Deep Personalization Is the Next AI Frontier
By Andre Pemmelaar
Andre Pemmelaar is the CEO and founder of Dubai-based Mygentic AI. Find him on LinkedIn.
It's evening, and I'm cradling my four-month-old son on the balcony, watching the sun dip below the horizon. Suddenly my phone chirps, demanding attention. It's not a text or an email, but an AI assistant, one of many that now populate my digital life. I take mental inventory of my tools, which includes a couple of image generators, a voice synthesizer, and a business agent. I fumble with my phone, but when my son eyes my distracted thumb, I decide to wait until later to deal with the AI assistant.
Siri chimes in—suddenly, she feels like the Clippy of 2024. Everything we hoped for in Siri is here now but distributed across a pantheon of AI assistants. They schedule, draft, and report with uncanny efficiency. Mark Zuckerberg predicts in the next few years, “There will be more AI agents than there are people in your business.” But to truly matter, these digital companions must evolve beyond task completion. They'll need to become nimble custodians of our most private data, adapting to our shifting moods, roles, and needs. Is our technology ready for this level of intimacy? Better yet, are we?
This isn't just AI automation; this is the dawn of deep personalization. AI agents will need to synthesize vast amounts of data to capture our essences, our quirks, our many facets. Mapping our digital selves poses serious privacy risks. The centralized own-the-users data approach of today’s tech giants won’t suffice. In this brave new world, your data isn't just valuable—it's the raw material for your AI confidant. The challenge before us is balancing personalization with privacy in a landscape where your digital footprint is increasingly synonymous with your entire identity.
***
The potential for deeply personalized AI is immense. But I look back into our brand new apartment, at our unopened moving boxes, and I’m reminded of AI's current limitations: I could have benefited from an assistant to sift through apartment listings, compare amenities, check availabilities, set up appointments. All the tech to help with these different tasks exists—and yet I spent hours scrolling, evaluating, and messaging flesh-and-blood real estate agents. The reality is that AI hasn't mastered the complexities of human preferences. The personalization layer simply isn't that sophisticated.
How do I begin to explain to an AI assistant what I want in an apartment? Sure, I can specify the parameters of my budget and my preferences—I can, for instance, say I want a bright, upscale one-bedroom unit. But what if I want an uncluttered interior design aesthetic with a glamorous downtown view, and the assistant fails to infer this? The gap between knowing what we want and effectively communicating it highlights a key hurdle for AI: understanding and adapting to complex human tastes.
The best method for achieving this level of understanding is “shadowing”. It’s a method for training humans, where people learn a skill or objective by closely observing others do it. Shadowing is more than mere observation: It's an immersive experience in understanding the reasoning and context behind another's choices. Capturing these subtle dynamics is crucial for transforming our personal AI into something genuinely personable.
It turns out that the techniques to learn preferences are built into recommendation algorithms, which have been quietly observing and updating your personal preference vectors at Netflix, Amazon, Instagram, and anywhere else recommendation engines are used. Each of these companies maintains a personal vector on you: a row of numbers that maps out your position in the multidimensional preference space of each of these companies.
While AI's overall personalization capabilities are still evolving, recent breakthroughs in specific domains like visual and audio modeling showcase the emerging potential for deep personalization. These advancements are transforming how we're represented digitally. We're no longer just a few data points; we're being distilled into precise mathematical representations in the form of vectors that capture who we are with uncanny accuracy. Models like LoRA can create personalized images of you from just a few photos, and similar tech exists for your voice. It's only a matter of time before these vectors capture many of the most essential aspects of who you are. This leap in AI capabilities isn't just a technical achievement—it's a fundamental shift in how we're represented digitally.
Because the next level of AI personalization will require unprecedented amounts of personal data to adapt to our identities, there are many privacy issues to consider. The scale and sensitivity of this information is too vast for any single entity to responsibly control. The potential for misuse or exploitation is too great, and the privacy implications too severe.
To address these challenges, applications will need to utilize a different kind of data infrastructure.
Future systems must ensure that the information powering our personalized AI remains private and under the exclusive control of individual users. No single corporation, government, or other standalone entity should have unrestricted access to that much personal data. At present, my data vectors are scattered across a portfolio of AI and consumer apps each with its own small slice of me—and I’m far from the only AI user with these particular habits. To be truly useful, we’re going to need to figure out how we bring all of our scattered data together in a way that maintains user sovereignty.
One solution is taking a hybrid approach that combines on-device storage with cloud backup. Personalized on-device models could work in tandem with distributed encrypted backups and anonymized cloud services. This structure would allow users to retain control over their data while still providing access to powerful AI capabilities. By keeping sensitive information local, encrypted, and on-device, while leveraging cloud resources for more general tasks, we can strike a balance between privacy and powerful AI services that remain personalized. This approach may allow us to harness the full potential of AI personalization without compromising individual data security.
In this light, decentralized web3 tech looks less like an aimless solution in search of a problem and more like a serendipitous development in digital infrastructure. Initially, web3 was primarily focused on financial use cases, which by nature require highly secure and preferably private transactions. This imperative has pushed web3 developers to create increasingly sophisticated privacy-preserving technologies. Innovations such as encrypted data sharding, multi-party computation (MPC), zero-knowledge proofs, and homomorphic encryption for private computation have emerged from this ecosystem.
These advancements, born from the stringent security requirements of decentralized finance, now offer powerful tools for addressing broader data privacy concerns. Importantly, many of these technologies are open-source, making them available for non-financial applications, including the complex privacy challenges posed by AI personalization. The evolution of web3 has inadvertently created a toolkit that could be instrumental in balancing the need for deep AI personalization with robust data protection and user sovereignty.
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It's evening now. The sun has disappeared and my four-month-old son has drifted off to sleep in my arms. As I gently place him in his crib, I find myself shifting gears. The attentive parent mode switches off, and I'm suddenly aware of multiple priorities vying for my attention. Should I be the focused professional catching up on emails, the mindfulness practitioner attempting a wind-down routine, or the aspiring writer ready to jot down the day's thoughts? This constant flux of roles, which anyone can relate to, speaks to a crucial challenge for the next frontier of AI personalization.
Emailing, meditating, journaling—each of these activities represents a different part of myself. And these different parts of myself have their own preferences, goals, and behaviors. The health-conscious longevity junkie I aspire to be might like to focus on the latest life-extending research, while the attentive entrepreneur wants to be more productive. The new parent wants tips on getting his little one to sleep through the night, while the AI geek craves a curated list of the latest trending topics in his area of expertise.
Taking these disparate selves altogether, and understanding that they belong to one single person, presents a complex challenge for AI. The personalization layer of the future will need to be built to seamlessly manage and respond to the many complicated versions of ourselves. The computational power, sophisticated algorithms, and continuous learning required to navigate our complex identities demand significant resources. This level of nuanced, context-aware personalization is incompatible with web2 services, which are “free” to use except they profit from the monetization of user data.
In the mid-2000s, long before its name-change, Facebook pioneered the “feed” and cemented attention-based monetization as the primary means of making money in tech. Soon after, tech companies built their fortunes by capturing user attention as a means to harvest valuable data. This data, rather than attention itself, became the true currency of the digital economy. In this landscape, personalization was corralled into a narrow path, forced to serve the singular purpose of maximizing engagement and ad revenue.
The vast potential of truly tailored experiences was funneled into a model that prioritized clicks over genuine user benefit. This trend reached its zenith with TikTok, where an endless stream of viral, bite-sized personal content became the ultimate mind-hijacking experience, perfecting the art of capturing and holding user attention at an unprecedented scale—a phenomenon Evan Armstrong aptly likened to “[watching] TV on crack.”
The coming era of personal AI seems fundamentally incompatible with this version of our digital world. Free services have historically meant surrendering control of your data to tech giants. But as AI becomes more deeply integrated into our lives, managing our schedules, preferences, and even our various identities, this model becomes untenable. The level of intimacy and trust required for truly personalized AI demands a new paradigm—one where users maintain sovereignty over their digital selves.
I turn to my son, watching him sleep peacefully in his crib. My mind wanders to the digital landscape we'll need to navigate by the time he enters kindergarten. In just a few years, we'll likely live in a world of ubiquitous AI: Everything, everywhere, all at once—and artificial. The coming wave of agents seems poised to overwhelm us. This proliferation, while promising unprecedented convenience, presents a new challenge: the need for coordination and orchestration.
As we find ourselves surrounded by specialized AI agents—each optimized for specific tasks—who will orchestrate our personal digital ecosystem? While tech giants are offering impressive AI solutions, it’s unlikely these AIs will be working on behalf of their users, who are the ones making these tools more intelligent. The objectives of tech giants, no matter how benevolent they may seem, are ultimately shaped by business interests and shareholder expectations. The AI that knows you best, that has access to the majority of your personal data, may not be the one that has your best interests at heart. Your digital doppelganger, that matrix of preferences and behaviors that makes you uniquely you, may end up scattered across various corporate databases, each holding a piece of the puzzle.
The question of ownership is paramount. Who will own your digital self? Who will control the AI that interprets and acts upon your data? As AI becomes more deeply integrated into our lives, these questions will move from philosophical musings to pressing concerns. In the coming years, our personal AI will evolve into digital reflections capable of accurately making choices on our behalf, managing our multi-selves, and acting as our representatives in AI enclaves. This shift will reshape our relationship with technology, raising new questions about privacy, identity, and the value we place on ownership of our digital selves.
Deep alignment with our personal needs and goals will necessitate a new form of ownership and control. Regulations won’t suffice. Instead, user sovereignty will need to be built into the technology itself, fundamentally changing how we interact with and value AI services.