💥 We are thrilled to unveil today our new visual identity and website 💥 tl;dr  Same love for product, more love for product founders and a ...toggle. connectventures.co/

A year ago everyone said LLMs would get commoditized but the opposite happened (Save this). And the reason is something most investors are still underestimating, the model itself was never the moat but the moat is what surrounds it. Sarah Friar laid out the thesis that the agentic layer creates context and memory that compounds with every interaction. Her own Codex instance knows her role, her communication style, her priorities, her family situation because the system has been learning her across hundreds of sessions. That distinction matters enormously for the investment case. A generic model can be replicated by any lab with enough capital, context and memory accumulated over time cannot. The longer an agent operates inside a person's life or a company's workflows, the more it knows that is irreplaceable specific preferences, past decisions, undocumented institutional knowledge, the way a particular CFO thinks about risk versus the way her predecessor did. This is what the enterprise AI race is actually about, not which model scores best on benchmarks which system owns the context graph. Salesforce built an entire agentic memory architecture specifically to solve this. Their work describes the problem precisely, stateless agents that reset at the start of every session fail at enterprise scale because they cannot learn, cannot improve, and cannot be trusted with autonomous long-horizon work. The companies building persistent memory infrastructure episodic memory for past events, semantic memory for accumulated knowledge, procedural memory for learned workflows are building something that becomes more valuable the longer it runs, not less. That compounding dynamic is fundamentally different from every prior generation of enterprise software. A seat license for a CRM tool has the same value on day one as it does on day 1,000 and the software does not know anything new. An agent with deep memory and context integration is worth multiples more on day 1,000 because it has absorbed a thousand days of decisions, corrections, preferences, and outcomes. This is why enterprise CEOs are moving because they can see the switching cost accumulating in real time on the other side of these deployments. The pricing model is changing to match the economics. A16Z and the leading enterprise SaaS analysts have been tracking a shift away from per-seat pricing toward outcome-based models, pay per resolution, per contract closed, per ticket resolved, per line of code shipped. Gartner projects that by 2030, at least 40% of enterprise SaaS spend will shift toward usage, agent, or outcome-based pricing. That is a complete restructuring of the software revenue model.

Is it the most competitive industry out there? Yes. Do we have a radically different take on it? Also yes. Come see what designing in prod feels like: create.dessn.ai/create

No other design tool can "get a view of your app rendered to use as a starting point" because no other design tool runs your production repo like we do This is pretty spot on from Karri😍

Great insights @soleio and fully aligned with the @Dessn_ai thesis

That line hit me: “Transitioning from Sketch to Figma was a no brainer because all of a sudden we went from working in local files to web based collaboration” People frame the current moment as “designers will code now”. I think the bigger story is simpler. We are quietly going back to local again. We already lived through local pain once. In the Photoshop era, a design file was a thing that lived on your machine. Big files, messy versions, “who has the latest” and collaboration felt like passing a large file from person to person. Even later, in a large company, we used Sketch with a semi cloud setup. Basically: shared storage, a versioning workflow, and rules everyone had to learn. We used Abstract for branching and merging. It worked, but it came with onboarding cost. New designers did not just learn the product, they learned the system. UI kits made it heavier. Consistency depended on process. Sync depended on discipline. Prototyping was also split across extra tools. If you wanted “real”, you learned a separate craft: After Effects, Principle, Origami, ProtoPie, or even React with early @Framer. It was doable, but it was not flowing. It was tool switching. Then Figma happened and it was obvious. Not because it was prettier, because it moved the work into shared space. Collaboration became the default, not an add on. AI coding tools are bringing back the same old friction Now designers are building “coded prototypes” with Claude Code, Cursor, and similar tools. They are powerful, but the workflow pulls you into local reality again: repos, env vars, local DBs, running servers, PRs, deployment, and “it works on my machine” That is what the report calls “we’re back in local space” And I agree. The problem is not capability. The problem is location. ⎯⎯⎯⎯⎯ Why I keep reaching for Figma Make? My current workflow at @diffusionhq is simple: we design in @Figma, and if needed, I prototype in Figma Make. Not because it's magically better than Cursor or v0. Because the setup cost is almost zero, and the output is easy to share. Click, prompt, iterate, send a link. That matters more than people admit. I mostly use it for one thing: previewing the experience at true scale, in the browser, at 100% zoom, with real interaction. Since we are building a browser tool, that feedback loop is gold. It helps me catch issues early, make decisions faster, and reduce back and forth before handoff. Big prototypes still take time, sure. But the difference is the collaboration stays online, which keeps the team moving. ⎯⎯⎯⎯⎯ The real “next switch”? Photoshop to Sketch was a productivity jump. Sketch to Figma was a collaboration jump. This next jump will be the same type of collaboration leap, but for coded prototypes. This is not “designers can code now”. It is about keeping design work shareable and close to production. The teams that win will not be the ones with the fanciest local setups. They will be the ones who keep making, testing, and reviewing work in the same shared space. That idea is a big part of how we think at Diffusion. A browser based video editor where work stays shared, friction stays low, and iteration stays fast

We’re excited to announce our investment in Mosaico, an open-source, next-generation data management platform built specifically for robotics. connectventures.co/opinions/…

Boom 💥💥 A huge day for @AikidoSecurity as they announce a $60m Series B and for Connect Ventures as we add another 🦄 to our portfolio. I wrote the full story here: connectventures.co/opinions/…

AI = trajectories. "Fifth, trajectories are the basis for optimizing AI models through reinforcement learning. Smaller specialized models trained on high-value paths replace massive generalists. tomtunguz.com/ai-trajectorie…

"9 months later but 9 times better" We're releasing a deep-dive agent that has access to your entire company data (structured and unstructured) and all the MCP tools connected to a Dust workspace to perform high level tasks on longer time horizons. It has taken over a number of weekly multi-hours tasks that used to be done by humans including our team.weekly meeting preparation as well as aggregation of user testimonials and feedback from call transcripts. I used it last week during a call with an investor to generate a specific retention graph they were asking for, sharing the results before the call was ended instead of going to our data team. Yes it's 9 months after deep research. But this is definitely 9 times better if your goal is to get things done at work. It has access to all our unstructured data presented as a file-system (notion, slack, drive, ...), all our data-warehouse with discoverability capabilities, and all of the MCP servers connected to our Dust workspace. The team has been using it increasingly shifting from custom agents accomplishing tasks to deep-dive based-agents accomplishing outcomes. 7 different technical initiatives went into enabling deep-dive in Dust. We're diving into the detail of each of them in the blog post below. A great read if you're looking to use or build long-time-horizon agents. blog.dust.tt/building-deep-d…

The secret behind Gemini 3? Simple: Improving pre-training & post-training 🤯 Pre-training: Contra the popular belief that scaling is over—which we discussed in our NeurIPS '25 talk with @ilyasut and @quocleix—the team delivered a drastic jump. The delta between 2.5 and 3.0 is as big as we've ever seen. No walls in sight! Post-training: Still a total greenfield. There's lots of room for algorithmic progress and improvement, and 3.0 hasn't been an exception, thanks to our stellar team. Congratulations to the whole team 💙💙💙

Within a year, an AI sales rep will join every human sales rep on every Zoom and call 🤖 95% of “I don’t knows” and “Uh I’ll get back to you” will be gone Within a year, an AI BDR will answer all inbound sales questions 🤖98% of humans setting up appointments with AEs will be gone Within a year, an AI CSM will handle 70% of renewals. 🤖No reason for this to be such a headache and friction-full experience anymore We’re already close on all of this

I'm really happy to say that Plain now powers support for @meetgranola - one of the most remarkable, and most loved productivity tools I've seen in a very long time. Few products go from launch to a daily essential this fast. When you see it as both partner and user, you know it's special. ❤️ Thrilled to work with their wonderful team. They're the most talented, smart and humble people. And yes, they're hiring!

and the best use @detailapp 😘

15/ Want to dive deeper into this thesis? Read the full article here: hugo.pm/p/consumerplus And if you're building in this space, my DMs are open.

So a big theme of 2023 and 2024 was “work-life balance”.  Moderation in growth, in spend, and in time spent on work and the journey.  😄 That a start-up maybe growing 40% but that is profitable might be better than a start-up growing 400% but addicted to venture capital. 🤽‍♀️ That maybe hitting 80% of quota but working 20 hours a week is better than killing yourself to hit 120% of quota and getting that big bonus check. 🏠 That everyone would be just as productive working 20-30 hours a week from home than 40 in the office plus commute time. Well look, 2025 is the year to be honest here.  The ones that work twice as hard, that grow twice as fast, that even … work in the office … are often thriving. And the ones that grew slowly, and in their minds, more sanely for 2 years are now being eclipsed by a new generation, often AI-fueled. It is tough to have balance when the competition is working 6 days a week, 10 hours a day, often half of that in the office. In fact, across the @saastrfund portfolio, I see the top-performing start-ups working harder than ever.  Harder than in my entire career. 2025 is the year to be honest: for most of us in tech, if you want to do great things, there is no balance. There is nothing wrong for looking for an easy job, a 20-hour-a-week role, a fractional life.  Just be honest with yourself where it may lead.  Likely not to a big success. The world is more competitive than ever.  And AI has fueled 10,000 new hyper-competitive start-ups. The lifestyle job and start-up in many cases has become the terminal start-up and career.

Imho: AI (LLMs) replace glue jobs. Most jobs in the corporate world are glue jobs. Glueing things together from different areas. Most are transformations/translations - eg translate from X to Y. This is where LLMs come from and are great at. Take a long book - create a short summary Take 100 emails - create one report Take UI specs - create code out of them Take customer care complain - create answer All of these are glue tasks. Stuff that you have to do to make one system work with another. The scary truth is that in most companies (esp larger ones) most of the jobs are glue jobs. A frontend engineer doing UI is also just doing a glue job.