Comments URL: https://news.ycombinator.com/item?id=48099264

Points: 4

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Comments URL: https://news.ycombinator.com/item?id=46466770

Points: 2

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The workflow was roughly: ingest publicly available outlook reports, extract claims, numbers, and framing (& force every synthesized statement to be tied back to a specific source/page), anchor on key themes (ai capex, policy path, power/energy, credit, etc.) rather than by institution.

Comments URL: https://news.ycombinator.com/item?id=46407376

Points: 10

# Comments: 3

Perhaps not. If LLMs keep getting better, more competent models can help him stay on top of it lol.

I agree - it is surprising how many are looking at doing in house.

I think what they miss (and I say this as someone who spent the early part of his career outside of tech) is an understanding of what goes into maintaining software products - and this ignorance will be short lived. I was honestly shocked how complex it was to build and maintain my first web app. So business types (like I was) who are used to 'maintaining' an excel spreadsheet and powerpoint deck they update every quarter may think of SaaS like a software license they can build once and use forever. They have no appreciation of the depth of challenges that come with maintaining anything in production.

My working model is that of no-code - many non-tech types experimented with bubble etc, but quickly realize that tech products are far deeper than the (heavily curated) surface level experience that the user has. It is not like an excel model where the UI is codebase. I expect vibe-coders will find the same thing.

I have on several occasions built my own versions of tools, only to cave and buy a $99 a year off the shelf version because the maintenance time isn't worth it. Non-tech folks have no idea of the depth of pain of maintaining any system.

They will learn. Will be interesting to see how it plays out.

Quite honestly, this is exactly what I am currently doing - identified a market with probably $50mm global TAM. Bootstrapping with first design partners currently.

One thing I didn't mention is that there are often a few sleepy legacy SaaS players (often public) in these niche markets who don't have the chops to add AI to their product and may be a good takeout / exit down the line. Won't be for billions, but if you bootstrap, that doesn't really matter.

To attempt to summarize the debate, there seems to be three prevailing schools of thought:

1. Status Quo + AI. SaaS companies will adopt AI and not lose share. Everyone keeps paying for the same SaaS plus a few bells and whistles. This seems unlikely given AI makes it dramatically cheaper to build and maintain SaaS. Incumbents will save on COGS, but have to cut their pricing (which is a hard sell to investors in the short term).

2. SaaS gets eaten by internal development (per OP). Unlikely in short/medium term (as most commenters highlight). See: complete cloud adoption will take 30+ years (shows that even obviously positive ROI development often does not happen). This view reminds me a bit of the (in)famous DropBox HN comment(1) - the average HN commenter is 100x more minded to hack and maintain their own tool than the market.

benzible (commenter) elsewhere said this well - "The bottleneck is still knowing what to build, not building. A lot of the value in our product is in decisions users don't even know we made for them. Domain expertise + tight feedback loop with users can't be replicated by an internal developer in an afternoon."

This same logic explains why external boutique beats internal builds --

3. AI helps boutique-software flourish because it changes vendor economics (not buyer economics). Whereas previously an ERP for a specific niche industry (e.g. wealth managers who only work with Canadian / US cross-border clients) would have had to make do with a non-specific ERP, there will now be a custom solution for them. Before AI, the $20MM TAM for this product would have made it a non-starter for VC backed startups. But now, a two person team can build and maintain a product that previously took ten devs. Distribution becomes the bottleneck.

This trend has been ongoing for a while -- Toast, Procore, Veeva -- AI just accelerates it.

If I had to guess, I expect some combination of all three - some incumbents will adapt well, cut pricing, and expand their offering. Some customers will move development in house (e.g. I have already seen several large private equity firms creating their own internal AI tooling teams rather than pay for expensive external vendors). And there will be a major flourishing of boutique tools.

(1) https://news.ycombinator.com/item?id=9224

You create 'content' by doing something orthogonal. You pay for access vs selling attention for ads. You only look at Strava when you are working out, so engagement is authentic vs contrived/performative. I care about my friend completing a run because I know they did it.

One of the best thing they did was allow the chance to add photos.

It was curious how effectively the American naval establishment gamed the European 'honor' system of naval warfare - they knew that if they kept these warships technically rated as 'frigates' (even though they were the largest and most powerful frigates ever built, similar in size to smaller ships of the line), the British would still try to fight them one on one with their frigates.

Gemini is $30/1000

So Anthropic is actually the cheapest.

For context, exa is $5 / 1000.

"Ben Thompson: What’s going to be more valuable in five years? A 1-billion daily active user destination site that doesn’t have to do customer acquisition, or the state-of-the-art model?

Sam Altman: The 1-billion user site I think."

Distribution: OpenAI believes the marginal token they sell will be accretive to their bottom line, so the goal then is to deliver as many tokens as possible. Windsurf already has 1k+ enterprise logos and allegedly millions of downloads. 2 m tokens × $0.00001 gross / token = $20/seat/mo; if windsrf runs 500k seats, oai books $120 m/yr gross @ 90% margin.

I saw a similar dynamic play out in the UK with Pub (bar) companies. By mid-2000s, the major players were failing. Margins were nearly zero, thanks to rising costs, and securlar decline in demand, plus they had too much expensive term debt.

But they represented profitable sources of distribution for the beer makers. So Heineken went on a buying spree. They didn't care about making money from the pubs themselves and were happy to run them break-even. This is because they then had a controlled channel of distribution for their beer (and they made a profit on every pint they shipped).

The switching costs are very different here, and the market is still so nascent. It is a thin product and vscode‑copilot can catchup. But 1% of enterprise value ($3bn of $300bn) is not a lot to gamble on owning the #2 horse in the most promising AI end market today.

What saved them? Heineken. They didn't care if the pubs made much of a profit - they made positive margins on having people drink their beer at market prices. They just wanted to increase volume. So they bought up several major players. In 2008 they acquired Scottish & Newcastle's operations, later thought bought Star Pubs & Bars, which had 1,049 leased and tenanted pubs, and finally half of Punch Taverns.

The same strategy can work for OpenAI - buy up the wrapper companies, and make sure YOUR models are served to the user base.

Comments URL: https://news.ycombinator.com/item?id=43698251

Points: 1

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