I have been writing blog posts for well over a decade, first at Making Things Out of Nothing, and then here at allensthoughts.com on WordPress.
I moved to WordPress because I wanted more control. WordPress is open source. I can host it myself. I can DIY. I am not at the mercy of any one platform.
But, like everything, there is a tradeoff.
Running my own WordPress site means I also need to invest time and effort to keep it modern, secure, and working well. And when time is my most expensive commodity, I have to admit that Allen’s Thoughts could have been better maintained.
More importantly, I want to spend my time sharing my thoughts, not maintaining the plumbing.
We are super excited to share our investment in Tiptree Systems!
Founded in Montreal by two Mila AI researchers, Dr. Martin Weiss and Nasim Rahaman, Tiptree is building an AI-native researcher and knowledge network: infrastructure for how AI researchers share, exchange, and advance knowledge.
There is an irony here. AI has advanced remarkably quickly, yet until now, there has not really been an AI-native way for AI researchers themselves to share knowledge and build on one another’s work. Tiptree is solving that problem.
This is exactly the kind of company that fits our thesis at Two Small Fish. We invest in technologies that can reshape large-scale behaviour, enabled by foundational shifts in computing. The collapsing cost of intelligence is changing not only how software is built but also how work gets done. It is also changing how knowledge can be organized, explored, and shared.
We are super excited to share that Two Small Fish led YScope’s US$3.9 million financing, with Snow Angels (the Snowflake alumni investment syndicate), Next Wave NYC, UTEST, and other successful founders participating.
YScope was cofounded by University of Toronto Professor Ding Yuan, who is also CEO, Professor Michael Stumm, Dr. Kirk Rodrigues, Dr. David Lion, Yu (Jack) Luo, and Beverly Xu (Guangji Xu). It is a deeply impressive team building open-source logging infrastructure for the AI era, combining deep systems research with real-world production traction.
Its core technology, CLP (Compressed Log Processor), makes log storage, search, and analytics dramatically more efficient for both humans and AI, across cloud and edge environments.
We believe this is a massive opportunity. As the cost of intelligence collapses, AI agents, robots, autonomous vehicles, and other intelligent systems will generate orders of magnitude more machine-generated events. A robotic finger moves. A self-driving car makes a slight turn. An AI agent retries a task. Each action creates an event, and the infrastructure layer that can handle that explosion efficiently will matter enormously.
YScope is also a strong mutual fit for TSF. We invest in the next frontier of computing and its applications, and we know firsthand how painful logging becomes at scale. I have spent enough time with logs that I will never get back. At Wattpad, logging every tap, swipe, and click could easily add up to billions of events a day. That is why YScope’s traction is so compelling, from powering Uber’s production logging platform to operating across more than 1.5 million connected electric vehicles and being used by Fortune 500 organizations.
Congrats to Ding, Michael, Kirk, David, Jack, Beverly, and the entire YScope team. Full blog post here.
OpenClaw, an AI agent that can operate a computer on your behalf, has taken the world by storm. Unless you have been living under a rock, you have probably either tried it already or at least wanted to find out what all the buzz is about.
Many, however, have failed to get past installation because it is so difficult. There is a reason why thousands of people lined up for help just to get OpenClaw installed on their machines. More importantly, using it without proper safeguards can create a real security risk.
From my perspective, three issues stand out in OpenClaw’s current form.
First, it is difficult to install, even for technical users. That matters more than many builders realize. A product does not become broadly useful simply because it is powerful. It becomes useful when people can actually get it running without friction or handholding.
Second, it can create a real security risk if not used properly. Tools that operate at the machine level can be compelling, but they also introduce a very different level of responsibility. Most users do not want to expose their full machine environment just to perform a simple task.
Third, it can become expensive quickly. Token bills can become material before users even realize it. A tool may look impressive in a demo, but if the economics do not work, adoption will eventually stall. In AI, performance matters, but efficiency matters just as much.
This is why, after looking at many options, I chose to use Crate from our portfolio company, Gensee, myself, and I believe it is by far the best way to try OpenClaw.
It addresses all three issues directly: one-click install in 60 seconds, a secure sandbox that only accesses what you explicitly allow, and deep expertise from Dr. Shengqi Zhu and award-winning operating systems expert Professor Yiying Zhang, whose work on agentic optimization and efficiency is exactly what makes this possible. That expertise is also why they have been able to make Crate completely free to use.
In other words, it makes OpenClaw easy, safe, and completely free.
There is also a bonus. Crate comes with Gensee’s proprietary AI search engine built in. That search engine ranked #1 on Source Bench for finding the highest-quality web sources.
Another bonus is that Crate comes pre-installed with a set of common, useful skills vetted by the Gensee team for safety, while still allowing users to install additional skills themselves. That makes it both easier to get started and more flexible over time.
A final bonus is flexible control. Users can create multiple instances, pause and resume them, take snapshots, and roll back at any time. That means full control without the usual complexity.
So Gensee Crate is not just an easier and safer way to use OpenClaw. It is also a better one, and that points to where this market is going. The first wave of a technology shows what is possible; the next wave makes it practical for mainstream users. AI agents are now entering that phase. To become part of everyday workflows, they need to be easy to use, safe by design, and efficient enough to be economically viable. That is where adoption happens.
And that is why Gensee Crate is the best way to try out OpenClaw and why it is worth paying attention to.
If you are curious about OpenClaw, try Gensee Crate here.
We need new architectures to meet the speed, security, and energy demands of the next frontier of computing and its applications, which is the lens I used in The Factory Analogy.
Our portfolio company Applied Brain Research (ABR) just achieved a new milestone: ABR announced the successful closure of its oversubscribed seed funding round, including investment from TSF as a lead investor, with Eva Lau joining the board.
ABR created and patented a new type of AI model, called state space models, to make AI smaller, faster, and more energy efficient than transformer models. State space models deliver real-time voice and time series intelligence without the cloud, built for privacy and efficiency. ABR’s first chip, TSP1, delivers real-time, fully on-device voice AI without the cloud. Full vocabulary speech-to-text and text-to-speech are now possible at under 30mW.
At the edge, every millisecond and every milliwatt count.
For context:
30mW is 100× less than a 3W LED lightbulb.
A data-center GPU lives in a different universe: an NVIDIA H200 NVL is up to 600W.
Now connect that to the three constraints that define the edge:
Speed: for voice and interaction, half a second is half a second too late. Cloud voice is “a terrible experience,” plagued by delays.
Security: shipping voice data to the cloud bakes in privacy risk by default — which is why we keep coming back to intelligence that stays close to the user, as Brandon argued in his post In Favour of Intelligence That Stays Put. ABR calls out “privacy concerns” as a core issue with cloud voice.
Energy: edge devices are constrained by battery life and on-device resources. ABR’s on-device voice numbers move this from “interesting” to “deployable.”
This is why ABR enables numerous new use cases that weren’t viable before in categories like AR, robotics, wearables, medical devices, and automotive.
Imagine AR glasses (or other wearables) that respond to your command in real time without draining the battery. Imagine a robot that reacts with no hesitation. Imagine a medical device that can provide insight securely, without exporting sensitive data. Imagine a car that can respond to voice commands even when the network is unreliable. These are just a few examples. The list can go on and on.
Or as Eva put it in ABR’s announcement: sophisticated voice AI doesn’t require the cloud.
The Laus have some exciting news to share. We are making a $2 million donation to the University of Toronto, our alma mater, to launch the Eva and Allen Lau Commercialization Catalyst Prize for Computing & Engineering Innovation.
And the best part: U of T’s Faculty of Arts & Science and Faculty of Applied Science & Engineering will match our gift, doubling its impact. This partnership will give even more researchers the resources they need to take their inventions and turn them into impactful companies.
Why This Matters
Canada is full of world class talent. At U of T alone, researchers are pushing the boundaries of semiconductors, AI, robotics, and quantum technologies—fields that will shape the future. The brilliance is already here.
But getting from invention to an impactful company is not easy. What is often missing are the resources that help move ideas out of the lab: mentorship, funding, workspace, and access to prototyping labs. That is where this prize comes in.
Each year, one team from Arts and Science and one from Engineering will receive support to bridge that critical gap and bring their boldest ideas closer to reality.
Why Catalyst
We named this prize Catalyst for a reason. Commercialization does not happen in isolation. It takes a community of mentors, peers, industry partners, and funders to transform research into companies that scale.
Our hope is that the Catalyst Prize sparks more than just a few startups. We want it to inspire others to join in, to create the conditions where many more homegrown tech giants can start, grow, and scale right here in Canada.
U of T
The University of Toronto is already a leader in innovation and entrepreneurship. It is home to one of the world’s top university incubators, has helped launch more than 1,200 venture backed startups, and is ranked among the top ten universities worldwide for powering innovation .
Add to that U of T’s research depth, industry partnerships, and global alumni network, and you have a powerful engine for turning big ideas into global impact. We are thrilled to help fuel that engine.
Coming Full Circle
For us, this is also personal. U of T gave us our start, Allen in electrical engineering and Eva in industrial engineering, and laid the foundation for everything that followed. From building Wattpad to starting Two Small Fish Ventures, we have lived the journey from idea to scale.
Now, through the Catalyst Prize, and with U of T matching to double the impact, we want to give today’s researchers and students even more opportunities than we had.
Innovation is unpredictable and the path is seldom linear. But with the right support at the right time, sparks can turn into something extraordinary.
That is what the Catalyst Prize is all about, helping Canadian innovators move their ideas out of the lab and into the world.
For nearly 70 years, the home electrical panel has looked the same. Meanwhile, the home itself is transforming: solar on the roof, batteries in the garage, heat pumps, EVs in the driveway, and smart appliances and devices everywhere.
And yet, the panel? Still the same. It is the last dumb box left, and FUTURi is fixing that with deep tech.
FUTURi’s Energy Processor
FUTURi Power, founded by Dr. Martin Ordonez (UBC Professor, Kaiser Chair at UBC, and recipient of the King Charles III Coronation Medal for leadership in clean energy innovation), reimagines the panel as the Energy Processor, a programmable energy computer that finally gives the home’s electrical system a brain. It is designed as a like-for-like replacement for the traditional panel that is future-proof and intelligently measures and coordinates loads, avoids peaks, and manages energy use at the edge.
Why This Matters
Homes are no longer passive energy consumers. They are dynamic nodes in the grid. By making the panel intelligent, FUTURi enables:
For homeowners: Achieve a 100% electric home without costly service upgrades. A smarter, more resilient, and efficient energy ecosystem.
For utilities: Demand peaks flattened, demand response (DR) programs and distributed energy resources (DERs) integrated, deferring costly capital expenditures.
For builders and communities: Intelligent electrification helps accelerate the deployment of built infrastructure without overloading the grid.
This is why FUTURi and utilities are already collaborating on projects to evaluate how Energy Processors can strengthen the grid and benefit customers.
Our Perspective
As Dr. Martin Ordonez, Founder and CEO of FUTURi Power, puts it: “Panels used to be passive. The Energy Processor is active, safe, and software-defined. It gives homes and grids a common language.” At TSF, Smart Energy is one of our five focus areas. Our thesis is simple: the cost of intelligence is collapsing, and the biggest opportunities lie where software and hardware come together to reshape behaviour.
FUTURi is exactly that blueprint for intelligent electrification: deep-tech power electronics plus intelligent control. That combination turns a 70-year-old box into the brain of the modern home. Dr. Ordonez and his team are globally recognized experts in electrification who are translating decades of pioneering research into transformative commercial solutions.
And this is just the beginning. There is so much more the company can do to make electricity truly intelligent. FUTURi has a bright future ahead (pun fully intended).
A few years back, Eva met Dr. Scott Stornetta. Later, I did too. Alongside Dr. Stuart Haber, Scott is widely credited as the creator of blockchain. Blockchain is a technology built on a simple but radical idea at the time: decentralization. No single authority, no central point of control, just a trusted system everyone can rely on.
Now, these two scientists are teaming up again to start a new company, SureMark Digital. Their mission is to bring that same decentralized philosophy to identity and authenticity on the internet, enabling anyone to prove who they are, certify their work, and push back against deepfakes and impersonation. No middlemen. No central gatekeepers.
It took us about 3.141592654 seconds to get excited. We are now proud to be the co-lead investor in SureMark’s first institutional round.
At Two Small Fish, we love backing frontier tech that can reshape large-scale behaviour. SureMark checks every box.
Eva has written a deeper dive on what they are building and why it matters. You can read it here.
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This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
Yet the ocean remains surprisingly underdeveloped — in fact, it’s the least developed.
Land transportation has been electrified. In space, payload costs have dropped drastically. Now, it’s time for marine to catch up.
Unlike cars, you can’t simply add an electric motor and battery to a boat and make it work. Why? One reason is that water’s viscosity is much higher than air, meaning drag or resistance is an order of magnitude greater. As a result, replacing a gas motor with an electric one would require a gigantic battery, making it impractical and, frankly, unusable. That’s why marine electrification has lagged.
Until now.
The “iPhone moment” of marine transportation has arrived. ENVGO’s hydrofoiling NV1 tackles these multidisciplinary complications head-on. Led by successful serial entrepreneur Mike Peasgood, the team brings together expertise in AI, robotics, control systems, computer vision, autonomous systems, and more. Leveraging their prior success as drone pioneers at Aeryon, they are now building a flying robot — on water.
It’s day one of a large-scale transformation of marine transportation. Two Small Fish is privileged and super excited to lead this round of funding, alongside our good friends at Garage, who are also participating. We can’t wait to see how ENVGO reimagines the uncharted waters — pun fully intended.
Read our official blog post by our partner Albert here.
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This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
That’s 10 years, 120 months, and 3,653 days (yes, we counted the leap years). What started as a bold experiment in early-stage investing has become a decade-long journey of backing audacious founders building at the edge of what’s possible.
Over the weekend, we wired funds for our 60th first investment. That’s not including the many follow-on cheques we’ve written along the way—if we counted those, the number would be much higher. We’re not naming the company just yet, but like the 59 before it, this one reflects deep conviction. We think it’ll make a splash!
For years, we’ve said we write 5 to 7 new cheques per year. Not because we aim for a quota, but because this is what a power-law portfolio construction strategy naturally produces. In venture, just a few outlier companies drive the vast majority of returns. The trick is to consistently back companies with 100x potential. That’s the focus—not pacing. And yet, the numbers tell their own story: we’ve averaged exactly six new investments a year. Apparently, clarity of focus brings consistency as a byproduct.
We’re now six months into our tenth year, and we’re right on pace.
To the founders we’ve backed: thank you for trusting us at the earliest, riskiest stage.
To those we haven’t met yet: if you’re building deep tech in the next frontier of computing, we’d love to hear from you. We invest globally. If you’ve got a breakthrough, we can help turn it into a product. If you’ve got a product, we can help turn it into a company.
P.S. If you enjoyed this blog post, please take a minute to like, comment, subscribe and share. Thank you for reading!
This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
A solo musician doesn’t need a conductor. Neither does a jazz trio.
But an orchestra? That’s a different story. You need a conductor to coordinate, to make sure all the parts come together.
Same with AI agents. One or two can operate fine on their own. But in a multi-agent setup, the real bottleneck is orchestration.
Yesterday, we announced our investment in GenseeAI. That’s the layer the company is building—the conductor for AI agents, i.e. the missing intelligent optimization layer for AI agents and workflows. Their first product, Cognify, takes AI workflows built with frameworks like LangChain or DSPy and intelligently rewrites them to be 10× faster, cheaper, and more reliable. It’s a bit like “compilation” for AI. Given a high-level workflow, Cognify produces a tuned, executable version optimized for production. Their second product, currently under development, goes one step further: a serving layer that continuously optimizes AI agents and workflows at runtime. Think of it as an intelligent “virtual machine” for AI, where the execution of agents and workflows is transparently and “automagically” improved while running.
If you’re building AI systems and want to go from prototype to production with confidence, get in touch with the GenseeAI team.
Read Brandon‘s blog post here or in the following for all the details:
At Two Small Fish, we invest in founders building foundational infrastructure for the AI-native world. We believe one of the most important – yet underdeveloped – layers of this stack is orchestration: how generative AI workflows are built, optimized, and deployed at scale.
Today, building a production-grade genAI app involves far more than calling an LLM. Developers must coordinate multiple steps – prompt chains, tool integrations, memory, RAG, agents – across a fragmented and fast-moving ecosystem and a variety of models. Optimizing this complexity for quality, speed, and cost is often a manual, lengthy process that businesses must navigate before a demo can become a product.
GenseeAI is building the missing optimization layer for AI agents and workflows in an intelligent way. Their first product, Cognify, takes AI workflows built with frameworks like LangChain or DSPy and intelligently rewrites them to be faster, cheaper, and better. It’s a bit like “compilation” for AI: given a high-level workflow, Cognify produces a tuned, executable version optimized for production.
Their second product–currently under development–goes one step further: a serving layer that continuously optimizes AI agents and workflows at runtime. Think of it as an intelligent “virtual machine” for AI: where the execution of agents and workflows is transparently and automatically improved while running.
We believe GenseeAI is a critical unlock for AI’s next phase. Much of today’s genAI development is stuck in prototype purgatory – great demos that fall apart in the real world due to cost overruns, latency, and poor reliability. Gensee helps teams move from “it works” to “it works well, and at scale.”
What drew us to Gensee was not just the elegance of the idea, but the clarity and depth of its execution. The company is led by Yiying Zhang, a UC San Diego professor with a strong track record in systems infrastructure research, and Shengqi Zhu, an engineering leader who has built and scaled AI systems at Google. Together, they bring a rare blend of academic rigor and hands-on experience in deploying large-scale infrastructure. In early benchmarks, Cognify delivered up to 10× cost reductions and 2× quality improvements – all automatically. Their roadmap – including fully automated optimization, enterprise integrations, and a registry of reusable “optimization tricks” – shows ambition to become the default runtime for generative AI.
As the AI stack matures, we believe Gensee will become a foundational layer for organizations deploying intelligent systems. It’s the kind of infrastructure that quietly powers the AI apps we’ll all use – and we’re proud to support them on that journey. If you’re building AI systems and want to go from prototype to production with confidence, get in touch with the team at GenseeAI.
This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
Fibra is developing smart underwear embedded with proprietory textile-based sensors for seamless, non-invasive monitoring of previously untapped vital biomarkers. Their innovative technology provides continuous, accurate health insights—all within the comfort of everyday clothing. Learning from user data, it then provides personalized insights, helping women track, plan, and optimize their reproductive health with ease. This AI-driven approach enhances the precision and effectiveness of health monitoring, empowering users with actionable information tailored to their unique needs.
Fibra has already collected millions of data points with its product, further strengthening its AI capabilities and improving the accuracy of its health insights. While Fibra’s initial focus is female fertility tracking, its platform has the potential to expand into broader areas of women’s health, including pregnancy detection/monitoring, menopause, detection of STDs and cervical cancer and many more, fundamentally transforming how we monitor and understand our bodies.
Perfect Founder-Market Fit
Fibra was founded by Parnian Majd, an exceptional leader in biomedical innovation. She holds a Master of Engineering in Biomedical Engineering from the University of Toronto and a Bachelor’s degree in Biomedical Engineering from TMU. Her achievements have been widely recognized, including being an EY Women in Tech Award recipient, a Rogers Women Empowerment Award finalist for Innovation, and more.
We are thrilled to support Parnian and the Fibra team as they push the boundaries of AI-driven smart textiles and health monitoring. We are entering a golden age of deep-tech innovation and software-hardware convergence—a space we are excited to champion at Two Small Fish Ventures.
Stay tuned as Fibra advances its mission to empower women through cutting-edge health technology.
This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
The Two Small Fish team is thrilled to announce our investment in Hepzibah AI, a new venture founded by Untether AI’s co-founders, serial entrepreneurs Martin Snelgrove and Raymond Chik, along with David Lynch and Taneem Ahmed. Their mission is to bring next-generation, energy-efficient AI inference technologies to market, transforming how AI compute is integrated into everything from consumer electronics to industrial systems. We are proud to be the lead investor in this round, and I will be joining as a board observer to support Hepzibah AI as they build the future of AI inference.
The Vision Behind Hepzibah AI
Hepzibah AI is built on the breakthrough energy-efficient AI inference compute architecture pioneered at Untether AI—but takes it even further. In addition to pushing performance/power harder, it can handle training loads like distillation, and it provides supercomputer-style networking on-chip. Their business model focuses on providing IP and core designs that chipmakers can incorporate into their system-on-chip designs. Rather than manufacturing AI chips themselves, Hepzibah AI will license its advanced AI inference IP for integration into a wide variety of devices and products.
Hepzibah AI’s tagline, “Extreme Full-stack AI: from models to metals,” perfectly encapsulates their vision. They are tackling AI from the highest levels of software optimization down to the most fundamental aspects of hardware architecture, ensuring that AI inference is not only more powerful but also dramatically more efficient.
Why does this matter? AI is rapidly becoming as indispensable as the CPU has been for the past few decades. Today, many modern chips, especially system-on-chip (SoC) devices, include a CPU or MCU core, and increasingly, those same chips will require AI capabilities to keep up with the growing demand for smarter, more efficient processing.
This approach allows Hepzibah AI to focus on programmability and adaptable hardware configurations, ensuring they stay ahead of the rapidly evolving AI landscape. By providing best-in-class AI inference IP, Hepzibah AI is in a prime position to capture this massive opportunity.
An Exceptional Founding Team
Martin Snelgrove and Raymond Chik are luminaries in this space—I’ve known them for decades. David Lynch and Taneem Ahmed also bring deep industry expertise, having spent years building and commercializing cutting-edge silicon and software products.
Their collective experience in this rapidly expanding, soon-to-be ubiquitous industry makes investing in Hepzibah AI a clear choice. We can’t wait to see what they accomplish next.
P.S. You may notice that the logo is a curled skunk. I’d like to highlight that the skunk’s eyes are zeros from the MNIST dataset. 🙂
This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
We are thrilled to announce that Albert Chen is joining Two Small Fish as a Venture Partner!
Albert brings a wealth of experience to our team. Like all our partners at TSF, Albert’s expertise spans the full spectrum—from technical innovation to product development to operational leadership in entrepreneurial startups. His impressive academic background further underscores his exceptional capabilities.
Albert earned his Ph.D. in BioMEMS, Acoustics, and Medical Engineering from the University of Waterloo. He also completed his undergraduate studies in Systems Design Engineering at Waterloo and participated in an international exchange program in Electrical Engineering at National Taiwan University.
Albert’s professional career is equally remarkable. Most notably, he served as the CTO of robotics and edge AI company Forcen. His diverse experience also includes roles at Metergy (smart energy), Excelitas (photonics), and North (smart glass, acquired by Google).
This is just a glimpse of Albert’s impressive journey. Follow him on LinkedIn to learn more about his background and accomplishments.
At Two Small Fish Ventures, we are committed to supporting bold founders shaping the future of technology through our experience. Albert’s extensive academic background, combined with his hands-on leadership and innovation experience, makes him an invaluable addition to our team.
Please join us in welcoming Albert to Two Small Fish!
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
The next frontier of AI lies at the edge — where data is generated. By moving AI toward the edge, we unlock real-time, efficient, and privacy-focused processing, opening the door to a wave of new opportunities. One of our most recent investments, Applied Brain Research (ABR), is leading this revolution by bringing “cloud-level” AI capabilities to edge devices.
Why is this important? Billions of power-constrained devices require substantial AI processing. Many of these devices operate offline (e.g., drones, medical devices, and industrial equipment), have access only to unreliable, slow, or high-latency networks (e.g., wearables and smart glasses), or must process data streams in real time (e.g., autonomous vehicles). Due to insufficient on-device capability, the only solution today is to send data to the cloud — a suboptimal or outright infeasible approach.
How does ABR solve this? ABR’s groundbreaking technology addresses these challenges by delivering “cloud-sized” high-performance AI on compact, ultra-low-power devices. This shift is transforming industries such as consumer electronics, healthcare, automotive, and a range of industrial applications, where latency, reliability, energy efficiency, and localized intelligence are essential.
What is ABR’s secret sauce? ABR’s unique approach is rooted in computational neuroscience. Co-founded by Dr. Chris Eliasmith, CTO and Head of the University of Waterloo’s Computational Neuroscience Research Group, ABR leverages a brain-inspired invention called the Legendre Memory Unit (LMU), which was invented by Dr. Eliasmith and his team of researchers. LMUs are provably optimal for compressing time-series data—like voice, video, sensor data, and bio-signals—enabling significant reductions in memory usage. Running the
LMU on ABR’s unique processor architecture has created a breakthrough that “kills three birds with one stone” by:
1. Increasing performance,
2. Reducing power consumption by up to 200x, and
3. Cutting costs by 10x.
This is further turbocharged by ABR’s AI toolchain, which enables customers to deploy solutions in weeks instead of months. Time is money, and ABR’s technology allows for advanced on-device functions—like natural language processing—without relying on the cloud. This unlocks entirely new use cases and possibilities.
At the helm of ABR is Kevin Conley, the CEO and a former CTO of SanDisk, alongside Dr. Chris Eliasmith. Together, they bring exceptionally strong leadership across both hardware and software domains—a rare but powerful combination that gives ABR a significant competitive advantage.
ABR’s vision aligns perfectly with our investment thesis and our belief that edge computing and software-hardware convergence represent the next frontier of opportunity in computing. We’re excited to see ABR power billions of devices in the years to come.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
When it comes to watches, my go-to is a Fitbit. It may not be the most common choice, but I value practicality, especially when not having to recharge daily is a necessity to me. My Fitbit lasts about 4 to 5 days—decent, but still not perfect.
Now, imagine if we could extend that battery life to a month or even a year. The freedom and convenience would be incredible. Considering the immense computing demands of modern smartwatches, this might sound far-fetched. But that’s where our portfolio company, Blumind, comes into play.
Blumind’s ultra-low power, always-on, real-time, offline AI chip holds the potential to redefine how we think about battery life and device efficiency. This advancement enables edge computing with extended battery life, potentially lasting years – not a typo – instead of days. Products powered by Blumind can transform user behaviours and empower businesses and individuals to unlock new and impactful value (see our thesis).
Blumind’s secret lies in its brain-inspired, all-analog chip design. The human brain is renowned for its energy-efficient computing abilities. Unlike most modern chips that rely on digital systems and require continuous digital-to-analog and analog-to-digital conversions (which drain power), Blumind’s approach emulates the brain’s seamless analog processing. This unique architecture makes it perfect for power-sensitive AI applications, resulting in chips that could be up to 1000 times more energy-efficient than conventional chips, making them ideal for edge computing.
Blumind’s breakthrough technology has practical and wide-ranging applications. Here are just a few use cases:
• Always-on Keyword Detection: Integrates into various devices for continuous voice activation without excessive power usage.
• Rapid Image Recognition: Supports always-on visual wake word detection for applications such as access control, enhancing human-device interaction with real-time responses.
• Time-Series Data Processing: Processes data streams with exceptional speed for real-time analysis in areas like predictive maintenance, health monitoring, and weather forecasting.
These capabilities unlock new possibilities across multiple industries, including wearables, smart home technology, security, agriculture, medical, smart mobility, and even military and aerospace.
A few weeks ago, I visited Blumind’s team at their ventureLAB office and got an up-close look at their BM110 chip, now in its third tapeout. Blumind exemplifies the future of semiconductor startups through its fabless model, which significantly lowers the initial infrastructure costs associated with traditional semiconductor companies. With resources like ventureLAB supporting them, Blumind has managed to innovate with remarkable efficiency and sustainability. (I’ll share more about the fabless model in an upcoming post.)
I’m thrilled to see where Blumind’s journey leads and how its groundbreaking technology will transform daily life and reshape multiple industries. When devices can go years without needing a recharge instead of mere hours, that’s nothing short of game-changing.
Image: Close-up view of BM110. It is a piece of art!
Image: Qualification in action. Note that BM110 (lower-left corner) is tiny and space-efficient.
Image: The Blumind team is working hard at their ventureLAB office. More on this in a separate blog post here.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
We’re thrilled to share that Printify, a company we have proudly backed since its first funding round, has entered into a merger with Printful (see report by TechCrunch). As long-time supporters of the Printify team, we at Two Small Fish Ventures are incredibly happy with this outcome, which marks a significant milestone in the production-on-demand industry and an exciting moment for everyone involved.
Printify and Printful are both leading platforms that empower entrepreneurs and businesses to create and sell custom products worldwide without the need to hold inventory, thanks to their advanced production-on-demand fulfillment networks. Printify has been growing rapidly, now boasting a team of over 700 employees. Combined with Printful’s team, the newly merged company will have well over 2,000 employees, making it by far the number one player in the production-on-demand market.
Printful, with over $130 million raised and a valuation exceeding $1 billion, and Printify, backed by $54.1 million in funding, have established themselves as the top two global leaders in this field. This merger solidifies their position as the dominant force in the industry, setting new standards and driving innovation in production-on-demand services worldwide. We’re proud to have supported Printify from the very beginning and look forward to witnessing the next chapter in their remarkable journey.
P.S. In true spirit of unity, founders Lauris Liberts and James Berdigans have sealed the deal by swapping T-shirts with each other’s logos—because nothing says “teamwork” like wearing the competition’s brand!
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
Today’s blog post is brought to you by Eva Lau. She will talk about one of our recent investments: Axiomatic AI.
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Congratulations to Axiomatic on their recent US$6M seed round led by Kleiner Perkins! Two Small Fish Ventures is thrilled to be an early investor since the company’s inception—and the only Canadian investor—in what promises to be a game-changer in solving fundamental problems in physics, electronics, and engineering.
Why is this important? Large Language Models (LLMs) excel at languages (as their name suggests) but struggle with logic. That’s why AI can write poetry but struggles with math, as LLMs mainly rely on ‘pattern-matching’ rather than ‘reasoning.’
This is where Axiomatic steps in. The company’s secret sauce is its new AI model called Automated Interpretable Reasoning (AIR), which combines advances in reinforcement learning, LLMs, and world models. Axiomatic’s mission is to create software and algorithms that not only automate processes but also provide clear, understandable insights to fuel innovation and research, ultimately solving real-world problems in engineering and other industrial applications.
The startup is the brainchild of world-renowned professors from MIT, the University of Toronto, and The Institute of Photonic Sciences (ICFO) in Barcelona. The team includes leading engineers, physicists, and computer science experts.
With its innovative models, the startup fits squarely within our fund’s focus: the next frontier of computing and its applications. As all TSF partners are engineers, product experts, and recent operators, we are uniquely positioned to understand the potential of Axiomatic and support the team.
Axiomatic’s new AIR model is well-positioned to accelerate engineering and scientific discovery, boosting productivity by orders of magnitude in the coming years, and ultimately make the world’s information intelligible.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
Co-founded by AI and reinforcement learning luminaries Rich Sutton, Randy Goebel and Joseph Modayil, Openmind is a Canadian non-profit focused on conducting fundamental AI research to better understand minds.
We believe the greatest advancements in AI are yet to come. Basic research is essential to understanding what is scientifically possible before pursuing the next generation of commercial and technological developments.
A key aspect of Openmind is its commitment to open research. Openmind places no intellectual property restrictions on its research, allowing everyone to contribute to and build upon this shared knowledge.
As a board member, I will leverage my decades-long experience in building, operating, and investing in AI companies to support Openmind’s mission. Supporting innovation is one of my life’s passions, and I am thrilled to accept this position and join a team dedicated to pioneering advancements in AI.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
We’re thrilled to share that Toronto-based Viggle AI, a Canadian start-up revolutionizing character animation through generative AI, has raised US$19 million in funding. The round was led by a16z with Two Small Fish participating as a significant investor. As part of the investment, I also became an advisor to the company.
Creators are unleashing their creativity with Viggle AI by generating some of the most entertaining memes and videos online. You’ve probably seen a clip of Joaquin Phoenix’s Joker persona replacing recreating Lil Yachy’s walkout from the Summer Smash Festival – it was made with Viggle AI!
But Viggle AI is much more than a simple meme generator. It’s a powerful platform that can completely reinvent how games, animation, and other videos are produced.
Powered by JST-1, the first-in-the-world 3D-video foundation model with actual physics understanding, Viggle AI can make any character move as you want. Its unique AI model can generate high-quality, realistic, physics-based 3D animations and videos from either static images or text prompts.
For professional animation engineers, game designers, and VFX artists, this is game-changing. Viggle AI can streamline the ideation and pre-production process allowing them to focus on their creative vision and ultimately reduce production timelines.
And, for content creators and everyday users, Viggle AI can generate high-quality animations using simple prompts to create engaging animated character videos within a matter of minutes.
Easier. Faster. Cheaper. Viggle AI is a truly transformative product that will unlock new values for consumers and professionals alike.
Here are a couple of fun examples of Viggle AI in action – I was terrible at dancing, but now I can do it!
Since launching in March, Viggle AI has taken the internet by storm and now boasts over 4 million users. When the startup first landed on our radar it only had 1000s of users. This rapid growth is not only a testament to Viggle AI’s ability to create an engaging product but also Two Small Fish’s ability to spot tech giants in the making.
Two Small Fish has an unparalleled track record of helping create the future of content through technology. After all, the team built Wattpad from a simple app for fiction into a massive global entertainment powerhouse with 100 million users. Seeing the future is our superpower. We’re the best investors to help future tech giants like Viggle AI as they transform how content is created, remixed, customized, consumed, and interacted with. We’re excited to continue to play a role in reinventing content creation and entertainment.
Congratulations Hang Chu and the entire Viggle AI team!
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
I haven’t been involved with Wattpad for a while now, so it’s a strange feeling—though not in a bad way—to catch up on all the details about WEBTOON and Wattpad in the SEC filing. From what I’ve gathered, WEBTOON is performing exceptionally well, with revenue now surpassing $1 billion.
Three years ago, one of the main reasons I was drawn to Naver WEBTOON among all the suitors was Naver’s intention to spin out WEBTOON, together with Wattpad, as a separate, entertainment-focused, NASDAQ-listed company. This was a significant undertaking with numerous challenges, and the WEBTOON team is delivering on the promise. I’m pleased to see that Wattpad is playing a crucial role in this upcoming IPO.
The timing has turned out to be ideal for both WEBTOON and myself personally. With the rise of generative AI, the media industry is undergoing a new wave of massive disruption. It’s exciting to see WEBTOON raising more capital to seize this opportunity. From a distance, I wish the WEBTOON team all the best!
At Two Small Fish Ventures, we’re equally excited as we witness many incredible AI-native media startups and are actively investing in several amazing ones. I’ll share more about this in future posts.
This is a once-in-a-decade, platform-shift opportunity. It is arguably the biggest platform shift in the past century! TSF is actively investing in the next frontier of computing and its applications as a lead investor or as part of a syndicate. If you’re a founder of an early-stage AI-native company—media or not—don’t hesitate to reach out to us, as TSF is a rare investor who understands this space extremely well, and possibly the best investor with real-world operating experience who can help you achieve massive success like Wattpad did.
P.S. This blog is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, even commercially, as long as appropriate credit is given.
