Antoine

Interested in AI, DevTools, infra, fintech. And also jazz 🎷

[Kyle Harrison @kwharrison13]

I found the dictionary-perfect definition of mission-driven:

“I believe in ruthless optimism. Rational decision making requires detached risk analysis. But we also cannot win if we believe we can lose. Merging the two requires orienting teams around driving missions. That way, when a real opportunity presents itself, you can take a huge swing.”

[Rem Koning @orgRem]

🤖Excited to share a new working paper.🤖

The phrase "AI-Native firms" it everywhere, but are they any different? Is it just hype?

In our new paper we show AI ventures are organized differently, but not for the reasons you think.

[Séb Krier @sebkrier]

How do we go from AGI to Superintelligence? New report discusses four potential pathways: scaling, AI paradigm shifts, recursive improvement, and ASI emerging from large-scale multi- agent collectives. Importantly, it also looks at possible frictions and bottlenecks along these pathways. Instant classic! https://t.co/uBF3m2YoyH

[Richard Seroter @rseroter]

The Unbundling and Bundling of the PaaS Market https://t.co/yewyySndng < keeps happening. We get a bunch of independent primitives for people to assemble into stacks, and then we get opinionated stacks. And back again. @sogrady looks at the pendulum.

[Antoine @anbuteau]

@nectarios The worst! Feels like that’s the right attitude

[BuccoCapital Bloke @buccocapital]

At this point I am just highlighting whole sections of documents and commenting “AI. Bad. Redo”

We need to make people feel ashamed to deliver garbage

[Antoine @anbuteau]

@ericrichy @coinbase Congrats Eric!

[Antoine @anbuteau]

@jeff_weinstein if you like music there's also Bar Ste Angele that has jazz on Friday-Sunday. Pretty small / cozy place. Always a good time.

[Antoine @anbuteau]

@jeff_weinstein Food: taniere3, clan, chez biceps if you like bbq, jjacques for seafood

To do: walk around old Quebec, strom spa, Ile d’Orléans tour, Charlevoix.

If with kids there’s Zoo Miller which is a bit further but really nice.

[Antoine @anbuteau]

@karrisaarinen @linear shakepay, thank you!

[Antoine @anbuteau]

@tdoggyholhol Love the aesthetics!

[Jaya Gupta @JayaGup10]

https://t.co/eUp5waUIwl

[Cedric Chin @ejames_c]

Two weeks ago I finished what I think will be Commoncog's most significant contribution to the AI discourse, at least for this year. (God forbid!)

This was the Sensemaking Series — which is a short, 3-essay series about how to make sense of AI.

Some of its ideas are surprising.

[Tropical Value @tropicalvalue]

https://t.co/3ac5cFgWaB

[Antoine @anbuteau]

It’s really good!

[Tristan @homsiT]

this has been a fun one. every piece of data you save to readwise/reader (highlights, full articles, PDFs, tweets, etc) is now embedded and indexed for full-text + vector hybrid search.. instantly ready

probably the easiest way to start saving external context for any AI

[Jared Sleeper @JaredSleeper]

For the last few months, I profiled one public software company per day (50 total) and wrote about the impact of AI on each.

Posts collated here:

https://t.co/cyEQtxFfYN

My takeaways after reflecting this morning:

1) This is not an innovator's dilemma situation like the on-prem to SaaS transition. The on-prem software companies were in deep trouble- they had legacy products and a legacy pricing model, and getting to the cloud meant sacrificing near-term revenue/profits/cash (subscription transition) AND moving each of their customers from an on-prem version of their software to the new cloud-native version, which created a significant change event- i.e. an occaison for their customers to consider whether the cloud-native option might be better, since they were going to have to make a big change anyway. For better or worse, the SaaS companies are not experiencing dynamics like that- no one is targeting them and running a successful "rip and replace" strategy at any scale. Net retention is stable to up, even for companies disappointing at the margins. The existing business model is intact for now- even growing (and in some cases accelerating).

2) The greater the long-term risk, the better the short-term case for acceleration. The paradox of investing in SaaS right now is that the more threatened a SaaS company is in the long term, the better the odds that it will accelerate and "disprove" the bear case in the near term. It is the companies closest to killer-app AI use cases (code, image/video gen/CX, etc.) that have both the best prospects for near-term upside and the most ferocious AI native competition. In many other categories, it simply isn't clear yet that AI adds enough value and/or is token-intensive enough to generate the incremental revenue required to accelerate. It seems likely that some SaaS companies will accelerate into their own obsolescence, and companies that don't accelerate near term are paradoxically better off in the long run (because they will have more time to adapt).

3) Most software management teams don't seem to have made wholesale organizational changes due to AI yet, and that's a disappointment. I would have expected to have read/heard widespread stories about increased operating leverage, cost takeouts, etc. but instead SaaS hiring continues apace and it shows up in management commentary. There is clearly a major struggle to change the culture of SaaS companies and elite talent is being poached by AI natives. I haven't seen a single management team talk about this honestly and put forward a strategy for attracting/retaining top talent in this environment- and I think that's a huge issue in the long run.

4) The mythical "shitty thin SaaS company with no moat" doesn't really exist in public markets. These companies have all gone through the gauntlet- competition with other VC-backed startups, competition with Microsoft, etc. Almost by definition, they've built up complex, moated businesses with brand equity, network effects, exceptional complexity, etc. That doesn't mean AI isn't an issue for them, but the "issues" I found that concerned me were more around disruption to the workflow the software company serves (see: Figma, Five9, etc.) than a disruption to the SaaS business model writ large.

Summing it up, the broad-brush SaaS bear case melts away somewhat when you go company-by-company. The median SaaS company is seeing little to no impact (in either direction today), while saying all of the right things on the product side and none of the right things on the organizational side.

That doesn't mean there won't be some sort of wholesale disruption down the road- but I don't yet have a clear picture of what it will look like. If you feel like you do, please pick a specific company, respond to or quote-tweet its profile, and explain what you think is going to happen. That's much more fun than debating in the abstract. :)

[Matt Slotnick @matt_slotnick]

https://t.co/aaI91e6ZMe

[Ajay Kulkarni @acoustik]

Fascinating profile of @Raspberry_Pi, a company I haven't thought about in a decade.

- $300M revenue ($1.4B market cap)

- 80% of revenue from industrial applications

- See a future of running LLMs on Pis

But the most fascinating part to me was how they built the company. They started as an educational company trying to give kids affordable programmable computers. Not focused on performance, but focused on accessibility, affordability, and getting computers into the hands of students.

And somehow those constraints led to a platform now used across industrial systems around the world.

They even moved manufacturing from China to a small town in Wales without losing efficiency.

I love projects like this. They bring out the kid in me.

And it sounds like they are having fun too:

"Yes, we call Apple 'the other fruit company.' Raspberry is special because raspberry is the rudest fruit. Originally, we were framing our product as a successor to the BBC Micro. And at the time, there was talk of some people at MIT making a successor to the Apple 2. There was a sense of, 'Hey, let’s blow a raspberry at that idea.'"

😂

https://t.co/m7gsGBxGG7

[Drew Bredvick @DBredvick]

Everyone is running coding benchmarks, but are these models any good at sales?

So I ran an experiment.

I generated 25 different example calls and ran 18 different model settings against them to spot coaching errors in calls.

Spoiler: GPT-5.4 high wins (read more in thread)

[tobi lutke @tobi]

https://t.co/5MJ7u1VHwf