Alex Labreche retweeted
US voters just rejected data centres everywhere they got the chance.
That investment has to land somewhere. Clean hydro. Open land. Cold winters.
That's Canada 🇨🇦
This is a massive opportunity for us. Generational.
That investment has to land somewhere. Clean hydro. Open land. Cold winters.
That's Canada 🇨🇦
This is a massive opportunity for us. Generational.
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Alex Labreche retweeted
Then this morning, we announced this year’s Ottawa Rocketships, the companies investors picked as some of the fastest-growing in the city:
🚀 @BackboardIO (Co-founder Jonathan Murray)
🚀 @Pluvoapp (Founder and CEO @AlexLabreche)
🚀 DashQ (VP of Regulated Products Zev Kershman)
wI (CEO and Executive Chair Niraj Bhargava)
🚀 @miRoncol (Co-founder and CEO Victoria Xu)
🚀 enurgen (Co-founder and CEO Kibby Pollak)
Congrats to every founder on the list, and thank you to SAAS NORTH AI for making room for us. ✨
🚀 @BackboardIO (Co-founder Jonathan Murray)
🚀 @Pluvoapp (Founder and CEO @AlexLabreche)
🚀 DashQ (VP of Regulated Products Zev Kershman)
wI (CEO and Executive Chair Niraj Bhargava)
🚀 @miRoncol (Co-founder and CEO Victoria Xu)
🚀 enurgen (Co-founder and CEO Kibby Pollak)
Congrats to every founder on the list, and thank you to SAAS NORTH AI for making room for us. ✨
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Alex Labreche retweeted
Ben Horowitz's message to early-stage founders on betting on yourself, the doubts that do the most damage, and why fear means you're doing it right:
"Your job is to make your bet pay off. You believed in yourself enough to do it. There are gonna be many times when you question on that belief, but that gets you nothing."
"The doubts are gonna be there. Ignore the doubts. The worst doubts aren't the ones people put on you. It's the ones that you put on yourself. Every entrepreneur has them."
"I spent a lot of time with Mark Zuckerberg when he was 22 years old and he had a tremendous doubt about whether he was gonna make it."
"It's not what you feel. It's what you do. You're gonna feel scared. If your guts aren't boiling, you're not even trying at this thing."
@bhorowitz w/ @speedrun GP @andrewchen
"Your job is to make your bet pay off. You believed in yourself enough to do it. There are gonna be many times when you question on that belief, but that gets you nothing."
"The doubts are gonna be there. Ignore the doubts. The worst doubts aren't the ones people put on you. It's the ones that you put on yourself. Every entrepreneur has them."
"I spent a lot of time with Mark Zuckerberg when he was 22 years old and he had a tremendous doubt about whether he was gonna make it."
"It's not what you feel. It's what you do. You're gonna feel scared. If your guts aren't boiling, you're not even trying at this thing."
@bhorowitz w/ @speedrun GP @andrewchen
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Alex Labreche retweeted
We experimented with wake-sleep learning algorithms for long-horizon legal agents.
We found:
1) Wake-sleep meaningfully improves agent performance on complex tasks.
2) Memory and context written offline during sleep generalizes to new matters and task types the agent did not trieving only the memories relevant to each task, rather than reading them all, halves cost per task while maintaining average quality.
Deep dive by @nikogrupen:
We found:
1) Wake-sleep meaningfully improves agent performance on complex tasks.
2) Memory and context written offline during sleep generalizes to new matters and task types the agent did not trieving only the memories relevant to each task, rather than reading them all, halves cost per task while maintaining average quality.
Deep dive by @nikogrupen:
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Alex Labreche retweeted
The CFO job has gone from accountant to banker to builder, and the whole finance function is being rebuilt around it:
1. Data stopped being the bottleneck. AI pulls and reconciles data across systems, so teams spend more time acting on numbers instead of chasing them.
2. The ng part of the finance team. AI-native tools stand up in days, pull their own data, and turn a monthly close into a daily one.
3. The rise of the finance engineer. Finance people proficient in Claude Code or Codex are building their own tools.
4. The finance team gets smaller and higher leverage. The old heuristic of finance at 5% of headcount is heading to 2% for the leanest teams.
5. Planning becomes continuous. Usage-based revenue (tokens, API calls) moves too fast for a monthly forecast, so planning shifts from annual budgets to a live view.
6. Controls become a critical feature of the product. Every number an agent touches keeps an audit trail back to its source, and finance still owns sign-off.
7. Finance gets a bigger mandate. Compute spend, people versus software, and build versus buy are now finance decisions.
"The CFO is becoming a builder and architect of the company's operating system... The job is still finance, but more of the job now is designing how finance gets done."
a16z's @seemaamble, @IvanoMakSF, and David Borecky on why you need a new CFO: a16z.news/p/you-need-a-n…
1. Data stopped being the bottleneck. AI pulls and reconciles data across systems, so teams spend more time acting on numbers instead of chasing them.
2. The ng part of the finance team. AI-native tools stand up in days, pull their own data, and turn a monthly close into a daily one.
3. The rise of the finance engineer. Finance people proficient in Claude Code or Codex are building their own tools.
4. The finance team gets smaller and higher leverage. The old heuristic of finance at 5% of headcount is heading to 2% for the leanest teams.
5. Planning becomes continuous. Usage-based revenue (tokens, API calls) moves too fast for a monthly forecast, so planning shifts from annual budgets to a live view.
6. Controls become a critical feature of the product. Every number an agent touches keeps an audit trail back to its source, and finance still owns sign-off.
7. Finance gets a bigger mandate. Compute spend, people versus software, and build versus buy are now finance decisions.
"The CFO is becoming a builder and architect of the company's operating system... The job is still finance, but more of the job now is designing how finance gets done."
a16z's @seemaamble, @IvanoMakSF, and David Borecky on why you need a new CFO: a16z.news/p/you-need-a-n…
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Alex Labreche retweeted
A couple days ago I mentioned Legora's surprisingly high 9x annual growth rate. These numbers explain what's happening. They win 78% of competitive pilots, and gross retention is 95%.
Five numbers tell you whether an AI business is a real business.
Gross retention.@WeAreLegora is 95%. Customers who bought last year are still here this year. If this one is broken, nothing downstream matters.
NRR. Ours is 300%+. Gross retention is the floor. NRR is how much on top of it. We don't sell shelf-ware.
DAU/MAU. Ours is north of 50%, and the average active user spends 17 hours a month in the product. A rollout tells you a firm has signed. This indicates the work actually matters, and it moves here before it appears in retention or NRR.
Win-rates. Our August pilot closed-won-win rate was 78%. Winning roughly 4 out of 5 competitive pilots is downstream from offering a superior product.
Gross margin. The one that matters most. Ours is positive and improving every quarter. Our customers want Legora to be a long-term partner, and this is what makes that possible.
There's a shorter route: Price below what it costs to serve, book the logo, and hop on a never-ending fundraise treadmill to pay for it. The top line goes up, everyone claps, and every new customer costs more than they pay.
Scaling a negative margin only exaggerates the problem. The whole point of scale is that the margin improves as you go up. Ours does. That's the only version of this business worth building.
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Alex Labreche retweeted
Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that t unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis.
Rationale:
A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.
Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.
This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.
Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.
The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.
Time will tell on both points. And likely fairly quickly.
Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
Rationale:
A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.
Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.
This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.
Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.
The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.
Time will tell on both points. And likely fairly quickly.
Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.
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Alex Labreche retweeted
you're not building a company for the world as it is today. you're building for the world as it'll exist in 5 years. make a hyper-opinionated bet on that future - then pre-build it. so when the world arrives, you're already there.
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Alex Labreche retweeted
being in Europe for a bit (while beautiful) ends up just making me more bullish on America 🇺🇸
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Random thought:
What’s the average cost of human intelligence vs. artificial intelligence?
Getting AI from 0 → 1 took billions of dollars. But 1 → N might become so cheap that it makes me question the very economics of investing in human intelligence over artificial intelligence.
If AI can discover, invent, and iterate using the same scientific methods and theories we do but at a pace we can’t match and at a fraction of the cost what does that mean for the future? (The cost piece still has a lot of work to do)
The bare minimum cost of human intelligence is the cost of survival. In most cases, it’s decades of food, shelter, education, mentorship, and support before someone reaches the ability to bring innovation to the market.
If intelligence itself becomes dramatically cheaper to create artificially, the only logical long-term path might be merging with the machines… while we still have the choice. Even our physical bodies aren’t exactly optimized. Whole other problem.
Anyways just thinking out loud… back to selling B2B AI software :)
What’s the average cost of human intelligence vs. artificial intelligence?
Getting AI from 0 → 1 took billions of dollars. But 1 → N might become so cheap that it makes me question the very economics of investing in human intelligence over artificial intelligence.
If AI can discover, invent, and iterate using the same scientific methods and theories we do but at a pace we can’t match and at a fraction of the cost what does that mean for the future? (The cost piece still has a lot of work to do)
The bare minimum cost of human intelligence is the cost of survival. In most cases, it’s decades of food, shelter, education, mentorship, and support before someone reaches the ability to bring innovation to the market.
If intelligence itself becomes dramatically cheaper to create artificially, the only logical long-term path might be merging with the machines… while we still have the choice. Even our physical bodies aren’t exactly optimized. Whole other problem.
Anyways just thinking out loud… back to selling B2B AI software :)
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I find this release particularly interesting. Theoretically, once @OpenAI releases the same functionality. Will this allow for better context portability. Will ChatGPT, just be able to pick up on the back and forth the team and I had *(including Claude’s contributions), and just continue as a slightly different agent?
If yes, inter-company communication tax are about to significantly drop.
If yes, inter-company communication tax are about to significantly drop.
Introducing Claude Tag, a new way for teams to work with Claude.
In Slack, Claude joins as a team member with access to the channels and tools you choose. Tag Claude in and delegate tasks to it while you focus on other work.
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This also makes me think about what are some of the most critical moats as a startup… the more I think about it, the more startups have to think about investing into their communities. Slack just became enterprise inter-company communications context, overnight, for anyone using slack…
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