Dave Copps

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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Nice insight on a fundamentally new capability that AI provides. “You can keep asking for things and making changes and it will never stop responding. This introduces something new in intellectual life, *abundance*. You don’t need to ask for one email, ask for three in different tones to inspire you. You don’t need to ask for one way to complete a sentence, ask for 15 options and see if that unlocks your writing. Don’t ask for 5 ideas, ask for 30.“

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Science-fiction is inherently speculative. Author George #Orwell of ‘#1984’ predicted concepts such as doublethink, little earbuds and society’s obsession with the black mirror screens that dominate our lives — and according to the BBC, novelist and short story author JG Ballard, of “High-Rise” and “Crash” fame, has predicted the current tool and cultural fixation of #GenerativeAI in the modern society — only he didn’t just speculate them within a story, he is actually one of the first use-cases of a generative AI used in a creative context — Ballard was so fascinated with the growing technological advancement of computers that he used one to compose poems, essentially the precursor to what we have now with ChatGPT. Today, the debate about the creative use of generative AI and its value on creative industries is an extremely fraught and controversial topic, but if controlled in a space that doesn’t replace human labor or try to equate itself to traditional art forms, it is interesting to see what computers can do and how they are creatively limited in comparison. Ballard’s two poems, The Yellow Back Novels and Machine Gun City are generated, and both include notes on how Ballard achieved producing them. Regardless of quality (Ballard himself probably wouldn’t describe these two poems as his best work), it is interesting to see primitive versions of the technology that we utilize on the daily today, and how these moral conversations that concern them would continuously evolve.

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Are LLMs better at Deductive (start with a general formula/relationship and compute outputs for different inputs) or Inductive (start with examples of inputs and outputs and figure out the generalized relationship) reasoning.... The answer is they are better at Inductive reasoning... Based on this what kind of applications are they better suited for???? More details in this amazing study....https://lnkd.in/gjKYUraW

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Get ready for one of the biggest IPOs since COVID. Lineage is expected to raise $3.85 billion this month. As Bloomberg put it, "Aside from a handful in 2020 and 2021 when the pandemic disrupted travel, only three companies in the past decade raised more than $1 billion on US exchanges in July." This is big news for the largest and fastest-growing consolidator in cold chain logistics. Kudos to Greg Lehmkuhl, Kevin Marchetti, Adam Forste and all involved! #logistics #supplychain #coldchain #IPO https://lnkd.in/eKj3UjvY

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Just finished reading "The Next Giant Leap" by Space Capital - a pioneering VC investing in the emerging Space Economy. The report speaks to how SpaceX’s Starship is more than just the most powerful rocket ever built—it is poised to reshape the global space economy in profound ways. With its immense payload capacity, reusable design, and lower cost per launch, Starship will unlock entirely new industries and transform existing business models. But how should industries and businesses evolve to align with this paradigm shift? 📈 Rethinking Business Strategies & Supply Chains - For businesses, Starship represents an era of mass-production over precision engineering. This means moving away from the focus on mass efficiency toward cost-effective, redundant, and scalable production. - In terms of supply chains, this opens opportunities for new players in manufacturing, materials, and space components. The demand for off-the-shelf parts and non-aerospace suppliers will rise dramatically. 💡 New Use Cases & Opportunities   The opportunities Starship unlocks go beyond launching satellites. - In-space manufacturing of high-value products like pharmaceuticals and advanced materials that cannot be created on Earth. - Mega-constellations of satellites for global broadband, expanding on what Starlink has already begun. - In-orbit assembly and servicing, allowing for larger space structures like telescopes or even habitats to be built in orbit, radically reducing costs. - Lunar infrastructure and resource extraction will become more viable, paving the way for sustainable human presence on the Moon and, eventually, Mars. 📊 What Should Businesses Do Now?   1. Embrace Redundancy & Scale – Instead of focusing on one-off, high-cost solutions, businesses should embrace redundancy and mass production.    2. Simplify Design – With Starship’s payload capacity, there is less pressure to optimize for mass efficiency. Simplified, scalable designs will allow for faster production and iteration cycles, akin to how SpaceX rapidly tests and evolves Starship itself. 3. Develop Strategic Partnerships – Collaborating with forward-thinking space startups, suppliers, and even non-aerospace sectors will be essential to capitalize on the opportunities Starship provides. 🌌 A New Era of Innovation   Starship will democratize access to space, lowering the barriers for businesses to create and scale. The next decade will witness breakthroughs in scientific exploration, energy production, and in-space transportation that we can only begin to imagine today. #SpaceEconomy #Starship #Innovation #SupplyChain #Manufacturing #SpaceTech #Entrepreneurship #FutureOfSpace https://lnkd.in/gNdbwXdq

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Ever wonder why the best tech doesn't always win in defense? Meeting lots of defense tech founders recently has shown me a hard truth—99.9% of them vastly underestimate defense go-to-market. This isn't a market where the best tech wins by default. Winning in defense requires much more regulatory capture than people realize—-product-led growth (PLG) is essentially non-existent. Traditional tech founders face huge disadvantages when building exclusively for DoD customers. In this field, having cutting-edge technology isn't enough, you need a massively unfair advantage (beyond technology) to succeed here. It's an area where connections, understanding the bureaucracy, and navigating the procurement maze are critical. I don't recommend building for this sector unless you're prepared for these unique challenges.

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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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Legislative processes are boring and a lot of people don't pay attention to laws until they get passed, so I'll apologize in advance for talking about a California bill currently in committee. It's called SB 1047. The bill recently passed the California Senate. To become law, it needs to pass through committee next week, get a vote of support in the Assembly this summer, and then be signed by Newsom. The problem is that SB 1047 is a deeply flawed bill that could kill the AI industry in California. Jeremy Howard, a guy I respect a lot in AI, has written about it here: https://lnkd.in/gcQy6T_g Here are his very good points: - The definition of “covered model” is overly broad and could encompass a wide range of open-source models. - The requirements for developers are overly burdensome and could discourage open-source development. - The potential for civil penalties for AI developers could have a chilling effect on research and innovation. - The ability to levy fees could create a barrier to entry for small businesses. There are better ways to support the responsible development of AI. I hope the tech community will rally to oppose this bill and create something more reasonable in its place.

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This is quite interesting advice and also quite a non-consensus view in the B2B software world. Cross-border founders who are considering the US vs International incorporation question should consider this as valuable input.

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I really enjoyed recording this episode of The Digital Industrial Podcast with Ty Findley. We talked about common misconceptions for deep tech, the "Why Now" for the category, and differences between investor diligence for deep tech and traditional tech. https://lnkd.in/eKtwjauu

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The AI Demo isn’t easy. Many of the major AI companies have demoed their AI systems, first starting with pre-recorded, & now pushing into live demos. They don’t always work. Multiply Murphy’s Law by a non-deterministic system & it’s not unreasonable to expect AI demos to nearly always hiccup. Demo disruptions aren’t disaster. These systems are early & changing rapidly. They might suggest the system requires work & tuning, not a fundamental challenge. But, they can be problematic in proofs-of concept. Proofs of concept are extended demonstrations of the software. Well-structured PoCs align on success criteria at the outset. These criteria enable vendors & customers to agree on what success looks like. Worflow proofs-of-concept are relatively straightforward. They are deterministic. Can I process a loan application in 5 minutes? Yes or no. But as AI applications shift to selling outcomes implicitly or explicitly, the PoC becomes a testing ground of those outcomes. Non-determinism means sometimes the PoC won’t produce the required wow moment. This also means the PoC criteria must be more flexible. How does a buyer evaluate a probabilistic system? Do we compare it to human performance? Speaking to some practitioners, they’ve shared with us human labelers typically agree on 60-70% of the time. Does a AI robot need to be as accurate as a human assuming it will be much less expensive? Or will we expect more as we do in self-driving cars? If AI systems require human assistance, then the ROI of the system must include some human operating expense - whether explicit or implicit. Some teams will want to benchmark systems in parallel to determine the relative performance. With most startups building atop existing models & setting aside differences in fine-tuning, the ultimate performance should be relatively comparable, provided they use the same data sets. Will startups compete on access to different data sets? Today, there are more questions than answers about how to sell AI agent systems. We’re hosting an event on the evening of Sep 10th in San Francisco to interview leaders in the space moderated by Dave Morse, former CRO at Hebbia & VPS/VPCS at ScaleAI to talk about some of these questions. If you’re interested to attend, see the details here : https://lnkd.in/dA8tJEcz

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[part 2 of 2 - cont’d from https://lnkd.in/g8Ar6qGj] ….. A very common pattern I see with “rookie” vs. master” operators is something like this: 🌱𝗥𝗢𝗢𝗞𝗜𝗘 (𝗥):  𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗶𝘀 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗮𝗻𝗱 𝗮𝘁𝘁𝗲𝗺𝗽𝘁𝘀 𝘁𝗼 𝗲𝘅𝗲𝗰𝘂𝘁𝗲 𝗮𝗻𝗱 𝘄𝗼𝗿𝗸 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗲𝗾𝘂𝗮𝗹𝗹𝘆 𝗶𝗻𝘁𝗲𝗻𝘀𝗶𝘁𝘆 𝗮𝗻𝗱 𝗲𝗳𝗳𝗼𝗿𝘁 👑𝗠𝗔𝗦𝗧𝗘𝗥 (𝗠):  𝗜𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲𝗹𝘆 𝗽𝗿𝗶𝗼𝗿𝗶𝘁𝗶𝘇𝗲𝘀 𝘄𝗵𝗮𝘁 𝗶𝘀 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁, 𝘄𝗵𝗮𝘁 𝘄𝗶𝗹𝗹 𝗱𝗶𝘀𝘀𝗼𝗹𝘃𝗲 𝘃𝘀. 𝗱𝗲𝗹𝗲𝗴𝗮𝘁𝗲 𝘃𝘀. 𝗱𝗲𝗳𝗹𝗲𝗰𝘁 ☐ 𝗧𝗼𝗽 𝘀𝗮𝗹𝗲𝘀 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗲𝗿 𝗷𝘂𝘀𝘁 𝗻𝗼𝘁𝗶𝗳𝗶𝗲𝗱 𝘁𝗵𝗲𝘆 𝗮𝗿𝗲 𝗾𝘂𝗶𝘁𝘁𝗶𝗻𝗴  🌱Immediately sets up 1:1 call to spend time saving them 👑Group text to CRO and Head of People asking why they are leaving >> realize it’s not salvageable.  Authorize backfill (via text).  ☐ 𝗜𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗮𝗻𝗰𝗵𝗼𝗿 𝗶𝗻𝘃𝗲𝘀𝘁𝗼𝗿 𝗶𝗻 𝟮 𝗱𝗮𝘆𝘀 𝘁𝗼 𝗰𝗹𝗼𝘀𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗕 - 𝗳𝗶𝗻𝗮𝗹𝗶𝘇𝗶𝗻𝗴 𝗽𝗶𝘁𝗰𝗵 𝗺𝗮𝘁𝗲𝗿𝗶𝗮𝗹𝘀 🌱 Set up 2 hour meeting with FP&A and CEO team to prep, review and polish ahead of the meeting.  Runs the meeting end to end. 👑Set up 30 minute meeting with just FP&A team and asks them to “mock run the meeting”.  Provide feedback and give 30 minutes to make edits and improve.  Set up follow-on 1 hour meeting, kicks off the call by “setting the table”, tells head of FP&A to lead and present.  CEO provides feedback. 30 minute call next day with CEO to go through pitch together. ☐ 𝗖𝗮𝘀𝗵 𝗯𝘂𝗿𝗻 𝗿𝗲𝗽𝗼𝗿𝘁 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘄𝗲𝗲𝗸𝗹𝘆 𝗲𝘅𝗲𝗰 𝘁𝗲𝗮𝗺 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝗶𝘀 𝗼𝘃𝗲𝗿𝗱𝘂𝗲 🌱 Carves out 30 minutes to review and sanity check the cash burn report in their inbox before 12pm 👑Sanity checks the numbers (60 seconds) to make sure nothing looks suspicious. Delegates to head of FP&A to check and publish.  ☐ 𝗧𝗲𝗮𝗺’𝘀 𝗾𝘂𝗮𝗿𝘁𝗲𝗿𝗹𝘆 𝗢𝗞𝗥𝘀, 𝟰 𝘄𝗲𝗲𝗸𝘀 𝗹𝗮𝘁𝗲 𝗻𝗼𝘄 🌱Sets up 1 hour meeting with team to review and another 1 hour to complete self-OKRs 👑Writes to team an apology for dropping the ball, and to ignore this quarter’s OKRs as they’ve gone stale and to focus on next quarter’s, taking into account latest pivot discussed. ☐ 𝟰𝟬𝟬 𝘂𝗻𝗿𝗲𝗮𝗱 𝗲𝗺𝗮𝗶𝗹𝘀 🌱 Attempts inbox zero by end of day 👑Scans emails for family/friend, customers, internal team (in that order) and leaves the rest to triage tomorrow.  ☐ 𝗡𝗮𝗻𝗻𝘆 𝗷𝘂𝘀𝘁 𝗰𝗮𝗹𝗹𝗲𝗱 𝗶𝗻 𝘀𝗶𝗰𝗸, 𝘆𝗼𝘂’𝘃𝗲 𝗴𝗼𝘁 𝗽𝗶𝗰𝗸-𝘂𝗽𝘀 𝗮𝘁 𝗮𝗹𝘁𝗲𝗿𝗻𝗮𝘁𝗶𝗻𝗴 𝗹𝗼𝗰𝗮𝘁𝗶𝗼𝗻𝘀 🌱 Texts four friends and babysitters to see if they can help 👑Fires up the Starlink and goes mobile for the day, swings by Swirl for random Monday fro-yo  treat If you feel “seen”... join us: Operators Guild - the most elite business scalers  - https://hubs.la/Q02q1ty40 OG Next - the future masters of business scaling - https://hubs.la/Q02q1w4d0

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Some unicorns and late-stage VC-backed companies restrict their employees from selling their stock, often at the request of their boards of directors. Not this one. Andrew Feldman and his executive team at Cerebras Systems (a high-flying AI chip startup mounting a challenge to the likes of NVIDIA and Groq) have been willing to recognize the blood, sweat and tears that employees with vested options have put into building this impressive business, by allowing them to sell their shares in the private market. This is not just great news for current and former Cerebras employees but also for the company and its investors! Why? Liquidity typically leads to higher stock pricing, making it more attractive to investors. How so? The short answer is that when a stock is more liquid, its riskiness declines. When risk goes down, so does the discount rate (the cost of capital) that investors use to price the asset. When the discount rate goes down, all else equal, the price should go up! This enhanced liquidity and pricing can help a company with fundraising valuations, consolidating the cap table before an IPO, and bridging the valuation gap between an illiquid private market and a very liquid public market. Congratulations to Andrew and the Cerebras team for delivering on your promise to your employees and investors by allowing a liquid market for your stock. And bravo to your board members (which, according to PitchBook include Eric Vishria of Benchmark , Brad Gerstner of Altimeter, Steve Vassallo of Foundation Capital, Lior Susan of Eclipse, Thomas Laffont of Coatue, and Pierre Lamond) for supporting your vision! #secondarymarket #privatemarket #marketplace #unicorns #stockoptions

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This analysis, at least anecdotally makes sense to me. In many situations, infringe and holdout is the least painful option for large infringers. The system enables it. - The plaintiff must allocate the time and $$ to assert, taking time and $$ away from selling and developing products. Even if they engage litigation financiers they are limiting their upside potential. - The infringer continues to infringe while employing stall tactics or simply relying on the painfully slow, overburdened legal system. - The cost benefit analysis for infringers - "infringe and pay lawyers" typically outweighs the cost of design around or license. - Enhanced damages and injunctions are rare. - Litigation insurance products reduce risk all around. "The Marcum report shows that there is indeed something wrong with the patent system in the United States but it’s not excessive litigation or patent trolls. The real problem is the inability of patent owners to enforce their rights against infringers. Given the difficulty of obtaining injunctive relief and the limited enhancement of damages, it is unsurprising that predatory infringement has become a standard business practice when, at worst, all an infringer can be expected to pay is a reasonable royalty and, at best, nothing at all."

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Whenever a startup ecosystem begins to gain steam and develop, a host of pilot fish swarm around the doers. These sychophants look like consultants/former non-tangential executives, irrevelant service providers looking to reinvent themselves, “advisors,” “independent board members,” “syndicate organizers,” dubious accelerators/studios, placement agents, et. al. It’s the ecosystem’s job to root them out and stop them from taking. If you’re thinking someone or some group could possibly fit this definition, or maybe it’s even you - then you're probably right. Knock it off. The last thing a founder needs is for some asshole in the middle of an existential crisis for relevance to take their precious time or resources.

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Find errors with SUMPRODUCT 👇 Here's the formula: =SUMPRODUCT(--iserror([range])) Note: In the video I incorrectly say "double negative 🙃," but Matthew Herbert of BizTrix pointed out that "the double minus is an operator, not negation. The double minus is the unary operator and it explicitly converts logical values into numbers (true converts to 1 and false converts to 0). ISERROR returns a logical value. This is why SUM(ISERROR(range)) or SUMPRODUCT(ISERROR(range)) returns 0 but placing the unary operator in front of ISERROR returns >0 when there is an error." Even as an experienced user and instructor there's always something new to learn! ~~~ 👋 Hey, I'm Chris Reilly, and I teach Financial Modeling based on real Private Equity and FP&A experience. 𝘱.𝘴. please ♻️ share this with your network if you found it helpful, thanks!

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It’s very hard to get terminal velocity in AI. The pace of AI right now is so fast that disruptors are being disrupted in 9 to 12 months.

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