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By Dr. Paul Jurcys (Co-Founder, Prifina) Happy Valentine's Day! On this day when the world celebrates love and connection, I would like to share a few thoughts about a rather new paradigm - our relationships with Personal AI assistants and “AI buddies” that are gradually becoming our daily digital companions at work as well as our private lives. I have no doubt that you have been increasingly relying on AI tools such as ChatGPT, or myriads of other AI assistants that helps solve various tasks, I wonder:
 Generalists vs. Personal AI Assistants
The emergence of Siri, Alexa, and ChatGPT reveals a fascinating landscape of technological advancements tailored to our personal, professional, and intimate lives. At the outset, I believe we should distinguish between “generalist AI assistants” and “Personal AI buddies.” Generalist AI assistants, such as ChatGPT and Alexa, designed to provide a broad range of services without accessing personal data. They excel in offering information, performing tasks like setting alarms, answering general queries, and controlling smart home devices through voice commands. Their strength lies in their versatility and the ability to serve multiple users without customization. On the other end of the spectrum, personal AI assistants take customization to the next level. Such assistant are able to tap into a wealth of your own personal data (e.g., data from wearables like Apple Watch or Oura Ring, your own Google Maps starredlocations, and payment histories from your personal credit card records) to offer highly personalized advice, reminders, and insights. These Personal AI buddies can be playful or very practical. Think of a My Fashion AI advisor that knows the styles I like, the shoes I have, as well as the colors I hate to wear. I can talk to my fashion AI advisor about the outfit I should wear for tonights date with my Valentine. In the wake of shortage of human experts (yes, we will never have enough doctors, coaches, or emotional support friends!), specialized AI assistants are becoming increasingly meaningful. Think of an Personal AI doctor who has access to my hearth rate data and sleep information from my wearables, and is capable to monitor different health metrics, suggest activities based on location and preferences, and predict certain events based on my own biometric data. It is not hard to imagine a personal AI finance advisor that knows my spending patterns and history, and can help manage my finances by tracking spending patterns, and nudging to save more wisely to achieve certain financial goals. Assistants According to Functions Performed Simple Personal Avatars help users represent themselves in virtual environments, such as video conferences or virtual reality platforms. They are designed to mimic the users appearance and mannerisms but do not necessarily require deep personal data access. Digital Twins is a step further in terms of their complexity. Digital twins utilize extensive data to simulate and predict outcomes in various scenarios. For instance, a digital twin could use health data from wearables to forecast potential health issues, enabling preventative care. Conversational Interfaces: AI assistants with conversational interfaces, such as ChatGPT, serve a wide array of problem-solving purposes. They can range from general assistants answering any query to specialized ones like AI nutritionists offering certain healthy diet advice or "intimate buddies" providing emotional support. These AI assistants rely on natural language processing to understand and respond to user inputs meaningfully. AI Agents Performing Tasks: These AI assistants are capable of executing specific tasks on behalf of the user, such as purchasing concert tickets or booking appointments. They might access personal calendars, preferences, and financial information to autonomously carry out tasks, streamlining the user's daily routine and ensuring engagements align with their interests and schedules. The Privacy Paradox in the Age of AI Creating personal AI assistants that genuinely understand and cater to our unique preferences and needs, requires a nuanced approach to data utilization and privacy. Let’s take a very practical illustration: what does it take to build a truly personal travel AI advisor? What specific user-generated data would be needed to build a truly personal travel AI? The answer largely revolves around integrating user’s own data from various sources that could help inform the AI to make truly personalized travel recommendations: Accessing Calendar Data: your personal AI travel advisor could benefit by integrating with your calendar data. This connection could offer insights into your available free time, potential holiday slots, and preferred travel durations. By understanding when you are planning to take time off, the personal travel AI advisor can suggest travel options that align perfectly with your schedule. Leveraging Google Maps Data: Further personalization could be achieved by analyzing your Google Maps data. This could include places you have frequently visited and enjoyed, restaurants that align with your culinary preferences, and even destinations you have marked as "want to visit." Such data could enable the personal AI travel assistant to craft travel suggestions that resonate with your past experiences and future aspirations, making every recommendation feel tailor-made. Incorporating Airbnb Booking History: Additionally, integrating our Airbnb booking history could provide the AI with a deeper understanding of your accommodation preferences and budgetary constraints. By analyzing the types of places you have stayed in the past, the personal AI travel advisor can tailor its accommodation suggestions to match your taste and financial comfort zone, further personalizing the travel planning process. However, the aspiration for such a highly personalized AI travel advisor encounters significant hurdles under current data privacy regulations and the reality of data silos maintained by tech giants. These barriers often prevent the seamless integration and utilization of data across platforms, limiting the potential for truly personalized AI assistants. To overcome these challenges, a radical reimagining of the data ecosystem is necessary, where individuals have sovereignty over their data. In this envisioned future, personal AI assistants operate directly on user’s own data, where data is private-by-default, ensuring personalization does not come at the expense of data privacy. This shift towards this more human-centric and user-controlled data ecosystem not only promises to enhance the personalization of AI services but also represents a critical step towards reconciling the competing interests of data utility and privacy. By placing individuals at the center, we can unlock the full potential of AI in personalizing experiences while safeguarding our right to privacy. Reimagining Consumer Experience in the Age of Personal AI’sIt’s time to recognize that we are rapidly entering an era dominated by personal AI assistants who will be seamlessly integrated into many aspects of our daily lives, including our most intimate spheres. It is also obvious that the way how we interact with apps and digital services is set to undergo profound transformation. Rather than having hundreds of apps, we will have one single portal with personal AI agents that have different specializations, and know us deeply (based on our own data). This transformation beckons us to ponder deeply about the nature of our future interactions with intelligent AI assistants and agents and the implications of such relationships on our emotional well-being and the essence of human connections. How Do We Build Trust with AI Companions? The foundation of any meaningful relationship, whether with humans or AI, hinges on trust. Therefore, understanding and building trust with these synthetic agents becomes paramount. Trust in such Personal AI assistants will not solely be about reliability or accuracy but will also encompass ethical considerations such as privacy, data security, and the AI's decision-making processes. How do we ensure these AI companions act in our best interests, and what mechanisms will be in place to safeguard our autonomy and privacy? Transforming Interactions and the Future of Intimacy The proliferation of AI agents will redefine our interaction with the world and each other. These AI companions could become our confidants, advisors, and caretakers, managing not just our schedules and health but also providing emotional support. The depth of these interactions raises questions about the nature of companionship and the role of AI in fulfilling human needs for connection and empathy. Will these sophisticated AI agents be able to discern and adapt to our emotional states, offering comfort and advice akin to a human friend or therapist? Relying on AI for emotional support and companionship introduces a paradigm shift in the understanding of intimacy. As personal AI assistants become more integrated into our lives, it's crucial to consider how this will shape our perceptions of love, friendship, and emotional support. Will AI companions complement human relationships, offering support where human availability is limited, or might they supplant traditional human connections in some aspects? Implications for Human Connection and Love The advent of emotionally intelligent AI raises profound questions about the future of human connection and love. As we form bonds with AI entities capable of understanding and responding to our emotional needs, it's imperative to reflect on how these relationships will coexist with human-to-human connections. Will the convenience and consistency of AI support erode the value we place on human imperfections and the unpredictable nature of human relationships? Or, conversely, could the presence of AI companions prompt a deeper appreciation for the unique aspects of human connection that AI cannot replicate? The journey into a future with personal AI asisstants invites us to embrace the possibilities and opportunities of improving our daily lives. The vision of such AI-powered future also requires us to critically assess and navigate the challenges that lie ahead. Let us embrace this new reality and work together in shaping the future.
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On September 19-21, 2023, the Prifina team attended the largest frontier tech event of the year in the Bay Area - TechCrunch Disrupt 2023. We had an opportunity to meet new and current partners and showcase some of the use-cases where Prifina’s partners are leveraging Prifina’s personal data platform to offer services on top of users’ own data. In the post below, we summarize some of our main accomplishments during TechCrunch 2023, and also provide some insights about the current trends in the consumer data and AI market.  Prifina's Hub at TechCrunch Expo Hall This year at TechCrunch Disrupt, the Prifina had a booth located in the very center of the expo hall thus becoming a hub of attraction for people who came to see what are the latest developments in frontier tech. At our booth the Prifina team primarily aimed to focus on two areas of our work:
Showcase of sensorized products At Prifina's stand, visitors could see and experience how sensors are added to the physical products we use daily. Specifically, the Prifina team brought real sensorized helmets and headbands produced by our partners Haierling (making sensorized ski boots) and Samphire Neuroscience (producing menstrual neuromodulation devices). Those devices helped us illustrate how Prifina empowers individuals to collect data from various sensors and platforms and build compelling solutions on top of consolidated users' data. In the case of a ski coach, for example, the user not only gets a report about the ski performance but can also talk to a personal AI ski coach about the recovery after an intense ski experience. Such recovery is possible because the user can collect data from various data sources (e.g., ski boot sensor and a sleep tracking device such as Apple Watch or Oura Ring). Showcase of Personal AI Applications As we enter a world where every service we use is transforming into AI-powered assistants, one question remains: how do we make AI assistants really personal to the user? With Prifina's human-centric data infrastructure, it is rather simple to build AI-powered assistants: we need to build the assistant to the user, and tap into the combined of the user. Here is how it looks in practice: assume you want to build a "Personal AI travel agent" app. What data would such an AI assistant need to make recommendations that are truly personal to me? In this case, perhaps it is about: (i) my previous bookings on AirBnb platform, (ii) my starred locations on Google Maps (places I liked and places I marked as to be visited in the future); (iii) obviously, my calendar(s) so that I could plan accordingly, and (iv) perhaps my payments history. In Prifina's architecture, the user downloads the "Travel AI Assistant App" and runs on top of his/her own data, which includes various data sources, also possible is the four mentioned above. Such Travel AI Assistant app runs privately (on top of device, or in my own personal data hub which only I can access). Prifina's human-centric data architecture received much attention; we are onboarding new partners to build applications that run privately on the combined data set that users have in their personal data environments. General Takeaways and Market Trends After spending three days and talking to hundreds of founders, partners and investors, we have the following three takeaways about the market trends: 1. AI everywhere, even where it is not needed There is so much buzz about Artificial Intelligence. There is no question that AI tools and AI solutions are coming to many areas of our work, social life and leisure. AI tools will help curtail the amount of redundant tasks and help people focus on things that matter more, increase efficiency. In fact, this process of AI tools has been already taking place for quite some time, but the emergence of OpenAI's tools such as ChatGPT has made it really obvious. We also observed that the AI hype could get out of control: namely, we see how startup founders and builders are trying to add "AI" even to those areas where it does not make any sense. The result? Clumsy and complex UX, forced functionalities that are not needed. We understand that is a natural process of trial-and-error; we just hope that it will not take too much time and resources. 2. Sensors and Empowerment with Raw Data Similarly to other technology conferences in 2023, we see witnessed the wave of sensors rapidly entering every aspect of your physical lives. From sensorized running shoes, sensitized toilet seats, complex sensorized exoskeletons to sensorized workspaces, sensors and transforming how we live, work and recover. 3. Predictive Analytics for Consumers At TechCrunch we evidenced many fertility, sexual, reproductive, and other solutions that rely on sensor data and provide insights and nudges for consumers. Some of those solutions are diagnostic and clinical focusing on sophisticated biometric data, others are simple consumer-facing apps. Yes, one of the key common thread among sensors and AI-powered apps that offer predictive analytics is the need to adopt a more holistic approach to data. That's where Prifina's human-centric data model for user generated data has received most attention and validation. We are looking forward to continue our work with Prifina's current and new partners to unlock the value from users' data created across platforms and devices. Showcase of sensorized products At Prifina's stand, visitors could see and experience how sensors are added to the physical products we use daily. Specifically, the Prifina team brought real sensorized helmets and headbands produced by our partners Haierling (making sensorized ski boots) and Samphire Neuroscience (producing menstrual neuromodulation devices). Those devices helped us illustrate how Prifina empowers individuals to collect data from various sensors and platforms and build compelling solutions on top of consolidated users' data. In the case of a ski coach, for example, the user not only gets a report about the ski performance but can also talk to a personal AI ski coach about the recovery after an intense ski experience. Such recovery is possible because the user can collect data from various data sources (e.g., ski boot sensor and a sleep tracking device such as Apple Watch or Oura Ring). Showcase of Personal AI Applications As we enter the world where every service we use is transforming into AI-powered assistants, one question remains: how do we make AI assistants really personal to the user? With Prifina's human-centric data infrastructure, it is rather simple to build AI-powered assistants: we need to build the assistant to the user, and tap into the combined of the user. Here is how it looks in practice: assume you want to build a "Personal AI travel agent" app. What data would such an AI assistant need to make recommendations that are truly personal to me? In this case, perhaps it is about: (i) my previous bookings on AirBnb platform, (ii) my starred locations on Google Maps (places I liked and places I marked as to be visited in the future); (iii) obviously, my calendar(s) so that I could plan accordingly, and (iv) perhaps my payments history. In Prifina's architecture, the user downloads the "Travel AI Assistant App" and runs on top of his/her own data, which includes various data sources, also possible is the four mentioned above. Such Travel AI Assistant app runs privately (on top of device, or in my own personal data hub which only I can access). Prifina's human-centric data architecture received much attention; we are onboarding new partners to build applications that run privately on the combined data set that users have in their personal data environments. “Whoever wins the personal agent, that’s [going to be] the big thing. Because you will never go to a search site again, you’ll never go to Amazon again” - Bill Gates By dr. Paul Jurcys In the very near future, each of us will our personal AI assistants for every aspect of our lives. Each of us will have our personal AI doctor, personal AI coach, personal AI nutritionist, or digital assistant that will help us organize our calendars, schedule events, and purchase items on our behalf. Such AI-powered assistants will augment our abilities and help get rid of time-consuming tasks, and focus on things that matter most.
 Buzz In the Market In the past few months, there has been quite a bit of excitement about the possibility of building truly personal AI:
A massive investment in InflectionAI could be seen as an attempt to stave off any potential competition and secure dominance in this rapidly emerging domain. Shortage of Experts Today, we are unfortunately facing a remarkable shortage of experts. With over 8 billion people on Earth, there are a mere 10 million doctors. Teachers struggle to spend enough time on direct interactions with every student in the class. On a personal level, we often lack someone equally knowledgeable or passionate about our favorite topics. In times of grief, suffering, and emotional pain, the desire for a close companion to provide advice and understanding becomes ever more acute. This is where the transformative power of AI comes in. In the near future, countless individuals, potentially everyone, will be able to augment their personal capabilities with our own personal AI assistants. The trajectory toward universal access to intelligence is clearly mapped out: we can expect that any person with access to the internet and a hand-held device will have access to the same expert advice from a doctor, an educator, or any other specialist, all through their personal AI. 2 Ways to Build Personal AI As technology continues to leap forward at ever-increasing speed, personal AI assistants are evolving to become more sophisticated and versatile. There are two paths of bringing AI superpowers to humans: A top-down approach: after the release of ChatGPT and other generative AI tools, we are witnessing an influx of various personal AI assistants that are supposed to hello us find information, summarize text, or generate images. The top-down approach is rooted in creating software applications - personal AI assistants - that are marketed as tools to help us solve various daily tasks and assignments or offer us a companion, a buddy that is available 24/7. Think of InflectionAI’s Pi, Midjourney, Dalle, and countless others. However, most of our essential tasks are rooted in the physical world: we need to eat, sleep, and and go to the bathroom. As cheap sensors are being integrated in our wearable devices and environments around us, individuals have tools and ability to track personal biological clock - we start living quantified lives. If you are a runner, you probably have a fitness tracker to measure your running distance and heart rate, as well as recovery rate. Also, our cars track hundreds of data points (e.g., acceleration, braking, engine performance, and outside factors). Scientists around the world are building assistive robots to help us to recover from injuries or learn important skills. These sensor-derived data sets also lead to the development of AI-powered applications that help optimize different aspects of our lives. This sensor-data-AI path resembles a bottom-up approach to building personal AI. Data Eats the World, But How to Access Data? AI assistants such as ChatGPT, or PI, are amazing in answering our general questions and performing general tasks. However, if you ask anything personal, something related to yourself, they stumble. Let’s see what responses we get to the question “How did I sleep last night?”:  How come!? Isn’t it odd that if you wear Apple watches that track our activity, sleep and process various biometric data, Siri - an AI assistant built by Apple for Apple’s products - is not able to answer such a simple question?! Eventually, every company building personal AI will face the challenge of connecting the personal AI to the user’s own data. To build a truly personal AI, we need not only general knowledge from publicly available sources and research repositories, we also need to bring this general, publicly available knowledge and correlate it with the specific data of each individual. In other words, to build a truly personal AI, Ai assistant must be able to access to the user’s personal data. Otherwise, the AI assistant will be a generalist, not a personal:  This is where Prifina’s human-centric approach to data comes into play: with Prifina’s user-held data model, it is possible to bring those personal AI assistants to each individual and run those personal AI assistants “on top of” each user’s own data, privately. In Prifina’s personal data ecosystem, each individual user connects their data from personal data sources to their own data “vault”, where data is collected and unified. AI-powered apps and assistants run in each individual users’ data environment. Each individual user’s data is private by default. A human-centric approach to data opens vast opportunities for the use of personal data: not only personal AI assistants can answer the question about last night’s sleep, but ML and LLMs can offer new possibilities for personalization and automation of tasks and generate new value for individuals. Multi-Agent Interface So how many personal AI assistants can there be? And how many AI assistants can one person handle? As humans, we have limited time and a limited attention span. Our guess is that each of us will be really interacting with 5, 7 or 10 digital agents (similar to a manager at a corporation who has 7-10 directly responding employees). The image below illustrates the universe of digital assistants in the human-centric environment:
 These digital agents will also vary in their expertise, capabilities, and proximity to the individual human being. It is likely, that people will have their own AI doctors, AI coaches, and AI-powered shopping assistants who will help to achieve specialized tasks.
Some of them will operate in the “inner orbit” - on top of the user’s own data, privately, while other AI assistants will be generalists, and will not have access to user’s own data. In this multi-agent environment, one possible scenario is that each individual will have one primary and preferred assistant - we can call it “my personal AI.” But how will those digital agents and assistants interact with one another? What technological infrastructure is needed to make them talk to one another? Assume you feel unwell and suffer from an upset stomach, which personal AI assistant do you go first? You might go to your “own private AI” and ask “Why do I feel pain in my stomach”? Your own personal AI may refer you to your personal AI doctor. In this kind of scenario, it will be important to make sure that the initial inquiry made by you to your own personal AI is automatically transmitted to other agents. This will ensure that your AI doctor will already know your concern and will be able to continue the conversation. Such communication between AI-powered agents can only be possible in the human-centric data environment, where all the AI experts run on the user’s side. Sharing sensitive data outside of the user’s own personal data environment is not optimal. In fact, such communication between agents would be impossible in the old, enterprise-centric data ecosystem where data is locked in separate silos. Paths Forward As we stand on the entrance gates to the age of AI, the future seems quite exciting: how will our lives change when each of us will be able to tap into the potential of our Personal AI? The integration of Personal AI agents into our daily lives is not a question of if, but when and how. To bring this vision into reality, a shift in thinking is required. We need to embrace a human-centric data paradigm where our Private AI is operating on our side, on our own data, privately. At Prifina we are building an infrastructure to empower each individual with such “steamengines of the mind”; we want to empower developers to build Personal AI applications for people to augment each individual’s creative potential. Join us! Prifina’s CEO Markus Lampinen spoke to Alex Bond on the podcast about the current trends with personal data and how individualized data-driven innovation could be introduced to the insurance services market. TLDR
 Markus: My name is Markus Lampinen, I'm based in San Francisco where I run a company Prifina. We help make products smart. We do this by embedding sensors into different types of physical products. For example, I'm wearing - not that you can see it on the podcast - a really nice kimono-based silk jacket produced by a Tokyo-based company Ichijiku. This jacket has an embedded sensor that can measure a number of different things around, e.g., how the jacket is doing, surrounding data like humidity, etc. Our role at Prifina is to overlay different types of datasets like the sensor data and correlating them with other types of data, e.g., your heart rate when you wear the jacket. Prifina allows companies to build different types of applications that enable them reimagine the end-user experience for how we interact with not only physical products but data. Our vision is that in the future, have more data than ever - how can we use it to create new experiences? Something that's different from what we've had? Alex: That's great. I think that when people hear about sensor-driven technology in clothing, they think about performance, sportswear. But it's much more than that, isn't it? Markus: That's right. We work with pro athletes that want to improve their performance, e.g., top basketball clubs. For them, it's about understanding performance, recovery, sleep, stress, hormonal activities, and so on. From a player’s point of view, how can players themselves understand what's going on with their body and their environment to essentially get better? But it's not just about performance sports, it's also about stress - how do you manage stress? There is a lot of data that we already have, e.g., our sleep data. We can go all the way to, for example, luxury fashion and thinking about how does the clothing that you wear impact your your mood or happiness? As for all this data, we're kind of sitting on this goldmine: we, individuals, have so much data but we're just not using it. It's a huge opportunity. With user-generated data, it is possible to build new types of experiences. Alex: We're now talking at the MENA Insurance Summit. How does this relate to, for example, insurers? Markus: We work with a lot of insurance companies around the world are looking look at preventative health, mental health and wellness. If you can make your policyholders healthier and happier, then they're going to be better customers for the insurance company, because they have very similar incentives as the insurance company does. Alex: Okay, applications are really wide for the insurance industry. … Markus, you explained that it's not just about kind of tracking the data, but it is about the lifetime value of a product that is enhanced by your own data.  Markus Lampinen: Indeed, the lifetime value is one important aspect of a product. How are you maintaining your car? Are you driving it too fast, and so on and so forth. But then, we get into this very delicate balance as well: the data should benefit you as an individual, not the insurance company alone. At the end of the day, for example, when it comes to different types of collectibles, tracing the entire value chain (where everything came from, who actually owns it, etc.) becomes important, especially for maintaining the value and also secondary transactions. This fits into a broader conversation about what is authentic, especially in the digital environments powered by applied AI and deep fakes. We have Barack Obama thats now trying to sell you some skin care product. How do you know that something is authentic? But our focus is very much on reimagining the whole of the consumer experience using data. There are incredible applications as we move forward. For example, cars generate a lot of data. So do you as an individual. What if you could just enter into the car which already knows that, am I a five star driver today? Or that I am only a three star driver today? If you slept really poorly of if you had a little bit more than one drink…? We're not talking about sharing it anywhere, but using your own data for your own benefit. That’s where there's this interplay between all the data that we have and all the things around us. How can we tie them into a unique importable experience going forward? It's a very, very big theme, of course, but that's kind of something that we look at as an inevitable trend. Alex Bond: I think people are just starting to wake up to be protective of that. I’d like to finish with one more question: what does it mean for insurance? I'm an insurance guy. What about applications for the insurance sector? Markus Lampinen: Sure. Insurance emerged as a very clear secondary market for personal data use-cases. Let's take an example. After today’s summit here in Doha, I'm traveling to Switzerland where we're working with our partner that has a sensor embedded into both the left and the right ski boot as well as the helmet. So you can actually get your triangulate the skier and their position to maybe help them improve. Now, performance athletes may care about the angle of tilt on their 760 turn on the mountain. But most skiers don't. But if you start essentially hitting this type of data on skiers, could you create individualized insurance per skier per day in dynamic conditions? Could you give them incentives to ski safer based on those? These are some things that are currently really hard to price dynamically. But if you started having this type of data, could he actually create almost like reverse marketplaces for these types of products? Could you do this on, let's say, a resort by resort basis? You can also use it as, let's say recommendations for routes on different types of ski resorts. You can also use it for insurance products as well. So that's been very much our foray into thinking about starting off in a very, very clear value prop for the individual, but then realizing that there's this entire ecosystem. Alex Bond: Markus, you've been very generous with your time. Thanks for being a guest. You find the entire conversation on , here it is.
In the heart of Bern, Switzerland, an innovation and future conference - The 2291 Festival - recently provided a platform for Prifina to showcase its remarkable progress in collaboration with its Swiss partner companies. This event brought together visionaries, industry leaders, and venture capital firms, highlighting the transformative potential in the areas of sensors, personal data and AI. Prifina, along with Heierling, known for their high-end ski boots and equipment, took center stage at the event. We unveiled next-generation of ski boots and helmets that collect skiing data and seamlessly integrate it into their personal data cloud provided by Prifina. Users can then access applications that leverage this data, such as an AI ski coach, providing personalized insights and recommendations.  Markus Lampinen, Hans-Martin from Heierling, and Jouko Ahvenainen Markus Lampinen and Jouko Ahvenainen took the stage to deliver thought-provoking talks during the event. Their focus centered on the fundamental question of personal data ownership and control, emphasizing that it is not only about owning the data but also enabling ordinary individuals to access applications built on their personal data. “Empowerment with personal data is crucial for individuals to lead healthier and more fulfilling lives.” - Jouko Ahvenainen Prifina's current primary focus revolves around leveraging data and applications to enhance people's well-being based on health and wearable data. By harnessing the power of personal data, Prifina aims to provide individuals with the tools they need to lead healthier lifestyles and make informed decisions about their well-being. Throughout the event, there were compelling discussions on the role of innovation in improving the lives of people. It was acknowledged that while technological advancements have the potential to revolutionize various aspects of our lives, there are also concerns about how companies and governments may misuse personal data for control. In this context, the consensus was that giving individuals better control over their personal data can empower them to lead better, healthier lives. Switzerland, known for its commitment to individual freedom and empowerment, has always held personal data control in high regard. The Swiss people recognize the importance of safeguarding personal data and ensuring that individuals retain the rights to make decisions regarding their own information. It is this shared belief that has facilitated a fruitful collaboration between Prifina, Heierling and Code Fabric, as they work together to redefine personal data ownership and control.
Prifina’s collaborations with Swiss companies shows the immense potential with user-generated personal data. By combining our expertise in technology, skiing equipment, and personal data management, we are pushing the boundaries of what is possible. This represents a unique opportunity for other consumer brands seeking innovative approaches to their product offering that have a positive impact on people's lives. Our general takeaway is that the future with user-generated personal data looks promising. Our vision of empowering individuals through personal data control resonates strongly with the Swiss people and the global community at large. Stay tuned for more updates as Prifina strives to revolutionize the way we experience sports and personal data, ultimately enhancing our lives in ways we never thought possible. On May 3rd, 2023, the Prifina team with its co-organizers at New York Life, hosted a panel on how AI-driven innovation and advances will impact consumer experiences. The event was sold out and we thank all attendees who braved the spontaneous rain in San Francisco to join in person. We’re grateful for such lively audience participation and brilliant questions for our panel, which included Mickey McManus, BCG, Malavica Sridhar, Founder, Northstarre, Markus Lampinen, Co-Founder and CEO, Prifina and moderator Amber Wang, Women in AI / Cerebral Valley.  The panelists together raised various observations and addressed the trends in the current market:
 With such a boom in generative AI, personal data and private use of personal data become fundamentally important. Not only because of privacy concerns but also because of representation; if apps and services are built utilizing individual models that represent the users, we can truly deliver private AI that helps us achieve what we want in life. Like an audience member asked: which human activities should we trust to AI and which should we want to perform ourselves? Questions like these are at the very heart of the debate, but should it not be our choice as individuals based on our preferences? After all, studies show that outsourcing tasks and chores you do not wish to do increases your overall satisfaction in your life. Thank you all for the wonderful debate and we cannot wait to see you soon to showcase more of what Personal AI can deliver, privately, to each of us. |
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The User-Held Data CompanyPrifina is a venture-backed data platform based in San Francisco dedicated to enabling Personal AI Twins and personal data ownership. Prifina empowers individuals to collect, combine, and leverage their personal information, giving them full control over their digital lives. Consumer brands and developers can create AI agents and apps that deliver personalized customer experiences while ensuring users retain ownership and control over their data.
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