI (Claude Code) to make finding mispriced stocks and special situations more efficient I kind of have a front row seat to this.

It seems the bear thesis has two parts:

• Customers will just build products inhouse.

• SaaS market will be flooded with new competition from smaller companies as AI lowers barriers of entry

I think the first point is completely ridiculous for 99% of SaaS services. While there will be consumers and companies building vibe coded apps for very niche use cases, most SaaS apps such as accounting, EHR, logistics software etc will not be built inhouse as economies of scale kick in very very rapidly for most customers. There will always be weird edge cases and bugs that cost way more in time spent to fix than the 3-400/year you are paying to some well established service that has these ironed out already. Especially when other things such as security are very important.

As for the second point, I think this assumes that the only moat in software is cost of building an app. Assuming that somehow most software was gate kept by a lack of software engineers, prohibitively high costs. I would say mid to large scale software companies primarily get their moat from:

• Stickiness, can be broken up into:

  • Employees having to learn a new work flow (AI wont help much here).

  • Time cost of switching (AI will erode this one)

  • Something going wrong and losing a customer worth 10x the cost savings (AI will help with this one, but will take a long time before perception of this risk goes away).

  • Custom made rules, automations, fields etc. Building this requires feedback with the customer which is a headache, AI won’t help much here.

• Network effects. Everyone usually thinks of facebook here. But for example employees knowing how your software works so they can be more productive at it is kind of a network effect, (although technically this belongs in stickiness basket as well.) These can be local or global. AI does little to erode this.

• Data Moat. AI actually widens this moat. As new entrants will simply lack the data to provide same quality service.

• Integrations with other software. Not easy to achieve for a new entrant. Especially if trust declines when a lot of these vibe coded apps start causing damage.

• Good track record of reliability if that is important (for example very high up time, security or compliance). A new entrant would have to be much better in some quick and obvious way.

• Distribution advantage. When you are the major player, when customers search for a solution that is first one they will find.

• Economies of scale. AI tools won’t mean code becomes free. Larger companies can spend on more security, new features, customer service, lower hosting costs etc.

• Certification requirement. This one is somewhat rare, but I have seen them. Where it is near impossible for a new entrant to break in as government doesn’t like sensitive data spread out over a dozen companies for example.

Finally I have said this before, but the more complex/specialized a piece of software is, the worse these AI tools are at building it. To the point where an experienced dev is more productive just writing it themselves.

I do think in order to win this time around, you need companies that actually generate earnings (SBC is a real expense!) or some other strong catalyst. PE ratios will matter. So buying some cash burning SaaS at 5x sales that sold off 50% from 10x sales might not be a winning strategy anymore.

Also good company culture matters. I think for quite some time these AI tools will work best in the hands of experienced devs. And most experienced devs work in some capacity for software companies. So companies with decent code bases and good talent will likely become significantly more productive.

My prediction is that in a year or two as the strengths and weaknesses of these AI tools become clear people will still remember that a lot of SaaS companies have really nice recurring revenue, solid potential for a moat and need essentially 0 capital to grow revenue once already breaking even and the good ones will end up doing well.