Matt Trifiro

A SAFE and convertible note had a baby and Wilson Sonsini Goodrich & Rosati was the midwife- out popped the D-SAFE! 👶🏽🎉 A new #climatetech funding instrument was announced by Elemental Excelerator last week. One of the company’s main goals is to support smaller, up and coming companies during the awkward “Scale Gap” phase between #VC funding and natural growth. It’s called the #DSAFE (Development Simple Agreement for Future Equity) and it aims to aid the process by giving companies a small amount of cash to overcome initial risks and expenses during the developmental stages of a product launch or company. How does it work? D-SAFE has a redemption clause meaning companies can choose to have their payments offered as a loan or convert to equity at the investor’s discretion. Funders and companies will have clear communication and expectations about where the money is going and how it will be applied to either the company or the projects at hand. Financiers and companies will work together with more #transparency, and that’s a great boon for #startups and VC alike. As of this month, Elemental has already invested using D-SAFE in eight different companies, including Dimensional Energy and Origen — both companies investing in innovative and out-of-the-box solutions to environmental issues! Seems like a win-win on the surface. What do you all think about the D-SAFE? Alfredo Coppola Momoka Ueda Tim Wagner Stephen D. Torres Olya Irzak Natsuho Toyama Noriya Tarutani Shin Ogawa

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I got a preview of this talk. It's going to be good. If you think we've reached the end of the gen AI opportunity, then you should listen to what Roman Shaposhnik and Tanya Dadasheva have to share about the future of transformer architecture for AI.

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Check out my recent post on why President Trump is the forcing function for us to solve the “Missing Middle” and catch the industrial Tsunami of onshoring coming to the US!

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"United Independents, the group behind the Independent National Convention set for Denver in September, has launched a new software platform for indie candidates called Electify.us, which came out of a partnership with incubator Wolfram Labs — the first tool to emerge from its program." I am so proud to share that Chief Product Officer Dana Cichon has launched the first political tech platform to come out of our accelerator program. What are you waiting for? We have the formula for breaking into the digital campaign space. Are you are the next success story? Read more in Campaigns & Elections -->> https://lnkd.in/eHhqa-Xr #techaccelerator #startups #incubator #VC #SaaS #software #political #campaigns

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We're launching the inaugural edition of Beyond Benchmarks, a deep dive into the metrics and trends observed across the early-stage enterprise cloud market. This is the first look at some of the business data around GenAI in SaaS. Some initial observations: 1) 60% of SaaS companies have already integrated GenAI into their offerings (60% of these as part of their current product and 40% as a new product), and an additional 20% are planning to do so this year. Almost half aren't monetizing these features. Those that are are primarily experimenting with both usage-based and flat fee pricing models. 2) Most companies are utilizing OpenAI as their primary LLM, but many are experimenting with multiple models - and we are noticing an early trend towards intelligently routing GenAI inference requests to different models based on cost, performance, and security. 3) Though we are in the initial innings of GenAI, we are seeing some positive signals - companies that implemented GenAI had 7% higher NDR than those that did not. Still early days, but we'll be doing this benchmarking report regularly to track and share how things progress in this new era. Link to report in comments.

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For an example of just how entitled the Big Tech culture has become -- and those who work for them, see their reaction to the CA legislation that holds LLM bot companies liable for the catastrophes they cause. Who else should be responsible? Well, they apparently think the government should provide a backstop for catastrophes (large and small) they create, presumably because they are so brilliant, valuable, and too-big-to-fail. I think the author of this article, Kelsey Piper, has it right. Companies training LLMs that cost $100 million or more (I would recommend including other metrics) are by definition commercializing a combination of high-risk technology and scale that causes catastrophic risks. Moreover, they are not only monetizing it, they have already created several $trillion in market cap by hyping the hell out of LLMs. So now they want to be immune from liability for their actions? To even suggest it and that some in Congress take it seriously demonstrates how far off the deep end U.S. governance of Big Techs has fallen. They could have gone down a very different path, like we did, and design-in safety and security from inception, but then our path has been much more difficult than the instant wealth created by the scorched earth approach. “If you don’t take reasonable precautions against enabling other people to cause mass harm, by eg failing to install reasonable safeguards in your dangerous products, you do have a ton of liability exposure!” Yale law professor Ketan Ramakrishnan responded to one such post by AI researcher Andrew Ng.

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Why Buying Your First Business is the hardest & Financing The Fear is Being seen as too risky by lenders and investors. The Solution: Prepare a solid business plan with detailed financial projections. Work with a financial advisor to enhance your credibility. Consider alternative financing options like SBA loans or seller financing.

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💡 Interesting takeaways (+ my own commentary) from this #SFClimateWeek session on startups engaging with #utilities - Bits (software) and atoms (hardware) will both be required to power #decarbonization and the #energytransition. Enterprise SaaS is a proven business model that needs to complement innovations in deeptech. Start by targeting the customers most open to moving quickly with new approaches - which might be municipal and coop utilities. Referencing those early customers and successful deployments then helps when you start targeting more risk-averse companies, who have higher expectations and potentially more to lose from new technologies. I've seen the latter play out many times in SaaS markets. Successfully penetrating more mainstream (and conservative) segments of a market requires thoughtfully sequencing and orchestrating product development, partnerships & integrations, go-to-market capabilities, and delivery/service capabilities. Would have loved to be at the session to hear firsthand from Adriana Penuela-Useche Louisa White Page Crahan Andrea Sguazzi Giancarlo Savini.

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AI that can use computers like humans? Anthropic just made it a reality. Anthropic's latest announcement is reshaping the AI landscape: 1. Upgraded Claude 3.5 Sonnet with industry-leading software engineering skills 2. New Claude 3.5 Haiku model matching top-tier performance at lower cost and speed 3. Groundbreaking "computer use" capability allowing AI to navigate interfaces like humans 4. Significant improvements in coding, with Claude 3.5 Sonnet outperforming all publicly available models on SWE-bench Verified 5. Early adoption by tech giants for automating complex, multi-step processes This leap forward in AI capabilities opens up exciting possibilities for automation, software development, and open-ended research tasks. However, it also raises important questions about responsible deployment and potential risks. What are your thoughts on the implications of AI systems that can use computers like humans? How might this change the future of work and technology? #ArtificialIntelligence #TechInnovation #FutureOfWork #AIEthics #SoftwareEngineering Share your insights in the comments!

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Vishal Sachdev highlights the strategic integration of open source and proprietary tech in architecting tech stacks, developer ecosystems and resulting business models. The world class companies effectively balance value commoditization in open source and value capture in proprietary tech. This is the strategic challenge for companies operating in #blockchain and #AI. I believe those who operate at the intersection of blockchain *and* AI stand to win this strategic battle...

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I'm so excited about the problems "traditional" software companies are tackling right now. LLMs let software teams move faster, make sense of larger domains, and built much more complex tech than we were able to even five years ago. I believe the impacts in healthtech, govtech, and fintech will be large. Simultaneously, I'm amazed at all the ways one can lose money investing in AI companies - especially the foundational models. There's no part of this market that isn't trending toward commoditization. This has been true for a while but the release of llama3.1 makes it especially clear: Most workflows can retrain on this open-weight 405B model with no change to their budgets (AWS Bedrock charges *at max* the same as OpenAI) and then find ways to negotiate/optimize that cost way down while not sending their data away to a 3rd party. Even the B2C story is commoditized — what AI wrappers have believable ROI if I can run this one-liner and the hard part is accomplished for a fraction of a cent?: aws bedrock-runtime converse --model-id meta.llama3-1-405b-instruct-v1:0 --messages '[{"role":"user","content":[{"text":"Write me a post enumerating how investments in LLMs may yield no competitive advantage."}]}]' As I look past the flashy parts of AI I see: * Building one-off tools against novel APIs is now (and forever) instant * Analyzing requirements for most tasks and understanding new domains is an order of magnitude faster * GitHub Copilot yearns to write React for us * LLMs give the same boost as pair programming at zero cost I haven't been this excited to build complex products in a long time.

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Emmett Shear called AI chatbots "highly dissociative agreeable neurotics." Never heard it put quite like that before. But he's right. Current AI tries so hard to please that it loses itself. Like a person with no backbone, agreeing with everything you say. Shear saw this problem and instead of just tweeting about it, he built Stem AI to solve it. That's what separates great founders from commentators. His career path is telling: - Built Twitch - Ran OpenAI during its darkest days - Now building tools for AI alignment Each step gave him deeper insights into the problems he's trying to solve now. Reminds me of why I back founders who've lived through the problems they're solving. They see angles others miss. --- Check out my Newsletter - TheFoundersCorner via the Link on my profile page or the Link at the top of the page ☝️ Jam Packed with Actionable Insights Every Week 💙

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DoD is pushing startups to take on venture capital pretty hard these days. VC funding can be a great option but it's not an out-of-the-box great fit for Defense Tech. Relying on private debt to effectively dictate what new tech into Defense agencies will have consequences and I don't want to experience that particular learning curve. #defensetech #emergingtechnology #venturecapital

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Everyone loves the 'picks and shovels' thesis—but the real untapped goldmine? Vertical agents and applications. 💡 As the underlying foundation models continue to advance, there won't be much space to build unicorn businesses that sit between the models on one hand and agentic applications on the other. (Don’t be surprised if GPT-5 is natively agentic in its architecture and capabilities.) The real opportunity lies in massive end markets ripe for "agentization": industries centered on structured, repeatable activities. Industries • Data Management • Customer Support • E-commerce Operations • Finance and Accounting • Marketing • Human Resources • Healthcare Administration • Supply Chain and Logistics • IT and Software Support • Legal and Compliance Digital Jobs • Virtual Assistants • Content Moderation • Market Research • Quality Assurance • Online Tutoring • Social Media Management • Transcription Services • Digital Advertising • Graphic Design • Software Development Support At Mucker Capital, we've built over a hundred companies in vertical SaaS. We see vertical agents as it's next generation.

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Data from the International Energy Agency (IEA) illustrates the current "readiness" of #ClimateTechs + their potential impact on carbon reduction. As shown, many techs are at an early stage of being able to have a material impact. Concerningly, some tech funds recently returned capital back to investors recently + closed their funds - citing inability to fund capital intensive techs, found in areas like ClimateTech. This in part may be contributing to the $2 trillion shortfall in ClimateTech, that Deloitte estimates. What this may mean for the future? - The migration of LPs to global, super VC funds within ClimateTech, given the large capital pools needed to fund tech development - Greater specialization from smaller funds, energizing more niche elements of the ClimateTech market - but also altering their / LP's risk profile - A greater role to be played by debt financing in funding growth entities at an earlier stage - impacting cashflow dynamics Will be an interesting space to watch moving forward. We at The Proptech Connection are constantly working with all parts of the Proptech ecosystem. From the leading LPs, funds, techs and adopters in market, providing an independent view. Reach out to us today to find out how we can help. #InvestorTrends #Strategy #Global Ivo van Breukelen Stuart Daun Kiki C. Ethan Ward

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"Falling costs of building new software companies are a mirage, and the reason for that is this thing in economics called the Jevon's paradox... ...the Jevon's paradox occurs when technological progress increases the efficiency with which a resource is used (reducing the amount necessary for any one use), but the falling cost induces increases in demand enough that resource use overall is increased, rather than reduced." Marc Andreessen with a counterintuitive take on what the future could hold for the software industry. #tech #technology #innovation #software #deeptech #startups #entrepreneurs #entrepreneurship #ai #artificialintelligence #vc #venturecapital #money #business

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Once overlooked but now rapidly gaining traction, GovTech is set for disruption! 🤓💡 I've just published an in-depth overview of the GovTech market. It explores key players, innovative solutions, and market segments that are shaping the future of government operations and public services. 🏛️🚌 From public safety management to civic engagement, procurement, and analytics, the GovTech landscape is brimming with opportunities for both startups and established companies to make a significant impact. 📈💼 https://lnkd.in/gWnn_WMn #GovTech #Innovation #PublicServices #GovernmentOperations #venturecapital #startups

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No surprise here for anyone involved in venturing, but a good overview of the situation and an opportunity to add my perspective. “We’ve raised a lot of money, and we’ve given very little back,” Thomas Laffont, co-founder of investment firm Coatue Management, said at a recent conference. “We are bleeding cash as an industry.” "There are currently more than 1,400 startups valued at $1 billion or more—so-called unicorns—according to a recent presentation from Coatue. All have investors waiting to get rich." “There are companies that are 13, 14, 15 years old. This is beyond any historic standard. And there are over a thousand of them,” said Bill Gurley, a partner at the venture firm Benchmark. There are several contributing factors that led to this situation, and I'll just list a few in order of importance: 1) There was way too much capital raised and invested by the venture capital 'industry' (hint as to the problem), which is best viewed and practiced as a discipline dependent on individual talent. 2) Valuations were falsely elevated due in no small part to excessive VC, and they still haven't corrected (see Gurley's comment -- most of these ventures are still valued far too high). 3) Big Techs became too big and crossed the antitrust line long ago. VC has become increasingly reliant on flipping to Big Techs in particular to achieve attractive ROIs when they should have been focused on building sustainable companies. M&A has always been a significant source of exits / liquidity / and ROI, but it reached 70% of total ROI for VC in the U.S. a couple years ago. If we were seeking to blame, there is plenty to go around. 1) Excessive monetary and fiscal stimulus inflated bubbles in all assets, including VC, and we still haven't seen a correction in most assets. 2) Big Techs have abused market power, engaged in regulatory capture, and generally behaved in a predatory manner, distorting and manipulating markets. 3) VCs had about as much discipline in managing capital as Congress... the level of fund raising in some funds is obscene, and that it's still centralized around SV is inexcusable. It's bad for the economy. 4) Like VCs, too many entrepreneurs became experts in raising capital, rather than building lasting companies. While I agree that the Trump admin will likely be friendlier to M&A, and we do need more of it, I really don't think it will be sufficient to bale out the majority of VC firms and unicorns that haven't marked their valuations to the actual market. Big Techs will likely still have handcuffs, as well they should. That said, we shouldn't punish entrepreneurs and VCs for what Big Techs, Congress and regulators have done. The 3rd and 4th tier in tech need to step up their game in M&A, as should other industries.

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Today's episode features a techno-utopian vision for the future that is facilitated by A.I. advancements, as envisaged by Dario Amodei (the CEO of Anthropic) in his recent, popular 15,000-word blog post. The piece (titled "Machines of Loving Grace") begins by defining "Powerful AI" as Dario's preferred term relative to AGI (Artificial General Intelligence; a term I don't myself mind, especially when clearly defined as, say, Google DeepMind published on earlier this year with their "Levels of AGI" paper). In any event, I think the "Powerful AI" concept is useful and interesting as well so here it is: 1. Exceeds Nobel Prize winner-level intelligence (across, e.g., biology, programming, math, engineering, and writing). 2. Has full virtual work capabilities: text, audio, video, internet access, and system controls. 3. Can work autonomously for extended periods on complex tasks. 4. Operates 10-100x faster than humans. 5. Can be scaled to millions of instances working independently or collaboratively. ...in the end, resulting in the equivalent of "a country full of geniuses... in a data center" 🤯 EXAMPLES OF HOW "POWERFUL AI" COULD TRANSFORM SOCIETY Healthcare: • Compress 100 years of progress into ~10 years on, e.g., cancer treatments and finding cures for genetic diseases • Potential doubling of average human lifespan to ~150 years • Revolutionary mental health treatments Economy: • Up to 20% annual growth in developing nations • Massive poverty reduction globally • Second Green Revolution in agriculture • Climate change solutions Governance: • Enhanced democratic institutions • More efficient government services • Improved fairness of legal systems TIMELINE • "Powerful AI" Could arrive as early as 2026... or could take decades (or maybe not even be feasible at all). • The timeline will be short (closer to 2026) if it turns out to be possible to attain "Powerful AI" through scaling (e.g., orders of magnitude more model parameters and orders of magnitude longer OpenAI o1-style inference times) using current A.I. approaches (e.g., Transformers). REALITY CHECK While superintelligent, "Powerful AI" is not magic; it will still face real-world constraints, e.g.: • Biological experiments take time (you can't conjure up a cancer cure; you have to test candidate therapies on animals and then gradually larger groups of patients). • Hardware manufacturing has physical limits. • Clinical trials and safety testing remain necessary. I'm a techno-optimist myself so Dario Amodei's piece resonated with me. In practice, however, "utopia" is probably fantastical; even a vastly improved society would like still have some dark sides, but A.I. (and other emerging tech like nuclear fusion) could allow us to improve society much more rapidly than before. The "Super Data Science Podcast with Jon Krohn" is available on all major podcasting platforms and a video version is on YouTube. Listen to today's episode (#832) for more detail on all the above!

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