Okay this one seems real. First time ever an OSS model beats Sonnet 4.6(!!) on our evals. Now begins vibe testing, but this is promising.

the best American open-source model ever just dropped, and it costs less than $1 per million tokens i feel like more people should be talking about this

We raised a $27M Series A to replace the spreadsheets and human duct tape behind $100 trillion in global assets. Fund administration is the invisible backbone of private equity and venture capital - and it’s broken. Why? Financial data is scattered, stale, and locked inside legacy providers. Books take forever to close. Basic questions about your own fund take days to answer. So we rebuilt the general ledger, waterfall engine, investor portal, and portfolio management from scratch. One single source of truth for your firm. Our AI agents read emails, propose journal entries, and extract portfolio updates in seconds. Our CPAs review every output. Today, we administer $15 billion in assets - and we’re just getting started. Every fund CFO keeps getting asked: how will you adopt AI? Now you have an answer. Run your firm in real-time with @hanoverpark. –- Excited to partner with Jake Saper at @emergencecap @peterjhebert at Lux, @chadbyers/@pratyushbuddiga at Susa and CFOs at the largest private equity firms to forge this future.

Public software is down 40% in 12 months. The selloff has been indiscriminate. But it won't stay that way. When the market starts distinguishing again, the winners will be companies whose moats survive a world where agents do the work. We spent Monday at @emergencecap debating which moats actually hold up. Here's the spectrum we landed on. The short version: Seat-based pricing and workflow stickiness were the backbone of the last era. Both are eroding. The next era rewards outcomes, context, and trust. And the most important/enduring moats in the AI era and currently being slept on...

Cognition.com is the strangest redirect I've ever seen. I'd love to know the story here...

(Cross-posting this from LI bc it seems to have hit a nerve, hopefully in a helpful way). ~~~~~ I'm mostly a buy (ideally at the startup phase 😉) and hold type of guy. I often struggle with when to sell, particularly names I've become attached to. Fortunately, AI is a great thought partner, which is why it's become core to my public equities trading approach. The prompt I use is cobbled together from a few others online. I've spent a ton of time testing different approaches and tweaking them for my specific needs. My current version is very long (20 pages), but the results have been strong so far, driving two sell and one hold decision that, so far, look like the right calls. Critically, the prompt addresses one of the major issues with most models: the sycophancy default. They all naturally bend bullish. Tell them you own a stock, and they'll find reasons why it's great. I've tried to mitigate this with aggressive prompt engineering. This includes adding phrases like "You are a senior buy-side equity analyst with a risk-manager mindset and forensic-accounting rigor." For fellow public markets investors using AI: how are you handling the built-in optimism bias? And what prompts have worked for your use cases? Here's a link to the prompt: docs.google.com/document/d/1…

Proud husband 😍 VERY happy wife ☺️

We're in a weird era of AI where amazing things are happening but simple things can still be surprisingly hard. Today, I've been trying to make a market map by uploading a spreadsheet with list of companies, URLs, descriptions, etc. I've tried OpenAI, Anthropic, Bolt, and nothing has been able to render the company logos properly. Ideas?

I had dinner the other night with a fascinating group of folks building AI-enabled products. PMs from companies like @perplexity_ai and @harvey__ai, plus engineers and founders. One question kept coming up: Why haven't incumbent software providers been able to leverage their valuable data assets yet? Both giants like @salesforce and growth-stage companies with 10 years of collected data seem to be struggling to deliver truly differentiated AI experiences. Most of us would assume these companies are sitting on data gold mines that should give them a massive advantage. But is that actually true? One perspective I found compelling: The historical data these companies have collected simply isn't the right kind to make AI models truly effective. The workflow data, outcomes data, or database info they've aggregated over the years might not be as useful as we'd think. What's most valuable in building agentic AI isn't just workflow data or outcomes data—it's a granular understanding of how humans actually perform tasks. This includes all the context switching between applications and even offline activities that traditional software workflows don't capture. The real value is understanding the detailed ontology of how tasks get done so you can replicate that in an agent. AI-native companies are building these task-level datasets from scratch and at the same time incumbents are trying to figure this out—potentially leveling the playing field. The counter perspective: Incumbents aren't failing; they're just slower. Over the next 12-18 months, we'll see them release AI that delivers differentiated insights thanks to this accumulated data. Their data is valuable; they're just taking longer to leverage it effectively. I don't know which view is right, but both offer interesting frames for thinking about the AI landscape and who might win in the long run. Curious what you all think—will incumbents' data moats prove valuable, or are AI-native companies starting on more equal footing than we might assume? Which incumbents are starting to leverage their data most effectively?

This feels like an important read, but avoid it before bed. Living now demands constant cognitive dissonance. If this forecasted world arrives, even if timelines double, how should we make decisions today? ai-2027.com/

Huge congratulations to the entire Logik.ai team on their agreement to be acquired by @servicenow! 👏 This is a well-earned milestone — and a powerful moment for the future of enterprise CPQ. 🧵

47 days ago, @OpenAI announced Operator, an agent that uses its own browser to perform tasks. We’re excited to share that this technology is now available in @unifygtm's AI Agent 🧵

This is one of the best investor focused shows I’ve done in a long time. @emergencecap is one of those brands where if you know VC, you know how good they are. Their track is fricking insane: $2BN Invested : $8BN Returned in Cash Salesforce $275.61BN Veeva Systems $33.04BN Zoom $25.37BN Bill.com $20.5BN Doximity $9.8BN Gusto $9.6BN Chainalysis $8.6BN Box $4.5BN $387 billion (total market cap of Emergence Companies) My 7 key takeaways with @jakesaper 👇