Eva Lau

Collision Conf is one of the biggest tech+AI events in North America and I am looking forward to learning more in this exploding space. If anyone is heading that way, feel free to ping and we can connect! #AI #generativeAI #digitaltransformation

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My 7-year journey via Maple VC with Clay. Thanks Josh Scott at BetaKit for covering the story. I was first introduced to Kareem Amin through a fellow Canadian expat and now Maple founder, Michael Hershfield of Accrue Savings. They first met working at Sailthru in NYC where Kareem sold his first company. In 2016, Kareem started to tinker on new ideas with his cofounder Nicolae Rusan and I was fortunate to meet them at this time. This happened to align with me establishing Maple 1 in November 2016 on the simple thesis of backing Canadians building in America. We noticed that Canadian expats, who graduate from Canadian universities, go on to build some of the biggest companies in tech – Uber, Slack, Instacart, Notion, Faire, Databricks, Cloudflare, Roblox and most recently, OpenAI. Kareem and Nicolae seemed to match this pattern as two graduates from McGill University building in the US. In 2017, they landed on a very clear mission to make programming accessible to all. They painted a picture of replacing the black terminal that developers use, with a spreadsheet that is connected to the internet which could allow non-developers to automate work. What we saw immediately that gave us conviction was a very big ambition (making programming accessible to all) with a unique insight (using a spreadsheet) via a new method of no-code tooling. This reminded us of the early days at Uber: The promise to deliver you anything (big ambition) by starting with a black car that takes you from point A to point B (unique insight) via your phone (new method). We first committed to investing in Clay in Jan 2017 before the company had a lead. In June 2017, we were thrilled to learn that First Round Capital was going to lead their Seed. Sequoia Capital led the Series A in June 2019, which was surprising, since we had no revenue and were still figuring things out. However, the low-code no-code category was starting to take off. Then, they spent the next 3 years figuring out where to focus. During this phase it was fascinating to witness the many disbelievers. We had folks tell us that we should mark this investment to $0. In late 2021, they decided to narrowly focus on automating sales and this turned out to be the big unlock. Varun Anand joined as a cofounder leading their GTM efforts. The timing played out beautifully with new LLMs that we could use to automate growth in a way that hadn’t been done before. Clay relaunched in Feb 2022 with $0 in revenue and has not looked back since. We 10x’d two years in a row, all organically, achieving 100k users, 2500+ customers, and 11k people exchanging ideas in our Slack community. Maple has tripled down since the Seed through the Series B which was just led by Meritech Capital. We have a strong feeling that Clay has a real shot at realizing that vision they first shared with us 7 years ago.

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More startups are betting on AI. Of the record 1,623 startups that exhibited at Collision, a Toronto-based startup conference earlier in June, 20 percent are building AI products. This excludes other Collision startups with AI components in their business, reported the Wall Street Journal. Venture firms are throwing money at generative AI startups. In 2023, investors put $21.8 billion into generative AI deals -- five times more than they did in 2022, according to CB Insights. The average round size for those deals was $51 million, over six times larger than the industry average, CB Insights found. 

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Cohere CEO Aidan Gomez says that #1 blocker for Enterprise GenAI use cases is data privacy and #2 blocker is the cost (implies ROI). He also explained what Canadian AI startup, Cohere does and how they make money. A must watch for anyone who is interested in Enterprise GenAI use cases. He also said that GenAI fear mongering is cheap ways to get attention (I am paraphrasing, he didn’t use the word cheap). Quite a bit commentary on Google Cloud AI developments as well. Other mentions Oracle and Massachusetts Institute of Technology. Aidan comes across as a mature tech leader for his young age. He sounds more realistic and less me too, in GenAI hyped era. Aidan was talking to CNBC’s Steve Kovach. https://lnkd.in/g36SFYfn ____________________ Last month, Cohere has raised $450 million in funding from returning investors such as Nvidia and Salesforce Ventures, as well as new investors including Cisco and Canadian pension fund PSP Investments cc David Vellante David Linthicum John Furrier Ron Miller Shelly DeMotte Kramer Robert Hof

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Today at Antler VC Toronto, we had Michael Kravshik, CPA, CA, the founder of LumiQ come speak to us. The cohort is in week 5 of the 10-week residency. We’re still very much in the process of solidifying our cofounders and searching for product market fit. So Michael’s speech was timely, informative, and inspirational. Here were my key take-aways. 𝐎𝐧 𝐟𝐢𝐧𝐝𝐢𝐧𝐠 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐦𝐚𝐫𝐤𝐞𝐭 𝐟𝐢𝐭  🔸 Ideas don’t matter, the iterative test approach does; it’s more painful in the present but saves you lots of time in the future 🔸 Start with really broad assumptions, start validating those broad ideas, and then through the process of speaking to customers to get to the really narrow concepts 🔸 At one point Michael and his co-founder went through invalidating 25 ideas, which would seem discouraging but they grew and developed a lot through this process (even though they didn’t know it at the time) 🔸 At one point they did a mind-boggling 500 one-on-one interviews in the span of 6 weeks 🔸 The only sustainable advantage you can in a startup is knowing your customer better than anyone else 🔸 PMF is a moving target 🔸 PMF is treated as a verb at LumiQ 𝐀𝐝𝐯𝐢𝐜𝐞 𝐅𝐨𝐫 𝐅𝐨𝐮𝐧𝐝𝐞𝐫𝐬 🔸 Be curious, open-minded, and be willing to kill your babies 🔸 If you keep going, you will eventually create your own luck 🔸 Lots of people get stuck in sunk-cost fallacy land, but you should listen to your gut and pivot when it doesn’t feel right 🔸 Founders have a reputation for being like cockroaches, and rightfully so, shamelessness can take one quite far I also asked Michael during the QA on what his emotionally compelling reason was for overcoming so many challenges to get the company to where it is today, and his answer was really insightful and relatable. He said that 𝐛𝐞𝐢𝐧𝐠 𝐚 𝐟𝐨𝐮𝐧𝐝𝐞𝐫 𝐢𝐬 𝐚 𝐥𝐢𝐟𝐞𝐬𝐭𝐲𝐥𝐞 𝐜𝐡𝐨𝐢𝐜𝐞, 𝐧𝐨𝐭 𝐚 𝐜𝐚𝐫𝐞𝐞𝐫 𝐜𝐡𝐨𝐢𝐜𝐞. Furthermore, if you’re not crazy AND stupid you probably shouldn’t do this! Thanks for bestowing your wisdom on us Michael Kravshik, CPA, CA and I hope I captured your words and sentiments correctly! 😃 #startups

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New experiment and data show how business leaders use generative AI. If you don’t, or doesn’t train your employees on how to use AI properly, they are already less productive but mostly, might may make errors that harm the bottom line.

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I'm at #Collision Conf here in Toronto, and I'm seeing a ton of AI startups. Many of them are asking me how to scale and where to find the right people. It's interesting because the vast majority of these “Ai companies” have never even heard of agentic engineering approaches. The ability to use agents in place of traditional people-based processes is the next step for startups. The next major trend will be billion-dollar startups with just a handful of people. Most of the startups here don't need to worry about hiring a big team. What they need is a core group who understand the basics of agentic engineering. They need the ability to deploy agents within their startup structure, building a company around these autonomous systems that can scale operations, manage finances, drive sales, and handle internet / data infrastructure and cloud computing deployments. With a small, knowledgeable team, you can create and manage these systems, scaling your startup to thousands of agents at a fraction of the cost of hiring a large staff. Sometimes at cost reduction of 1000 to 1. The real question is, where do you find the people who can help you do this? Define their roles and responsibilities clearly. If you can do that, you can build any company. Honestly, half the companies here at the Collision Conference could massively benefit from this approach.

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The AI hype bubble is almost over. When investors refused to fund new AI startups because they know they’re going to fail, then the AI hype bubble is starting to fail. Even the most clueless, Investors now know that this is a bubble waiting to burst like the metaverse bubble and the dotcom bubble. And nobody wants to be left, holding the back on this bubble. The era of free money iis over with interest rates sky high. When big tech companies are losing money on AI and the bigger AI startups are demonstrating that they are all dead and struggling to even have revenue, then no one is interested in funding, smaller AI startups that they don’t believe will be profitable anytime soon. People aren’t waiting for an AI unicorn to break out. They are waiting for Microsoft, Google, Amazon, and similar to start buying distressed AI startups. Collapsing companies and minimal paths to profitability doesn’t help a space that was already overhyped. Don’t forget in all this mess that the unfortunate reality is that engagement and retention don’t match installs and downloads. What I mean by this is the most basic of mobile tracking were ignored. You don’t judge success in the mobile market based on downloads or installs, but rather based on retention and engagement. What this means is, it’s not actually impressive for OpenAI to get to 100 million users quickly if 2% of them retain. The question is not how many people signed up for a free account. The question is how many monthly active users and daily active users do AI startups like OpenAI, Anthropic, Perplexity, Stability, and others have. And how many are paying customers? And what we are finding is that retention is in low single digits. The reality is that AI startups are getting users the way that mobile free to play did. And they haven’t found a way to actually make money on that. Don’t ask an AI startup how many users signed up. Ask them how many DAUs and MAUs they have. Ask them what their ARPDAU is. What you will find is that every number is low. AI startups have no idea how to do monetization, and they don’t have regular active users in any volume. What this points to is that they’re all going to fold or get bought.

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Over the weekend, Tobi, the founder and CEO of Shopify, discussed the major reason investors passed on Shopify in the early days : market size. I remember that financing round, & I remember having the same concern, & making the same mistake. Living in the valley & driving on 101, the billboards & logo-adorned headquarters of successful companies provide a daily infusion of all the mistakes in I’ve made in guessing how a company or a market might evolve. Years later, I listened as Bill Gurley shared his thoughts about market size. He asked himself a question : whether the startup was increasing, decreasing, or maintaining the market size? (I can’t seem to find the link - but I have it in my notes!) I added that question to my diligence list when meeting startups. Now much later, having seen many companies create categories or reinvigorate aging ones, the question I ask myself has evolved. It’s not as the market size large enough? Instead, it the question is : do we believe this company can create the market? Market size is the output of all the players marketing, selling, building. Their efforts alter, distort, & juice the supply/demand curves of macroeconomics. It’s not a given - not an output or a steady-state CAGR. Often it’s the startups that engender the demand, change the market dynamics, & make markets massive by taking risks with products. When done right, these founders create category-defining businesses like Shopify (now worth $90b)!

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great Post from Dr. Benjamin DELSOL: #StrategicThursday 🚀 Is the future of digital payments hidden in Hashtag #QuantumAlgorithms? As an #IPStrategist and Quantum #Physicist, I've seen countless innovations, but the latest breakthrough from Sofia Priazhkina and her team at the Bank of Canada is nothing short of revolutionary. Imagine using #QuantumComputing to untangle the complexities of #DigitalCurrency adoption among firms! Intrigued? Let’s dive in. 💡 Traditional computing falls short when modelling strategic behaviours in vast networks, but quantum computing, with its mind-bending capabilities, offers a tantalizing solution. This paper isn’t just theory - it's practical magic, demonstrating empirical feasibility using real data from the Canadian economy. 📊 For those navigating the world of digital currencies and firm networks, here’s the game-changer: the study uncovers how firms, driven by diverse preferences and costs, make cooperative decisions about digital payment adoption. It reveals a paradox where higher usage might lead to lower adoption rates, a nuance critical for strategists and policymakers. 🔍 The researchers brilliantly simulate these dynamics using quantum algorithmic, highlighting inefficiencies and suggesting that without coordination, we’re looking at a potential 10% under-adoption rate. And there’s more, the empirical simulations show how quantum computing can decode these intricate networks, offering predictive power we couldn’t dream of before. 🌐 As we stand on the brink of a quantum revolution, this study is a beacon. It showcases the transformative potential of quantum technologies in solving real-world problems, from payment networks to beyond. 👏 Hats off to the innovators at Multiverse Computing and the forward-thinkers at the #BankofCanada. This collaboration is proof that with the right tools, we can redefine the landscape of digital payments and firm networks. 🌟 Quantum computing is not just a buzzword; it's the key to unlocking future efficiencies and innovations. Dive into this groundbreaking research and witness the quantum leap in digital payments. ✨Quote of the Day: “From bits to qubits, the future’s bright, quantum algorithms will lead the flight!” by Dr. Benjamin DELSOL😉👍😎🚀 A special "Thank you" to Bob Dameron for sharing this great paper with me!

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Try more things. Fail more often. Learn faster. Burn brighter. Plant more gardens. Launch more startups. Make more babies. Build more community. Help others do the same. This is effective opportunism. Pronoia & Purpose. E/opp is a better meme than Hiis. Just as inspiring, but more secular. Gender neutral vs. male-coded. More conjectural, less prophetic. E/opp is building & growing your mind & body. Building & growing startups. Building & growing families & communities. Effective Opportunism.

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Thanks Gopal Swamy for raising this point. Hasn’t bottoms-up manifestation of Schumpeterian dynamics been the progression for most technology revolutions of the past few decades? The first chapter of my career was a slow, by current standards, execution from below of the stuff we were acclimated to at Carnegie Mellon - GUIs, n-tier architecture, relational databases, email and inter-networking, Unix multiprocessing platforms, object-oriented programming. As we grew in our organizations, we pulled these technologies into our respective institutional firmaments. How can top-down hope to keep pace at a time when what took years to realize is now happening in months - think about how silly private LLMs, earnestly discussed less than a year ago, sound for many use cases in the age of RAG, Langchain and DSPY.

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Learn about AI with me 🤖 This week in my UVic Applications of AI in Business class, we explored how machines learn. But what does machine learning actually mean? 3 Types of Machine Learning: 1. Supervised: Machines learn from labeled data. Think training a model with tons of labeled images of dogs so they can recognize them in a crowd 2. Unsupervised: Machines identify patterns in unlabeled data, like analyzing sales data to find customer types  3. Reinforcement: Machines learn through trial and error with a reward system We also chatted about deep learning, which is a subset of ML modeled after the human brain, and layers complex machine learning algorithms (called neural networks) to process data, like recognizing images 🧠 Challenges? Lack of transparency and explainability. An AI predicting tuberculosis linked its results to the manufacture date of the X-ray machine instead of the content of the patients’ scans, assuming older X-rays machines meant higher poverty and thus higher TB rates 🏥🔍. These complex machine learning programs could also create a barrier for smaller businesses who might not have crazy amounts of perfectly structured and labeled data, which has me thinking about how to make these exciting new technologies equally accessible. On that note, Trudeau was recently talking about democratizing AI in a recent podcast with the New York Times (below!). #AIInBusiness #MachineLearning #DeepLearning #TechInnovation https://lnkd.in/g_RiwQZg

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Just released! Episode 5 of the North of Patient Podcast: Ken Liu - the humanistic implications of AI on health, purpose, work and creativity You can watch on Substack or on major podcast directories (links in Comments below). 👇 So this week, I'm excited to introduce my guest, Ken Liu. Ken is an award-winning American author known for his #speculativefiction. He’s the creative mind behind the "Dandelion Dynasty", a silkpunk epic fantasy series that kicks off with "The Grace of Kings". (note: Ken is credited with coining the term - silkpunk - which I define in episode blog as a subgenre of speculative fiction). Ken has even penned a #StarWars novel! Before he was a full-time writer, Ken did everything from software engineering to corporate law. On top of this, he’s a sought-after thought leader on topics ranging from futurism and machine-augmented creativity to the history of technology, bookmaking, and even the mathematics of origami. So, how does Ken fit into a #healthcare podcast? It all started when I came across his short story “Good Stories”, (as part of the Creative Destruction Lab's AI reading club) which delves into the role of creativity in an AI-dominated world and touches on the yearning for human-generated art. This story sparked my thoughts on the future of #empathy in healthcare, particularly from #AI versus human clinicians, which I've discussed in another blog post. Ken’s insights are a powerful reminder that as we weave more AI into healthcare, we need to ensure these technologies bolster our human values and enhance care outcomes. This episode is essential for anyone keen on the future intersections of technology, storytelling, and human-centered care. Don’t miss out on this intriguing discussion. Key stamps from our conversation are below: 02:34 Journey to Becoming a Speculative Fiction Author 05:27 The Shift to Homeschooling and Parenting Philosophy 09:33 Exploring the Value of Education and Individuality 11:31 The Role of Higher Education and the Quest for Meaning 12:41 Paternalism in healthcare 14:23 Personal Reflections on Career Paths and Storytelling 15:31 On meandering and purpose 21:52 Redefining Passion and Career in a Changing World 25:01 The Future of Work and Inventing New Roles 27:39 Healthcare, AI, and the Human Element 29:39 Why do we have doctors to begin with? 32:51 How do we value human creation? 34:51 The Value of Creativity: Human vs. Machine 37:27 Exploring Empathy: Human Touch vs. Machine Efficiency 41:11 The Role of Empathy in Healthcare: A Deep Dive 49:55 AI and Creativity: Can Machines Surpass Human Artistry? 01:05:41 The Future of Healthcare: AI's Role in Prevention and Care 01:10:26 Closing Thoughts on Healthcare and Technology Have a listen and feel free to share questions, thoughts or feedback!

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Following her visit to Toronto, Audrey Tang's book tour continued to meet her co-author E. Glen Weyl in Boston where, among other events they were hosted by Beth Simone Noveck of the Institute for Experiential AI at Northeastern University, MIT GOV/LAB and The Burnes Center for Social Change. This recent great piece by Beth highlights a few of the technologies I hope to integrated in leveraging AI to buttress civic engagement and democracy: "Artificial Intelligence (AI) is not just transforming economies and industries; it holds the key to revolutionizing public engagement with the federal government. The potential for AI to bridge communication gaps, streamline feedback, and foster inclusion is immense, yet unrealized. [...] While traditional methods of public participation are valuable, they are often limited by scale, speed, and accessibility. People might talk but it is very difficult for either the agency or the public to hear them because of the volume of comments. Integrating AI can overcome these limitations, offering real-time analysis and broader inclusivity, particularly for underrepresented communities. The guidance should be amended to require that the CAIO consults regularly with the public and use AI to make those consultations more effective and equitable. There is a wide array of tools already in use by other governments. Over 500 governments use the Citizen Lab platform, which incorporates AI to cluster, group and organize public comments. AI is much faster than humans at making sense of large quantities of text and can automate the process of summarizing what people are saying, classifying submissions by topic and sorting them into categories to make it easier for governments and the public to read. Your Priorities, a free, open source tool for public engagement from Citizens Foundation and used in thousands of online engagements globally, uses AI to scan incoming postings for toxicity. AI automates the process of spotting offensive speech that violates terms of service and community norms, bringing such language to the attention of moderators and reducing the cost of organizing engagements. Your Priorities uses AI to offer a “chatbot” so that the public and government organizers can get answers to questions such as “show me all the comments on Topic X” or “summarize the best comments on Topic Y.” At Massachusetts Institute of Technology, Deb Roy’s Cortico uses machine learning to synthesize comments in live, face-to-face discussions. Remesh leverages artificial intelligence to rapidly make sense of answers to live questions in online sessions, making it possible to extract insights even from spoken conversations such as with communities that are low literacy. AI translation tools like Facebook’s open source SeamlessM4T make it possible to translate writing into 100 languages and speech into 35 languages, creating avenues for non-native English speakers to participate in conversations on a level playing field."

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🚀 Just had an incredible experience meeting Keily Blair, CEO of OnlyFans in Toronto! Here's a mind-blowing stat: OnlyFans has paid out $15 billion to creators since 2016. Yes, billion with a B. 💸 The company takes 1/5th of the payments generated. With over 3 million content creators on the platform, it was fascinating to discuss the future direction of the business - which really has a ripple effect... OnlyFans has already shifted culture and how we think about creators. But their next big wave goes beyond content and culture—it’s about challenging traditional banking norms. With its massive financial footprint, OnlyFans is pretty well poised to reshape the financial landscape for lawful, profitable businesses often marginalized due to stigma. (👋 Shoutout to Sextech School founders, Women of SexTech, and other businesses that struggle to transact or even open business bank accounts.) Keily’s background as a partner at a law firm specializing in cyber, privacy, and security makes her the fit to take on the big banks. I love learning about how people found their ways into these sorts of spaces - for her, OF was a former client (and a fav client). I learned so much in our meeting. One last nugget that stood out: OFTV (their safe-for-work streamer) just licensed their first reality show to Netflix! At this point, the mainstream influence of OF, whether it's in entertainment or financial sector, is undeniably having a hand in what our future looks like.

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Canada might be 1/10th the size of the US population, but Canadians still deploy capital into startups, but at 1/25th the rate of the US. If you're a Canadian founder you should meet some notable Toronto venture capitalists, ready to back your journey! 1. Relay Ventures-ventures Focus: Pre-Seed and Seed Stage Ventures Sector: Urbantech, Proptech, Fintech, Sportstech Top Portcos: Boomerang, Bird, Ecobee 2. Real Ventures Focus: Early-Stage Ventures Sector: Tech Enabled Businesses Top Portcos: Mejuri, Integrate AI, Unbounce 3. Radical Ventures Focus: Early Stage Ventures Sector: Artificial Intelligence, Disruptive tech Top Portcos: BirchAI, RoadBotics, Nabla Bio 4. Golden Ventures Focus: Seed Ventures Sector: Agnostic Top Portcos: ApplyBoard, Yesware, tacked 5. Alate Partners Sector: Real Estate, PropTech Top Portcos: Bird, Altrio, PadSplit 6. iGan Partners Focus: Seed Ventures Sector: Digital Health and MedTech Top Portcos: Meta, Akira, Think Research 7. Whitecap Venture Partners Focus: Seed and Series A Ventures Sector: B2B software and Med Tech Top Portcos: GoBolt, Real Matters, Clearwater 8. Ripple Ventures Sector: B2B Software Top Portcos: Wisely, Artemis, Plannuh 9. Loyal VC Sector: Tech Enabled Businesses Top Portcos: MyTM, ZestIOT, Avant Meats, BetaStore 10. Portage Focus: Seed to Series C Ventures Sector: Fintech Top Portcos: KOHO Financial, Albert, League, Clark Ready to connect with the perfect investor for your startup? Drop “LIST” in the comments, and I’ll tailor a personalized investor lineup just for you. Who’s your favorite VC firm in Toronto? Give them a shoutout in the comments! At Thunder.vc, we specialize in matching your fintech startup with venture capital firms that align with your sector, growth stage, and goals. Let’s find the right partner to fuel your growth!

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We often talk about the strength of AI research in Montreal, but when I’m asked about what makes it special I like to answer: the culture. Our culture is clearly different from the culture of Silicon Valley, or New York, or London. Yet like all these places we have a critical mass of brilliant AI researchers. Is our culture better? I don’t know, but it’s different — and nurturing differences is important. When we get together we don’t talk about AGI or tech-based solutions to the world’s problems. We talk about fishing and board games and how AI is still pretty bad at navigating the difficult social situations we, humans, face every day. More generally, I feel a very humane undertone to every small group discussion with AI researchers we have here. It resonates with me a lot more than many of the discourses I hear from the Valley. Again, it’s not “the right culture”. It’s still dominated by a few ideas, coming from people that are far from representing the world’s diversity… but it’s a culture that craves the company of other cultures and that quality alone makes it a group I’m proud to be contributing to.

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