Here’s my take on what AI can and can’t do—and what it means for software companies.

AI Is a Mirror (Mostly)

Modern LLMs are trained largely on internet data, plus synthetic data and human feedback. What goes in shapes what comes out. AI reflects humanity’s collective knowledge, patterns, and limitations.

This means AI excels at problems humanity has already solved and documented. Coding? Decades of open-source repositories. Legal boilerplate? Millions of contracts online. Customer support scripts? Endless training data.

Software development *costs* are genuinely plummeting. That part of the narrative is real.

Where AI Struggles

AI struggles with two things:

1) novel reasoning beyond its training distribution, and

2) problems requiring real-world causal intervention.

Consider macroeconomics. A policy change takes years to play out. Millions of variables interact.

But the deeper issue isn’t just slow feedback—it’s that you can’t run experiments. You can’t try a policy, observe the outcome, and retrain. AI can pattern-match on historical data, but it can’t reliably predict the effects of interventions never tried.

The hardest problems for AI are those requiring counterfactual reasoning about novel situations in complex systems. If you can’t experiment, you can’t generate reliable training signal.

The Network Effect Moat

Here’s what the SaaSpocalypse narrative misses: network effects.

Facebook isn’t valuable because its software is hard to build. It’s valuable because everyone you know is already on it. Salesforce isn’t sticky because CRM is technically complex. It’s sticky because your entire sales organization’s workflows, integrations, and data live there.

AI can lower the cost of building software. It can’t easily replicate an installed base of millions of users who would have to coordinate a simultaneous switch.

Companies with strong network effects can use AI to reduce operating costs while retaining users. They become more profitable, not obsolete.

My Prediction

SaaS companies without network effects—point solutions, undifferentiated tools, anything easily replicated—are genuinely at risk. AI makes building commodity software trivially cheap.

SaaS companies with network effects—platforms where value increases with each additional user—will likely survive and flourish. They’ll use AI to cut costs and improve products while competitors struggle to dislodge entrenched user bases.

The sorting mechanism isn’t “SaaS vs. AI.” It’s “network effects vs. no network effects.”

Why should You care?

The Privacy Reality

Every robot vacuum on the market today requires a cloud connection to function. You download the app, create an account, and your robot starts phoning home—mapping your house, tracking schedules, streaming telemetry to servers you don’t control.

Technically, your robot doesn’t need a cloud to operate inside your home. It has all the sensors and processing power to clean autonomously. The cloud exists for remote access and data collection.

Security researcher Dennis Giese has spent years reverse-engineering vacuum robots, presenting findings at DEF CON and Chaos Communication Congress. His research at dontvacuum.me documents how these devices collect Wi-Fi credentials, home maps, and telemetry—often with weak encryption.

One engineer discovered his vacuum was bricked remotely after he blocked its telemetry server. The device had rtty software allowing the cloud operator to execute commands without the owner’s knowledge.

His full writeup: "The day my smart vacuum turned against me"

The Liberation Movement

Valetudo (valetudo.cloud) is an open-source project with 8,600+ GitHub stars providing cloud-free operation for supported robots. It protects your data by keeping everything local.

What We’re Building

At Remake.ai, we’re launching app-enabled vacuum models with cloud-free operation out of the box. No rooting required.

And when you do use our cloud, you’re trusting a US-based platform where privacy and compliance are built into the architecture.

We’re also building a local operation mode: apps download and run on your Raspberry Pi 5 or Linux PC. You can unplug the internet entirely.

Tradeoffs exist—no remote access outside your home, and AI-powered apps need explicit permission for cloud APIs. But you get improved responsiveness and complete data sovereignty.

Cloud should be optional. Privacy should be default.

Here’s why robot vacuums are the right starting point.

The Installed Base Problem

Home vacuum cleaners have become ubiquitous. US household penetration for robot vacuum cleaners exceeds 10%, with some estimates suggesting it could reach 20% in the near future. The global market shipped 22.1 million units in 2025, generating $4.98 billion in revenue. This isn’t a niche market anymore—it’s mainstream.

For my startup’s business model—apps for robots—this means I can explain what we do to almost anyone. “It’s like an App Store, but for your Roomba” gets instant understanding. Try explaining an app platform for a humanoid robot that doesn’t exist in anyone’s home yet.

The De-Risked Form Factor

Consumers have spent over a decade getting comfortable with robots moving around their homes. Yes, people trip over their Roombas occasionally. Yes, they run into furniture. But these safety concerns have been largely normalized. The form factor is accepted.

Compare this to humanoids. I asked my wife if she’d be comfortable with a humanoid robot at home—one that’s stronger than her, powered by AI that could potentially malfunction or be hacked. Her answer: absolutely not. Mine too, frankly.

Even smaller humanoids make me uncomfortable. Child-sized robots doing household tasks? That crosses into disturbing territory for a lot of people, myself included.

The Manufacturing Ecosystem

Thanks to companies like iRobot and the explosion of Chinese manufacturers (Roborock, Ecovacs, Xiaomi), there’s now a thriving global supply chain for robot vacuum components. Costs have plummeted. A capable SLAM-equipped vacuum with LiDAR can be manufactured affordably, which means the potential market for third-party apps is economically viable.

Humanoids? Tesla is targeting 5,000 Optimus units in 2025. BYD aims for 1,500. Even with aggressive scaling plans, we’re talking about tens of thousands of units in the near term, not millions. The manufacturing ecosystem is still being built.

The Uncanny Valley Problem

There’s substantial robotics research on the “uncanny valley”—the phenomenon where human-like robots that are almost but not quite human trigger discomfort and aversion. First described by roboticist Masahiro Mori in 1970, the effect has been validated across numerous studies using fMRI, behavioral research, and real-world robot interactions.

Recent research has even identified two uncanny valleys: one for highly human-like robots, and another for moderately human-like robots. The brain’s parietal cortex lights up when it detects mismatches between human-like appearance and robotic motion—our visual processing systems literally reject the contradiction.

Robot vacuums don’t have this problem. They’re clearly robots. No one expects them to look human, so there’s no uncanny valley to navigate.

The Privacy Advantage

SLAM-enabled home vacuums use 2D LiDAR sensors that measure distances to surrounding objects—and even that only at floor level. They can detect walls, furniture, people, and pets without actually capturing images. No cameras recording your daily life. No facial recognition. No visual data being uploaded to the cloud.

Even if you’re comfortable with humanoid robots in principle, would you want one watching you with cameras and advanced sensors in your private residence every day? The major humanoid companies—Tesla, Figure AI, Agility Robotics, Boston Dynamics, Apptronik, UBTECH, Sanctuary AI, Unitree, Engineered Arts—are all building robots with extensive visual and sensor arrays. That’s a fundamentally different privacy proposition.

What This Means for Remake.ai

A SLAM-enabled home vacuum cleaner robot isn’t perfect, but it’s:

• Affordable

• Already in millions of homes

• Capable of running apps for entertainment, security, companionship, education, and more

• Privacy-preserving by design

• Accepted by consumers as safe

It’s a platform that exists today, not one I’m betting will exist in five years.

We’re building the first App Store for consumer robots. Starting with vacuums means we can ship to real customers, generate real revenue, and prove the platform model works—before the humanoid wave arrives. And when it does, we’ll already have the infrastructure, developer ecosystem, and consumer trust to expand.

Sometimes the best strategy isn’t to build for the future. It’s to build for the largest deployable base you can reach right now.

I’m Ilia Ovsiannikov, founder of Remake.ai. We’re launching our apps marketplace in 2026. Follow my journey at iliaov.substack.com or ovsy.com.

My solution: hire internationally and fly periodically to co-work in person. Details in my previous post.

In this post I describe how to find, screen and hire remote talent quickly. Here’s what actually works.

The LinkedIn Problem

I Posted a “Full Stack Developer” role → 1,000 applications in 24 hours.

Sounds great until you realize:

• 1,000 resumes to screen (no time as solo founder)

• Schedule interviews with promising candidates

• Co-working sessions (standard for early-stage)

• Negotiate salary, equity, benefits

• Repeat for each role

Time cost: weeks. I stopped after two attempts.

The Upwork Solution

I switched to Upwork (not affiliated, just sharing what works). Here’s my actual process:

1. Hire Freelancers Directly, Not Agencies

Why no agencies for early-stage?

• Pre-PMF, I don’t have a spec. I’m iterating MVP prototypes rapidly—build, test, discard, rebuild. The spec emerges through iteration.

• Agencies need specs upfront

• Quality drops when agencies swap devs without telling you (happened multiple times)

• No agency overhead

• I interview the actual developer, not a manager

• Contract-to-hire path for growing headcount

Exception: If you have a well-defined project scope and aren’t growing staff, agencies can work fine.

2. My Screening Process

• Post job with clear description + skill tags (no paid promotion initially)

• Shortlist: Top 10%+ badge AND in a location I can visit (safe, accessible)

• Filter out hidden agencies:

• Check profiles for “team” mentions

• Skip freelancers with $500K+ earned or 100+ jobs (likely agencies)

• Read feedback between the lines (I’ve been pressured to change <5 star reviews to 5 stars)

• Paid trial task (up to 8 hours)

• Zoom interview with video ON (watch for interview proxy services)

3. Key Questions I Ask

• Explain your project

• Look for genuine signals the dev feels your project is not difficult - confirm the reasons are right

• Are you OK with me visiting to work face-to-face periodically?

• Do you use Claude Code or similar AI tools? (Required for my projects)

• Communication test: Can we understand each other easily?

4. Trial Task

“I’m hiring you for a paid trial (up to 8 hours). You must do this yourself. If you don’t pass, that’s fine—5 stars and we may work together on another project. If you pass, expect regular Zoom video calls.”

This discourages interview proxies.

Cost Optimization

• Use basic Upwork membership (5% fee). Premium (10% fee) shows top 1% badge, but I don’t see meaningful difference vs. top 10%.

• SF Bay Area founding engineer: ~$183K total comp

• International senior engineer: $40-80K (via Upwork)

Result: 2-4x headcount for same budget

I developed autoimmune arthritis a few years after graduating and starting work.

My doctor suggested—and I believed—that my arthritis was triggered by work stress: “Consider working less to keep your arthritis under control.”

But now I’m building a startup. I need to work more, not less. This created a real problem.

The bigger issue wasn’t the joint deformity or pain—it was the immunosuppressant medications. With my immune system suppressed, I got sick easily and frequently, and recovery took longer.

Try building a startup when you’re frequently ill. Try traveling with a weakened immune system.

Years ago, I’d experienced an unexplained remission. My doctor checked my bloodwork, confirmed my remission and took me off the meds. With that in mind, I started paying closer attention to what made my symptoms better or worse.

After a while, I noticed something odd: working at Starbucks seemed to trigger especially strong flares. My rheumatologist restarted my meds immediately.

The final straw was that my renewed medications seemed to have lost their effectiveness. My arthritis kept flaring even as my immune system got weaker. Running out of options and frequently unable to work, I needed to try something different.

After some experimentation, I discovered something surprising — my symptoms went away once I stopped drinking coffee AND tea AND orange juice. I also noticed that not eating acidic foods (ketchup) and not having heartburns also improved my symptoms.

Once I eliminated these dietary suspects, my symptoms improved rapidly. My rheumatologist let me off the meds completely as my blood tests showed no inflammation. I’ve been in remission for a year now, with regular monitoring.

My immune system seems to have recovered — I’m no longer getting sick frequently. This has allowed me to work on my early-stage startup.

Here’s what I still find puzzling: I’m now working 24/7 with considerable stress, but my arthritis remains in remission. My doctor had originally suggested stress was a trigger.

In my case, it seems possible that it wasn’t the stress itself, but rather the lifestyle changes that came with transitioning from student to professional—specifically, starting to drink coffee and tea regularly. Of course, I can’t know this for certain.

Again: This is my individual experience with my specific case of arthritis. Your disease is different. Your treatment should be different. Work with your doctor.

After three months of searching—including creative outreach that generated half a dozen face-to-face interviews—I found exactly zero cofounders.

The Expiration Date

Here’s the thing about cofounder searches: they have an expiration date. At some point, the business needs to move to its next phase, with or without the perfect cofounder. I hit that point two weeks ago during a manufacturing trip to China.

While there, I did some math. The cost of flying to China and staying for two weeks was two orders of magnitude less than paying a single month’s SF Bay Area salary for an engineer ($2K/month vs $160/year + $2K/month benefits + $25K capital rais bonus + equity).

That’s when I realized I was optimizing for the wrong constraint.

Splitting the “Ideal” Cofounder

I was looking for someone who was both a 10x engineer and had top-tier credentials. But what if I split that persona into two separate hires?

New approach:

Hire 1: Remote high-performer via Upwork

- $20-80/hour (2x to 4x cheaper than SF startup salaries)

- Autonomous, enthusiastic, hard worker

- Location: anywhere

Hire 2: Part-time advisor with tier-1 credentials

- Equity only

- Primary role: help with fundraising credibility

- Location: anywhere

My wife is okay with me traveling, so I dropped the location filter entirely.

The Problem With Remote at This Stage

Here’s where it gets tricky. I’m still at the very early MVP stage. The ideation and trial-and-error loop is brutally fast—especially because I’m using Claude Code to implement ideas. (I use Claude for everything in MVP prototyping, and it’s been fantastic.)

The problem: that fast iteration loop doesn’t work well with remote. If I send specs in the evening, get implementation in the morning, test during the day, and repeat—we’re already adding 12+ hour delays to each cycle. When you’re running multiple cycles per day in-person, going remote feels like moving through molasses.

The Solution: Hybrid “Out-Person” + Remote

Here’s what I’m planning:

- Fly to the freelancer’s city

- Stay in a hotel for 1-2 weeks

- Bring hardware and MVP prototype with me

- Work face-to-face to get them to 100% productivity

- Return home

- Work remotely until the next in-person sprint is needed

- Repeat

The cost structure makes sense. A week or two of travel + accommodation + focused work is still 10x dramatically cheaper than SF salaries. And I can work from anywhere with my laptop.

What do you think about it?

- Has anyone else tried this hybrid approach? How did it work out?

- What are the failure modes I’m not seeing?

- Is this sustainable long-term?

I know this is unconventional. Most advice says “MVP phase = everyone in the same room.” But most advice also assumes you have the capital to pay SF salaries or the network to find cofounders quickly.

I don’t have either, but I do have a laptop, Claude Code, and the ability to get on a plane.

Maybe that’s enough.

How startups are changing in 2025.

As I search for a cofounder for Remake.ai, I’ve been talking to founders of AI startups that recently ran out of runway.

Surprisingly, many of these startups had traction. In some cases, they had a product–market fit.

What went wrong?

A lot of these companies were AI wrappers — web apps wrapped around online LLMs like ChatGPT or Anthropic.

Here are a few AI wrapper type startup ideas:

• “AI patent attorney”

• “AI financial advisor”

• “AI recruiter that ranks job applicants”

Need more ideas? Prompt ChatGPT “Generate ideas for AI startups.”

The problem with AI wrapper startups is - while they’re easy to build, they often have no competitive moat.

Let me give you a worked example. Suppose I wanted to build PatentAnything.ai, an AI startup that helps you patent anything, almost instantly.

• PatentAnything.ai takes your idea, checks if it’s patentable, and—if it is—writes the draft, adds drawings, handholds you through the USPTO filing process and collects a convenience fee.

  “Save big on patent attorney fees! File your patent in minutes, before someone else does!”, PatentAnything.ai pitches to the (apparently experienced) inventor.

• If your idea is NOT patentable, AI suggests creative ways to MAKE it patentable. “Make your idea patentable using AI instantly!” PatentAnything.ai pitches to the (apparently amateur wannabe) inventor excited to and, hopefully, willing to pay the convenience fee at the spur of the moment.

Here’s how I would build PatentAnything.ai:

• Frontend in Next.js

• Backend calling OpenAI or Anthropic APIs

• Stripe API for payments

• Hosted on Vercel with serverless functions

• All code generated by Claude Code from a single prompt

That’s a one-day MVP. But that’s also the point: if I can build it in a day, so can someone else.

Disclaimer - PatentAnything.ai is a trivialized example. You can build a competitive moat using other methods. For example, you can clone Facebook technically but never outcompete it due to Facebook’s network effect moat.

Lack of competitive moat makes businesses fail. Importantly, one can build an AI wrapper, find traction, raise capital and appear to be a successful startup - only to find that your success has attracted copycats who compete with you on price, changing your race to the top to a race to the bottom.

This is why I’m building Remake.ai, NOT an AI wrapper startup.

You may ask, why makes Remake.ai so special? How is it different from AI wrappers? What is Remake.ai competitive moat?

Answering this question may require a separate post. I believe Remake.ai can be for consumer robots what Apple is for smartphones.

Interested? Let’s talk more! Drop me a message.

#robotics #AI #startups #founders #VC #deeptech #remakeai

🤦 3. My real pain: Claude’s presentations and artifacts

This one really hurts.
Claude makes great presentations — in HTML.
Not PowerPoint. Not Google Slides.
HTML.

If you’ve ever tried converting HTML slides to PowerPoint, you know the pain.

My current workflow:
- Edit HTML + CSS by hand.
- Take screenshots of each slide.
- Paste into PowerPoint or Google Slides.
- Resize until fonts look right.
- Export to PDF.

Yes, it works. But it feels like building slides before PowerPoint existed.

Longer decks (15–20 slides) often break mid-generation. Claude usually fixes them — but it can take 5–10 iterations before I get a clean version.

Still, I’ll take that over writing decks from scratch.

🤔4. Sometimes Claude doesn’t know something

Rarely, but this can happen. Specifically, I had to help Claude when coding for the ROS2 robotics platform.

Claude assumed a certain way to run a ROS2 node, which failed during testing.

My fix:
I give Claude a working example file. She reads it, adapts instantly, and corrects herself.

With the right example, Claude becomes an instant domain expert.

🪰5. Debugging and testing

Claude debugs surprisingly well — but someone still needs to act as the architect, debugger, and systems engineer.

In my project she installs npm modules, Python libraries, sets up venv and runs tests, but I usually handle the environment setup — like installing ROS2 or configuring GPUs.

I use VS Code with Claude’s plugin. It’s fantastic — no need for Cursor.

❤️ Why I Love Claude (No Matter What)

Because the trade-off is unbeatable.

Claude writes my code, drafts my business plans, helps me explore funding strategies, drafts NDAs for manufacturers, and even gives feedback on resumes.

I can ask (and have asked over and over):
- “What if I raise early and aggressively?”
- “What if I bootstrap longer?”
- “How should I pitch to investors vs job applicants vs factories in China?”

Claude answers in seconds — and helps me think harder. That said, it does take *me* tens of minutes to digest Claude’s advice. That’s my flaw, not Claude’s.

So yes, Claude has flaws.
But overall, she’s the best cofounder I’ve ever had — energetic, brilliant, and humble enough to learn.

Final thought:
If you’re a founder or engineer considering using Claude for real-world product work — do it.
Just remember: treat her like a promising junior developer.
Guide her with senior wisdom.
And enjoy the 48× speedup.

#AI #startups #founderupdate #ClaudeAI #productivity #robotics #remakeai

Think for a moment - there are 50M+ consumer robots already in homes — vacuums, drones, companions — many equipped with sensors, compute, navigation and connectivity.

For now, most of these are closed systems. That’s like owning an iPhone that can only make phone calls.

Imagine being able to download a “robot app” to your home robot - making the robot play ball with your child.

How about a hide-and-seek app? A race with kids app? Play fetch app? Clean room? Patrol your house? Watch kids and pets? Keep company for seniors? Teach kids? Play with pets?

We’re building the first App Store for consumer robots. Think Google Play for intelligent machines.

We are also building the platform making this possible - think Android for robotics.

Developer ecosystem included. AI-first from day one.

Recent Progress

• $1M of personal capital committed (I draw no salary, work full-time)

• Prototype live, built using Claude.ai

• Interest from six manufacturers, with one partnership ready to sign

• China visit scheduled to meet and finalize manufacturing partners

• Beta launch early 2026 (if all goes well)

This level of traction came without outside funding.

Now, I’m looking to start scaling out.

What’s Next:

• I’m hiring a Cofounder and Founding Engineers in San Jose, CA. Please see the job posting.

• We’ll be expanding pilot integrations with hardware partners and launching our developer SDK soon after.

• I’m looking for advisors, with an equity stake

• I’m looking for introductions to infrastructure platform VCs

The Opportunity

The consumer robotics market exceeds $4B today (home vacuum cleaner robot alone). Outside the home vacuum cleaners, estimates run 10x higher.

Remake.ai aims to become the platform layer powering that transformation.

It’s exciting to see visionary firms like Lux Capital, Playground Global, Eclipse, and Accel already leaning into frontier robotics and AI infrastructure, anticipating the incoming wave.

Please sign up for our upcoming platform launch at https://Remake.ai

Here’s the short version: I tried. I posted, networked, recruited, cold-emailed, and joined YC’s matching platform. After a month, I had spoken with some interesting people, but I wasn’t much closer to finding the right partner. Meanwhile, the startup couldn’t just pause while I searched. I kept building, kept testing, and kept moving toward market fit.

During that same period, something unexpected happened: my development speed exploded.

I started experimenting with what I first called “vibe coding” (though, more accurately, it’s “delegation coding”). The idea is simple: I design the system architecture, set the requirements, and let AI handle almost all of the actual coding. I then step back into the role of tester, debugger, and integrator.

The results? Astonishing. Tasks that used to take a week now take a day. In other words: 10x faster development. Delegation coding has replaced what would have been a small team of remote developers, but instead of waiting overnight for pull requests, I get working code in minutes.

So I decided to push the experiment further. I made AI—specifically, Claude from Anthropic—my cofounder. At least for now.

Why? Because when you think about it, an AI cofounder has some remarkable advantages:

• You get an exceptionally talented, energetic “100x developer” with extraordinary breadth and depth of knowledge.

• A great listener, always willing to bounce ideas and brainstorm.

• No cofounder breakups. Humans have disagreements, life changes, or fallouts. AI doesn’t.

• Cost: $20 per month, instead of equity or salary. No vacation days required.

• No relocation. My AI cofounder is always online, wherever I am.

This flips some core assumptions about startups:

• Why quit your full-time job if you can “vibe-build” not just an MVP but a full-scale product on nights and weekends?

• Why chase accelerators or funding rounds if your burn rate is near zero?

• Why keep searching for a human cofounder when an AI can already help you ship at scale?

To be clear: AI is not a perfect replacement for a human cofounder. There are gaps and limitations. But as of today, I’ve found that it’s the best “cofounder” I could ask for.

In my next post, I’ll talk about those limitations—because they matter too. But for now, let me just say this: the cofounder I couldn’t find in the Bay Area, I found in the cloud.

#robotics #AI #startups #founders #remakeai

Y Combinator (my startup accelerator idol) strongly recommends that startups have cofounders, and I believe that having one could significantly improve my chances of getting accepted. At the same time, I should admit: I don’t strictly need a cofounder. I’m technical and hands-on enough to build everything myself. Beyond that, I can hire, contract, or outsource the rest—from business operations to sales.

Still, I decided to give the search an honest try. Here’s what I’ve done so far:

1. Announced the startup publicly. I came out of stealth on LinkedIn.

2. Signed up for YC’s cofounder matching platform. I’ve connected with a few people there, but progress has been slow. About half of my “matches” are really using the platform to get hired, rather than to cofound.

3. Reached out to former colleagues. These are people I consider 10x engineers and would gladly work with again. About half replied, but none were available.

4. Posted online. I shared my search on LinkedIn, relevant subreddits, Hacker News and industry forums. Mostly silence. I did post in r/roastmystartup and got a helpful reply—thank you, internet stranger! Posting on Hacker News under the title of “Roast my startup” got me another reply.

5. Posted a job opening on LinkedIn. Another experiment, with similar results.

6. Hired recruiters.

After about a month of this, here’s what I’ve learned:

• The potential cofounder pool is small. YC’s platform shows about 16K total, worldwide.

• The filtered pool is tiny. For people in the Bay Area, interested in robotics, and available to quit their jobs soon, that number drops to just a few hundred.

• After manually reviewing profiles, I’ve identified about a dozen I’d actually like to talk to.

• Response rates aren’t great:

  • ~50% of invites are declined.

  • ~20% of accepted invites never lead to a meeting.

  • Of those I meet, about half are really just looking for a job.

• On the bright side, there’s a surprisingly wide range of aspiring cofounders—ages 16 to 70.

All in all, it hasn’t felt like much progress.

But—I think I may have found a different solution. One that I genuinely love. It might seem unusual, surprising, or even a little shocking.

I’ll share it in my next post.