Bram Sugarman

Building. Investing. TBD. prev: VP @Shopify, VC @OMERSVentures, SE @EA

[Bram Sugarman @bram]

Legendary studio 🥲

[Kawhi Leonard @Way2Whi]

As I Return.

[Bram Sugarman @bram]

The next wave of great founders and companies will use AI as an abundance resource focused in two areas: science and creativity.

Gaming is an obvious example. Some of the best technical and creative minds already build games. Can't wait to see what they unleash.

[Tim Sweeney @TimSweeneyEpic]

The games we've long wanted, which didn't exist because they'd cost several times more to make than they'd earn back, will soon become possible. As will the games developers wanted to build, but couldn't with the resources available. It'll be a new golden age of gaming.

[Elizabeth Holmes @ElizabethHolmes]

@gregisenberg KEEP GOING. You got this. Never give up!!!!

[Sid @sid_srk]

Last week to apply for Catalyst 003: Critical Minerals 🇨🇦
Looking at the applicants now, I'm SO SO EXCITED to kick this Catalyst off, bringing together a bunch of experienced geoscientists and software people that want to learn and contribute.

[Brandon Chu @BrandonMChu]

Every now dreams every night - processing the day's client and operating activities - to prioritize the top 5 things your biz should focus on next.

We're A/B/C/D testing Argon/Astra/Fable/Wonder to see which delivers the most prescient priorities.

[Bram Sugarman @bram]

@ddebow 🙋‍♂️

[daniel debow @ddebow]

I’m looking for 6 Toronto families to try Torch, the instrument we’re building to make playing and singing easier.

Beginners or lapsed players. Borrow one for Nov; help us learn what makes you keep playing—or stop.

Email me: [email protected]

[GREG ISENBERG @gregisenberg]

I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.

My observations:

1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.

2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.

3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.

4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.

5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.

6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.

They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.

7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.

8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.

Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.

9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.

10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!

11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.

12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.

13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.

14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.

15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.

16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.

17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?

18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.

19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.

20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.

21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.

Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.

Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.

Happy to be home and locked in.

Until next time!

[Scott Stevenson @scottastevenson]

Hiring more product engineers/designers who can code at Spellbook:

* Obsessed with product craft
* Competent at rapidly iterating on prototypes with AI
* Relentlessly focused on solving problems and creating a Tier A user experience

DM me if interested!

[Scott Stevenson @scottastevenson]

Spellbook's Autonomous Contract Management is now in GA!

It's the first end-to-end AI system for contracts.

It reviews before you sit down, drives approvals, storage and longterm insight.

One step closer to building the earth's infrastructure for contracts.

[Satish Kanwar @skanwar]

Our https://t.co/nQ3doKj9hR homepage is now the actual app instead of a video. It gives itself a tour while you watch, or you can click in anytime to drive:

[Zain Manji @ZainManji]

If any founders in Toronto want to play Pickleball, DM me 👀

[Bram Sugarman @bram]

@aliasaria @MitacsCanada Would love to chat.

Congrats on the @transformerlab launch this week!!! Amazing stuff.

[Bram Sugarman @bram]

Honest question: what does @MitacsCanada do?

[Evan Solomon @EvanLSolomon]

Canada's AI advantage starts with our people.

Today, we announced up to $162 million through Mitacs AI Advantage to create 10,000 paid AI-related work placements for young Canadians. At a time when countries around the world are competing for talent, investment and growth, we're investing in the next generation of Canadian innovators.

More opportunity. More skills. More Canadians building the future.

That's how we keep building Canada strong.

🇨🇦

L'avantage du Canada en IA, c’est son talent.

Aujourd'hui, nous avons annoncé jusqu'à 162 millions de dollars pour L'avantage IA de Mitacs, qui créera 10 000 placements professionnels en IA pour les jeunes Canadiennes et Canadiens.
Alors que les pays du monde entier se font concurrence pour attirer les talents, les investissements et la croissance, nous misons sur la relève canadienne en innovation.

Plus d’opportunités. Plus de compétences. Plus de Canadiennes et de Canadiens qui bâtissent l'avenir.

C'est comme ça qu'on continue de bâtir un Canada fort.

[Bram Sugarman @bram]

@gravis821 @YasserElfahli @MitacsCanada Not suggesting it’s a scam. Genuinely trying to understand the details.

[Bram Sugarman @bram]

@marcojaneczek @MitacsCanada Money comes from the government through Mitacs? As the hiring organization, did you pay a fee to Mitacs? Do they receive a fee some other way? Not at all?

[Bram Sugarman @bram]

@myviewpt @MitacsCanada Where was the money from? Government? If government had provided the runway directly to your startup would that have changed anything? Would you have been ineligible for other funding programs?

Also - what’s your startup? Let’s hype it up! Maybe some investors are watching 😉

[Bram Sugarman @bram]

So:
- government commits $16,200 to internship
- student registers w/ Mitacs
- Mitacs calls partner organization, says, “I’ve got an amazing intern candidate, you should hire them”
- partner organization hires student
- student is paid $32,400 (or more?)
- partner organization pays Mitacs a finders fee

Is that right?

[Bram Sugarman @bram]

Awesome to hear you had a positive experience @myviewpt! As a Mitacs Elevate Fellow did you spend the time building your company? Did you work with a partner organization? What were the most helpful actions Mitacs contributed? Were you paid? By who?

*I’m no longer a VP at Shopify. Questions are my own as a supporter of the Canadian ecosystem.