OpenAI is preparing to launch a Chromium‑based browser with a built‑in chat interface and deep AI-agent integrations. On the same day, startup Perplexity AI unveiled Comet, an AI‑powered browser that turns browsing into a conversation. Just weeks earlier, The Browser Company released Dia, a browser built around an AI chat bar.

These launches aren’t just software releases — they’re a fundamental reimagining of what it means to browse the web. As someone who’s spent years building products and hosting honest tech conversations, I see both the incredible potential and the looming pitfalls of this “agentic browser” movement.

What Are Agentic Browsers?

“Agentic” comes from psychology — it describes systems that can act autonomously toward a goal. Applied to software, an agentic browser is more than a window into the web; it’s an AI assistant that understands context, anticipates needs, and can act on your behalf.

Instead of typing a search query and clicking through blue links, you describe your intent and the browser does the work: summarizing sources, comparing options, booking reservations, or purchasing products.

In other words, the browser becomes a proactive partner. It understands your goals, curates choices, and even completes tasks — turning web browsing into an intelligent, conversational experience. This is a paradigm shift from the last 30 years of browsing.

Who Are the New Challengers?

Perplexity’s Comet

Comet is an AI-native browser built on Chromium. It comes with a built-in AI sidekick that can summarize emails and calendar events, manage tabs, and answer questions about what’s on the page — without you needing to copy/paste anything.

That said, it’s not cheap — Comet is only available to Max plan users, costing $200/month. And early reviews highlight both its promise and limitations. It’s great at summarizing content but struggles with more complex tasks like cross-app coordination.

Dia by The Browser Company

Dia is all-in on chat. Built by the makers of Arc, it puts an AI bar at the heart of your browsing. Ask questions about what you’re reading, reference multiple tabs, and get context-aware answers.

But the power comes with tradeoffs: the AI sees everything — every tab, every login, every cookie. The company emphasizes local storage and encryption, but it openly admits the browser has access to sensitive info (like your SSN if you’ve ever typed it). That raises real questions about data trust and boundaries.

OpenAI’s Browser (Coming Soon)

OpenAI is reportedly working on a browser that keeps users inside the ChatGPT interface. Instead of jumping from site to site, you stay in conversation — asking your agent to act on your behalf. This browser could pull OpenAI closer to becoming the “operating system” for the web.

The scale is huge: OpenAI already has 500 million weekly users. But breaking Chrome’s habit-forming grip won’t be easy.

Why Are AI Companies Building Browsers?

1. Control the funnel

   The browser is the front door to your digital life. Own it, and you own default search, data collection, and monetization.

2. Better UX through conversation

   Browsing today is tab chaos. AI lets you “think out loud” and skip the busywork.

3. Deeper integration

   By owning the browser, companies can embed agents that fill forms, make purchases, and automate tasks without relying on clunky extensions.

Where AI Browsers Shine

• Productivity: Summarizing docs, prepping meetings, generating briefs.

• Shopping: Comparing prices, booking services, auto-checkouts.

• Accessibility: Making the web easier for non-technical users.

A Devil’s Advocate View

• Privacy concerns

  • These tools require full access to your data — tabs, logins, even your inbox. Companies may say data is local, but access alone changes the game.

• Reliability gaps

  • Models still hallucinate and break down on complex flows. What happens if your browser gets it wrong?

• Adoption hurdles

  • People don’t like switching browsers. And most new players are premium-only. Chrome’s network effects are massive.

• Ethical ripple effects

  • AI summaries could starve publishers of traffic. And over-reliance could erode users’ critical thinking.

Final Thoughts

Agentic browsers could define the next generation of the internet — or flame out like many “better browsers” before them.

It’s up to us to test, question, and shape the future. But whatever you do, choose your co-pilot wisely.

We break down what it takes to scale systems that process billions of clicks, why retention is the real growth multiplier, and how to treat strategy like an operating system for product and business expansion.

Six months ago a freshly graduated engineer named Yasser Elsaid watched his dream job vanish in the wave of tech layoffs. Instead of mourning, he locked himself in his apartment with a laptop and an idea: what if anyone could train ChatGPT on their own data and embed a chatbot on their website? Yasser had no backend team or funding, just a deadline. He opened Supabase, spun up a database and auth in minutes, and by the time the ChatGPT API launched two months later he had built Chatbase. Within two weeks he went from idea to MVP; five months after launch the app was generating $1 million in annualized revenue and serving 2,200 paying customers. He credits Supabase’s all‑in‑one backend for saving 100–150 hours of work—no separate auth or storage solution—and letting him focus on product.

Around the same time, Zeno Rocha and the team at Resend were wrestling with a different problem: they needed to launch a modern email API and scale it without drowning in backend complexity. They chose Supabase for authentication and database and Vercel for deployment. By the time Resend completed Y Combinator, the company had over 5,000 paying customers and had processed millions of emails per day. As their CEO put it, they “would not have grown so quickly” without Supabase and Vercel; even as they outgrew other tools, these two platforms continued to scale effortlessly.

These stories aren’t anomalies; they illustrate a broader shift in the software stack. The combination of Cursor, Verceland Supabase allows tiny teams to build sophisticated, AI‑enabled products that would have required dozens of engineers a few years ago. Below, we explore why this stack is compelling, how real companies are using it, and where the pitfalls lie.

Cursor – turning design and intent into code

Cursor is a Visual Studio Code fork that integrates large‑language models directly into the IDE. You can ask an agent to scaffold a Next.js project, refactor multi‑file code or generate unit tests, and it will act on your repository. In a demo, a developer uploaded a Dribbble mockup and asked Cursor to build a React to‑do app; it delivered a working UI in minutes. Cursor supports more than 25 models and lets you switch between them automaticall, upload screenshots for context and run background agents for long tasks. For a small team, this means ideation and implementation blur together—you can describe what you want and watch code appear.

Pain points. Cursor is not magic. Users have reported hallucinated edits and incomplete code, especially on large repositories. Sessions can lose context, and some models claim to apply changes without modifying files. Pricing has also shifted frequently. Finally, because Cursor executes your code on remote servers, privacy‑sensitive teams may need to disable background agents and sacrifice automation.

Vercel – deployment, AI compute and scaling without ops

Vercel is best known for hosting Next.js apps, but its 2025 overhaul turned it into a compute layer for AI. Fluid computereuses the same environment for multiple invocations, cutting cold starts and enabling long‑running AI tasks. Customers like Suno, an AI music generator, saw upwards of 40 % cost savings when beta‑testing Fluid compute. Vercel further introduced Active CPU pricing—you pay only when your code uses CPU cycles—and extended function timeouts to five minutesDevelopers can call multiple AI providers through AI Gateway and run untrusted code in Sandbox micro‑VMs. For startups, this means you deploy by pushing to GitHub; Vercel handles global CDN caching, preview environments and secure rollouts.

Caveats. Vercel’s simplicity comes with trade‑offs. It is optimised for Next.js and Node runtimes; you cannot run arbitrary Docker images or long‑running background jobs. Pricing is per user and scales with bandwidth and function calls, so viral success can spike your bill. Some teams view Vercel’s opinionated infrastructure as high vendor lock‑in.

Supabase – an open‑source Postgres backend with superpowers

Supabase turns Postgres into a realtime, vector‑enabled BaaS. It bundles authentication, row‑level security, file storage, functions and vector search into a single platform. By April 2025 the community numbered 1.7 million developers, its GitHub repo had 81 k stars and the service had spun up over one million databases. Supabase’s YC programme provides credits to founders, which Resend used to cover backend costs while they found product–market fit.

During Supabase’s Launch Week it rolled out storage upgrades (up to 500 GB per file and cheaper egress), persistent edge function storage and 97 % faster cold starts. It also released an MCP server that exposes database management tools to AI agents, and deepened its first‑party integration with Vercel: you can create and delete databases from Vercel’s dashboard and have credentials auto‑injected. In other words, you can provision an entire backend with a click.

Real‑world impact. Supabase’s case studies show how small teams leverage this power:

• Resend. They scaled from zero to over 5,000 paying customers and millions of daily emails without building a custom backend. Supabase’s auth and database allowed two front‑end engineers to focus on email infrastructure instead of ops. The YC credits eliminated early costs.

• Humata. Originally using Pinecone for vector search, this AI‑document company switched to Supabase’s pgVector and saw a 4× reduction in costs while consolidating all data into one Postgres instance. Supabase’s real‑time and auth features supported millions of users, and row‑level security kept sensitive research data compliant.

• Chatbase. As noted above, a single founder built an AI chatbot builder on Supabase and hit $1 million in annualised revenue with a team of one. He credits Supabase’s integrated database, auth, storage and realtime for compressing months of work into weeks.

Challenges. Supabase isn’t perfect. Some developers warn that creating tables manually in its UI leads to migration headaches and recommend using an ORM. Embedding business logic into row‑level policies can cause vendor lock‑in. The generous free tier caps quickly, and paid plans jump from $25 per project to hundreds per month for higher tiers. Real‑time queries run on Postgres; for analytics‑heavy workloads you may still need a separate warehouse.

Why the trio works together

What makes these tools special is how they complement each other. Cursor turns design mockups and natural‑language prompts into production‑ready code. Supabase provides an open, scalable Postgres backend with realtime and vector search. Vercel deploys and scales that code globally, optimising compute for AI workloads. The integration between Supabase and Vercel means your database credentials are auto‑injected and billing is unified, and the MCP server allows AI agents in Cursor to spin up databases or run migrations programmatically. In practice, this means you can sketch a UI, generate the code with Cursor, push to GitHub and have Vercel deploy it with a Supabase backend in minutes. For startups, that drastically reduces coordination overhead.

Caution

The allure of building a billion‑dollar company with a dozen engineers is real, but experienced engineers know the devil is in the details. Tools like Cursor are still experimental; they hallucinate and may require human review. Vercel’s fluid compute lowers costs for AI workloads, but you lose control over runtime and infrastructure, and sudden viral traffic can still produce shocking bills. Supabase simplifies the backend but can lock you in if you over‑couple business logic to its functions and policies. And no stack solves product–market fit; that’s on you.

Ideas are rare, execution is a commodity

Fifteen years ago building a SaaS product meant hiring DevOps, database admins and front‑end specialists. Today a single founder can ship, scale and monetize an AI‑enabled product in months, thanks to tools like Cursor, Vercel and Supabase. Infrastructure has become a commodity; execution speed is no longer the differentiator. Ideas and taste are. The next billion‑dollar startup will not be the one that writes the most code but the one that uses these commoditized capabilities to solve a unique, painful problem with empathy and insight. If you’re contemplating your own venture, take a page from Yasser and Zeno: leverage the stack to eliminate undifferentiated work, but pour your energy into understanding users and crafting a product only you can imagine.

We talk about:

• The reality of scaling API-first platforms

• What makes a great developer experience

• Managing 70+ remote teammates across cultures

• Building for both B2B and B2B2C at once

• What it takes to lead in FinTech and HRTech today

Anastasiya also shares hard-won insights on launching across different markets, building intuition-driven products, and operating with clarity in a world obsessed with hype.

The discussion also touches on the significance of branding and the need for companies to adapt to remain competitive.

Chapters

• 00:00 Introduction to Entrepreneurial Mindset

• 05:50 Understanding Neuro Entrepreneurship

• 10:02 Neuroplasticity and Its Impact on Learning

• 15:42 The Role of Sleep in Neuroplasticity

• 20:20 Nutrition and Its Importance for Entrepreneurs

• 25:10 Dopamine and the Entrepreneurial Experience

• 30:19 Balancing Inspiration and Reality

• 34:09 Empathy vs. Cognitive Speed in Entrepreneurship

• 36:02 Understanding Dopamine and Nervous System Dynamics

• 39:48 The Entrepreneurial Brain: Connections and Opportunities

• 42:34 Physiology and Mental Health in the Workplace

• 46:33 Creating a Supportive Work Environment

• 52:37 Navigating Organizational Change

• 01:05:52 Embracing Individual Differences in the Workplace

We covered:

• Growing a company from zero during crisis

• How Ukraine’s government is going digital faster than the West

• Why AI is a tool—not a threat—for serious design teams

• The mindset that keeps his team pushing at 6am under air raid sirens

• And what founders in San Francisco should learn from Ukrainian resilience

Alexander doesn’t just talk about grit—he lives it. This one’s a must-listen for any builder or operator.

We dive into:

• Why mental health is the backbone of founder success

• How AI isn’t replacing leadership—but reshaping it

• Building businesses in a weekend vs. the old playbook

• What makes entrepreneurs psychologically different (backed by neuroscience)

• The role of play, curiosity, and personalization in leading teams through change

We also unpack Marina’s cutting-edge “AIQ” framework for measuring team readiness for AI adoption—and why emotional experience with AI will soon define how companies scale.

If you’re a founder, leader, or just curious about how the brain, business, and AI intersect—this one’s for you.

Kevin shares:

• How iOS 14.5 rewired performance marketing—and what winning teams did to adapt

• Why CDPs and first-party data integrations are now essential for scaling

• How Facebook rebuilt its algorithm and what it means for creators and startups

• The rise (and possible fall) of TikTok—and what platforms are next in line

• Hot takes on LinkedIn, Instagram, and why most teams still get creative formats wrong

• What AI-generated video and automation means for the future of ads

Whether you’re a founder, growth lead, or curious about how marketing is actually changing—this is the episode you’ve been waiting for

Anjali offers understanding on:

• Why the roles of product designers and product managers are merging — and what skills will be essential.

• In what way is AI both speeding up the opportunity and responsibility for builders?

• The actual distinguishing factors in an AI-first world are critical thinking, craftsmanship, and empathy. These are not only our best attributes but also the attributes that AI will never have.

• The significance of "zero UI" design and what it entails.

• How "zero UI" design is set to revolutionize our digital encounters and redefine them as fluid and natural, without any visible signs of an interface.

• The drawbacks of even the most advanced current interfaces, which still employ some form of visual component to convey information.

• The way that "zero UI" will help to streamline our interactions with the devices we use in every aspect of our lives.

• Pragmatic counsel for fresh graduates and seasoned designers alike on how to distinguish themselves and maintain relevance

Mastering tomorrow is a helter-skelter process of trying to seize and make sense of today’s many unfolding narratives. This conversation is a masterclass in some of the future directions these narratives are likely to take.

Anurag shares his journey of construction, tracing back to design school, and disappointing the clear, bright line drawn between technology and humanity. We also sweat the hot stuff, like:

• The reason why real specialization will become premium in an AI-driven world.

• Traditional product management is dying as roles compress.

• The evolution of designers into strategic builders and product owners is necessary.

• The concealed risks of depending excessively on ready-made AI tools.

• Why the future is the domain of builders who not only construct with speed but also infuse depth into their creations.

This conversation will hit home if you care about maintaining your lead in technology, developing genuine skills, and grasping the next steps in design and AI as they relate to our world.

The AI Leverage Evolution

A genuine 10x AI tool in 2025 delivers more than speed improvements because it creates new approaches to problem-solving. The era of treating AI as a speedier calculator or search engine has passed into history. Modern intellectual partner AI systems go beyond task execution to completely transform how tasks should be performed.

Speed: From Minutes to Moments

The most obvious aspect of 10x is its sheer rapidity. Assignments that previously took eons to accomplish; hunting down a subject, composing a written work, crunching numbers, or crafting software.

This can now be done in the blink of an eye. But when taken alone, speed doesn't really convey the magnitude of change that AI tools seem poised to bring to workplaces in 2025.

Depth: Nuance and Complexity

Depth is what lies beyond speed—the capacity to manage nuance and complexity that could easily swamp even proficient humans. Today's AI pals can chew on thousands of factors all at once, pick out slight signals among tremendous amounts of data, and keep the thread of their argument straight across documents in a way that would be impossible for any human team to sustain for even a few days. And most obviously, this depth enables insights that might not otherwise come to light.

Automation: From Thought to Completion

One of the most revolutionary aspects of 10x tools in 2025 is their capacity to connect thought and execution. The most advanced systems don't just answer prompts anymore—they take a half-baked idea and turn it into a polished product, handling all the intermediate steps between the two with almost no help from humans.

Insight: Seeing What We Miss

Tools that are really 10x better than anything else add to human creativity and problem-solving by showing us the connections and possibilities that we might otherwise miss. They are not super-intelligent machines that do our work for us. They are not even super-smart tools that we use in the same old ways and get the same old results. They are very much the opposite: extensions of our own limited intelligence that serve to magnify and multiply our force

Creativity: Beyond Imitation

By 2025, the most powerful AI tools have advanced beyond simple imitation and can act as truly creative partners. They do more than just replicate current patterns; they work with us to generate genuinely new ideas, designs, and solutions that go well beyond the usual framework. This is convention-defying stuff.

The Real Measure of 10x

A 10x tool's real worth isn't revealed through benchmark scores or technical specs. Its worth emerges in the world and in the hands of users—who, by the way, are the only true judges of a tool's value. We measure it through real-life impact, and we start with how it enables individuals and teams to accomplish results:

• Does it allow a solo founder to go up against teams ten times their size and hold up a good fight?

• Does it push a researcher to explore ten times more wild hypotheses within the same amount of time?

• Does it empower a product designer to go through ten times more iterations, build working prototypes and hit their result?

• Does it give a content creator, well, ten times more oomph to create with? (We suspect this is a yes for the same reasons, with the caveat that a content creator could just as easily be a facilitator of terrible outcomes as good ones.)

Use Case Deep Dives

This section is a detailed look at how the main tools and services based on artificial intelligence work and what kind of performance they deliver. When using these tools and services, you get the best results when you know what you want and can clearly describe it. Some tools shine in areas where others may not. We'll also touch on the subject of using specialized tools versus using more universal, general-purpose tools.

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Research and Knowledge Work

• What: Sifting through massive datasets, documents, and academic sources to uncover insights.

• Why it matters: Time-intensive and cognitively demanding, great research is a leverage multiplier.

• Top Tools: Claude for reasoning and context; DeepSeek for cost-effective technical research; GPT-4o for multimodal analysis.

• Takeaway: Claude is best for narrative and logic-heavy research; DeepSeek shines on math/science benchmarks; GPT-4o is ideal when your inputs are varied (text + visuals).

Coding and Development

• What: At scale, writing, debugging, and maintaining software.

• Why it matters: AI that can read, reason, and write code is already saving time in developer workflows.

• Top Tools: For all GitHub automation, use Claude; for algorithmically difficult problems, use DeepSeek; for transforming and generating code (e.g., from a natural or higher-level specification), use Gemini.

• Takeaway: Claude is dev-ready, DeepSeek is powerful and cheap, Gemini is built for scale and multimodal interaction.

Content Creation and Communication

• What: Penning various forms of written communication—blogs, emails, presentations, scripts—and nailing the brand's tone across all.

• Why it matters: Writing isn't just about churning out words. It's about making those words resonate and be understood quickly.

• Top Tools: GPT-4o for hitting all the mediums and for sheer versatility; Claude for making long, coherent pieces of writing; Gemini for the more visually-inclined.

• Takeaways: If you're looking for an all-around tool, go with GPT-4o. If your tool needs a toehold in long-form writing, go with Claude.

Automation and Task Execution

• What: Delegating not just tasks but full workflows to AI.

• Why it matters: Productivity isn’t just faster clicks—it’s making your own self less necessary in the loop.

• Top Tools: Manus for end-to-end task execution; Claude (via Claude Code) for dev workflows; DeepSeek for math/data automation.

• Takeaway: Manus is the only true autonomous agent right now; Claude excels within dev pipelines; DeepSeek wins on cost-efficiency.

Business Intelligence and Decision Support

• What: Making better calls from analyzing data, trends, and signals.

• Why it matters: The competitive advantage is in the speed and sharpness of decisions.

• Top Tools: Use Claude for cross-functional business logic; Manus for real-time research; and Gemini for deep-diving in Google-native environments.

• Takeaway: Claude is great for the types of complex scenarios that require business logic and reasoning; Manus gets you to fast insights in real time; and Gemini ties together the visual and structured data for deeper understanding.

Startup Operations and Product Development

• What: Market research, design, building, and GTM in a lean team.

• Why it matters: Startups can't afford siloed tools or stacks that are too fat.

• Top Tools: GPT-4o for all-end flexibility; Manus for operational scaling; DeepSeek for building cheap.

• Takeaway: GPT-4o is the Swiss Army knife; Manus gives the founders time back; DeepSeek is basically golden.

AI Tool Recommendation Matrix

We will break down how each leading AI tool performs across a set of important categories for founders, operators, researchers, and creators.

We will not treat these tools as interchangeable, and we will not treat them as a monolithic set of things that do the same kind of work in the same kind of ways.

Instead, we will look at where each tool really stands out and where it makes sense to use it.

Your 10x Tool: Decision Framework

When selecting which AI tool will provide the most leverage for your specific needs, consider these key factors:

• When it comes to simple, well-defined tasks, specialized tools usually do a better job than all-purpose ones. But if you have a complex workflow that demands a variety of capabilities, you're better off with a versatile tool like GPT-4o or Claude 3.7 Sonnet.

• For large documents or codebases, use tools with the largest context windows—such as Claude, with its 200,000-token context; Gemini; or Grok.

• For teams with limited financial resources, DeepSeek R1 provides tremendous value at a price point far below that of its competitors. The trade-off is that its user interface and creative functionalities are a few steps lower on the proficiency scale.

• If you need a tool that can perform tasks with little supervision, Manus has what it takes to ensure that operation remains largely autonomous. It is, however, an expensive option.

To meet the integration needs, consider where the tool lands in your tech stack and workflow. Gemini offers smooth integration with all things Google, while Claude's GitHub integrations are leaps and bounds ahead of the rest.

Often the most powerful approach is combining tools—using DeepSeek R1 for research and data analysis, using Claude for deep reasoning, and using GPT-4o for multimodal tasks and content creation, for instance.

The Future of AI-Augmented Work

What is clear, however, is that we have entered a new era of AI-enhanced work. The pioneer models of 2025 are not merely improvements on their predecessors, they're a fundamental shift in how we handle inherently difficult cognitive tasks. And when I say they're a shift, I mean they're faster and better; they're also different.

The most successful professionals in 2025 won't be those who just adopt AI tools as swifter versions of existing workflows. They'll be the ones who rethink their processes from square one, using AI's one-of-a-kind capabilities to solve problems that would have stumped them before.

Whether you're a solo founder constructing a venture with AI as your partner, an investigator probing profound questions at an unprecedented scale, or a maker of content who in the past would have required a platoon to pull it all together, the correct AI tools can indeed give you 10x leverage—if not more—in not just the swiftness with which you accomplish tasks, but in their profundity, quality, and actualization.

From UX research to product design, roles are merging. And if you’re not adapting, you’re already behind.

I share:

• How I built a production-ready chat app and news app with Lovable.dev in hours

• Why tools like Manus are reshaping what research, analysis, and execution even mean

• A hot take on the disappearing PM/Designer divide

• Why AI is now the new cofounder, engineer, and analyst—if you know how to use it

• My belief that execution is a commodity, and ideas are the new scarcity

This is not a sponsored episode. No fluff. Just real insight from the trenches.

This isn’t just another AI hype take. Lovable lets you build real, production-level apps with a single prompt. Manus feels like having a full research team running in the background—giving you startup-level insights in seconds.

I talk about:

• Why execution is no longer the bottleneck

• How tools like these let you go from “I have an idea” to “I shipped it” in hours

• The shift from general-purpose tools to personalized SaaS stacks

• Why product builders need to rethink what it means to ship

As I said in the episode: “It’s not about proving if you can build—it’s about proving whether the idea is even worth building.”

What if you could sit across from them, hear their voice, see them smile, ask them the questions you never got to?

My grandfather passed away right after I was born. I never met him, but I’ve spent my whole life hearing how brilliant he was—a maestro in math, a mind ahead of his time, someone who would’ve loved today’s technology.

I’ve always wondered what it would’ve been like to talk to him, even once.

That question—what if you could connect with someone long after they’re gone—is where this whole idea started.

Not as science fiction, but as something AI is actually making possible:

Digital Immortality

To me, it’s like preserving art—but the medium is you:

Your voice.

Your face.

Your memories.

Your personality.

I’m not going as far as freezing you in time but I’m thinking of weaving a virtual echo of your soul (your unique way of thinking, speaking, being) into a form that future generations can actively engage with. It’s essential you are in a virtual form being preserved (with all your memories and unique attributes) allowing future generations to connect with you, long after you’ve left this earth.

How Would I Build It Today?

A few tools I would leverage or possibly agentic workflow might be useful here – but let’s focus on tools first which focus on building a living portrait.

Technologies such as Crew.ai, Gemini, ChatGPT, Grok, ElevenLabs, and HeyGen already lay the foundation, what will be challenging is integrating attributes and memories.

Here’s a quick look at how these tools fit into the digital immortality puzzle

• Crew.ai: Think of Crew as the behind-the-scenes operator. It connects tools, organizes data, and ensures everything flows—from voice samples and videos to old tweets and memories. It’s what quietly pulls your digital self together.

• Gemini: This model handles memory like a beast. It can absorb years of your texts, emails, and convos, then recall them with nuance. The result? A version of you that remembers your childhood takes and niche opinions—like it actually knowsyou.

• ChatGPT: This one brings the vibe. It makes your digital self talk like you—not robotic, but warm, casual, sometimes funny. Think: a coffee chat, not a lecture.

• Grok: Grok digs into your personality. It gets your tone, your sarcasm, how you rant or explain things. It gives your digital self character—not just content.

• ElevenLabs: Voice matters. This tool nails yours, capturing how you sound, laugh, pause. One short clip and it recreates that familiar tone everyone remembers.

• HeyGen: This is the face of it all—literally. HeyGen animates your avatar so people can see you speak, smile, nod. Not just audio or text. You, brought back visually.

Attributes and Memories

Once you take care of the look and sound part – it needs the core aspect, which is personality and life experience to truly be you. Now this seems to be a deep-layer question or discovery required but based on how I’m thinking are the following:

Mimic Your Attributes

To make your digital self truly you, it needs to learn how you think, talk, and react. One approach could be spending 30 days interacting with your digital twin, training it like a companion. Feed it your thoughts, your responses, your worldview.

Pull in your social media, texts, journal entries—even home videos. Models like ChatGPT and Gemini could analyze patterns and mimic your communication style over time.

Memory Bank

Your life’s stories deserve structure. Think of building a memory bank—tagging key moments like “graduation,” “first job,” or “rainy hike in Iceland.” Each memory carries not just facts, but emotion.

Crew.ai could organize this data. Language models could retrieve and retell your stories in context. Every memory gets labeled—“thrilled,” “nervous,” “pissed off.” Sentiment analysis helps capture emotional texture, not just information.

Memories could be tied to cues. Ask about summer, and your digital self recalls the beach—because “sand” and “sun” are linked to laughter and nostalgia. With structure and emotional metadata, your digital self won’t just recall stories—it will relive them.

A New Kind of Legacy

This is truly possible – where you can preserve your legacy (wisdom, humor, personality) something future generations can leverage to build and connect to their roots with living connections.

The Human Side

There’s a quiet comfort in this idea—imagine your loved ones being able to hear your voice or see your smile again. It doesn’t erase the pain of loss, but it softens it. It creates presence where there would’ve been only absence.

And beyond grief, capturing the full complexity of a human in digital form could lead to breakthroughs—in memory, storytelling, even empathy.

Yes, Ethics Will Be Questioned!

If you’re reading this and wondering about the ethical implications—yeah, me too. I don’t have all the answers. Same goes for the emotional risks. But isn’t that the case with every frontier?

New Horizon

To me, this is a bridge between tech and something deeply human: the timeless urge to endure. The future has to be something we’re excited about—not just a list of problems to solve.

So what do you think—would you want a digital version of yourself to live on?

• Why execution is becoming a commodity, and ideas are the real differentiator

• Tools like Bubble, WeWeb, Xano, and FlutterFlow—and where they actually scale

• How startups and enterprises are leveraging no-code for speed, savings, and flexibility

• What product designers, non-technical founders, and even risk-averse corporations need to know right now

If you’ve ever felt stuck between having an idea and knowing how to build it—this one’s for you.

We talk about:

• Why she ran for office on a gut feeling—making a split-second decision that turned into a full-fledged campaign.

• The efficiency mindset in politics & business—why she treats campaigns like startups and maximizes impact with minimal resources.

• How tech is reshaping elections—from AI-powered research to data-driven voter outreach.

• Balancing leadership, motherhood, and relentless ambition—the raw, behind-the-scenes reality of running for office as a single mom.

Disclaimer: The views expressed by guests are their own. This podcast does not endorse or take responsibility for any guest’s statements or actions.

Oh, and fun fact—this podcast was cooked up using Elevenlabs, ChatGPT 4o, Grok, and some agentic podcast post-production magic (still in the oven as of March 2025).

Klarna’s AI Wake-Up Call

Picture this: you’re sipping your Monday coffee, scrolling X, and bam—Klarna’s in the headlines. The Swedish fintech slashed its workforce from 5,000 to 3,800, letting an AI chatbot handle customer service like a champ—doing the work of 700 people, per a February 2024 Business Insider piece (Klarna’s AI assistant is doing the work of 700 people). Resolution times? Down from 11 minutes to 2. Efficiency on steroids, right?

But here’s the kicker: Klarna says this wasn’t tied to their 2022 layoffs—it’s more about dodging new hires. Still, it’s got us techies wondering: is this the future? Are companies about to lean hard into AI and leave us refreshing LinkedIn job boards? Hold that thought—there’s more to the story.

AI: Job Killer or Job Creator?

Let’s get real—AI’s shaking things up across the board. Customer service? Chatbots got it. Coding? GitHub Copilot’s spitting out snippets faster than you can say “debug.” Even product management’s getting a glow-up, with AI crunching feedback and drafting roadmaps. A 2023 McKinsey report says generative AI could automate 60-70% of grunt work for knowledge roles (The economic potential of generative AI). That’s wild!

But don’t panic just yet. While AI’s axing some gigs, it’s also birthing new ones. Ever heard of an AI prompt engineer? Or an AI ethics guru? These roles didn’t exist a hot minute ago, and the World Economic Forum’s 2023 report predicts AI will spawn 97 million new jobs by 2025. PwC’s 2024 AI Jobs Barometer even drops a juicy stat: AI-skilled gigs can snag you a 25% wage bump.

Junior Devs and the Entry-Level Squeeze

Okay, devs, this one’s for you. Remember grinding through boilerplate code or late-night debugging as a junior? AI’s got that on lock now—Copilot and friends are eating those tasks for breakfast. Same deal for PMs—AI’s handling the entry-level grunt work. Sounds dope, but here’s the rub: how do you level up if the ladder’s bottom rungs are gone?

The ADP Research Institute’s 2024 survey says 85% of workers see AI hitting their jobs soon, and some worry it’s a talent gap waiting to happen (Most workers think AI will affect their jobs). Companies might save cash short-term, but long-term? They’ll need humans who’ve climbed the ranks.

The Flip Side: AI as Your Wingman

Here’s the good news—AI’s not just a job thief; it’s your new BFF. Customer service reps can let chatbots tackle FAQs and swoop in for the tricky stuff. Designers can use AI mockups to iterate like speed demons. Sales folks? AI’s scoring leads so you can close deals with that human charm.

Take marketing: AI spits out ad copy and analytics, but you’re the one weaving it into a killer campaign. Or leadership—AI can crunch market trends, but it’s not inspiring the team or navigating office drama. That’s your turf, and it’s staying that way.

How to Stay Ahead in the AI Game

So, how do you future-proof your tech career? We’ve got three pro tips:

1. Get AI-Fluent: Learn the tools—whether it’s Copilot for coding or AI analytics for PMs. The IMF’s 2024 blog says AI’s hitting 40% of jobs globally, so staying sharp is clutch (AI Will Transform the Global Economy).

2. Flex Your Human Superpowers: Creativity, empathy, strategic vibes—AI can’t touch that. Lean into what makes you, well, you.

3. Keep Evolving: The tech world’s a moving target. Upskill, reskill, and roll with the punches. Continuous learning’s your golden ticket.

The Big Picture

AI’s not here to yeet us all out of work—it’s a tool, not a terminator. Klarna’s efficiency flex? Inspiring, sure, but it’s not the full story. Product managers aren’t vanishing; they’re evolving. Juniors might sweat the entry-level squeeze, but new roles are popping up left and right. The trick? Ride the AI wave, not fight it.