Forem: Krunal Kanojiya

Sam Altman Dropped Some Hard Truths About AI

Krunal Kanojiya — Sun, 01 Feb 2026 06:45:30 +0000

On January 27, 2026, Sam Altman sat down with a room full of nervous developers in San Francisco. No slides. No polished pitch. Just him answering the questions everyone's been afraid to ask out loud.

Things like: Is my coding career over? How do I compete if anyone can build software now? What happens next?

He didn't sugarcoat it. Here's what he said, broken down into things you can actually do something about.

1. Coding Jobs Aren't Dying — They're Changing

Everyone's panicking that AI will replace developers. Altman pushed back on this.

Think about it this way: cars made driving faster, but people didn't drive less. They drove more. The same thing is happening with software. AI makes building faster, so the world is going to want more software, not less.

What changes is what your job looks like. You'll spend less time typing code and more time telling the AI what to build and why. The job title stays the same. The actual work looks completely different.

The shift: We're going from "writing code" to "directing outcomes."

2. The Real Problem Isn't Building — It's Getting Noticed

Back in the day, the hard part of starting a company was actually making the product. You needed developers, servers, months of work.

Now? A single person can ship a product in a weekend.

So what's the problem? Everyone else can too. The market is flooded.

Altman's take: the bottleneck isn't the product anymore. It's getting people to care about it. Anyone can build something. Very few people can make something that people actually pay attention to.

What this means for you: Stop worrying so much about your tech stack. Focus on who you're building for and how they'll find you. If you can't prove people want it before you build it, you're just wasting your time.

3. Software Is About to Get Weirdly Personal

Right now, software is one-size-fits-all. Microsoft Word looks the same on your screen as it does on mine.

Altman thinks that's about to end. He sees a future where software reshapes itself around you — not the other way around.

Picture a project management tool that doesn't just let you customize a few settings, but actually rearranges its entire layout based on how you work. Every single time.

The opportunity: Don't build rigid tools. Build tools that can adapt on the fly based on what the user actually needs in that moment.

4. AI Is Making Things Cheaper — But Not Everyone Wins

Here's the uncomfortable part. AI is driving the cost of knowledge and digital work down toward zero. That sounds great, right?

It is — if you're the one using that leverage. A single person with the right AI tools can now do what a small team used to do.

But if you're just selling your hours? That's a shrinking market. The people who benefit most are the ones who own the tools or know how to use them at scale.

The lesson: You need to be on the side that's using the leverage, not the side that is the leverage.

5. By 2027, AI Thinking Will Be Basically Free

Altman got specific here. The cost of running AI models is about to drop — potentially 100x by the end of 2027.

But there's a catch. Speed and cost are becoming a trade-off. Background tasks? Use the cheap, slower models. Need an answer right now in a live product? That'll cost a bit more.

The split: Cheap and slow for batch work. Fast and premium for anything the user is waiting on.

6. The Biggest Risk Right Now Is Bio-Safety

This was the serious part of the talk. Altman was blunt: the scariest thing AI can do right now isn't writing bad code or making deepfakes.

It's biology. Current models already know too much about how biological systems work, and that's dangerous in the wrong hands.

His take on how to handle it? Stop trying to prevent AI from ever knowing dangerous things — that ship has sailed. Instead, build systems and policies that can withstand bad actors. Think fire codes, not fire bans.

7. What You Should Actually Be Learning

For kids? Altman said keep them away from AI for now. Let them play, move around, interact with real people and real objects. The basics still matter.

For the rest of us, the game has changed. Memorizing syntax and following tutorials isn't going to cut it anymore. What matters now is:

Can you make things happen? Not just write code — actually get results in the real world.

Can you recover when AI gets it wrong? Because it will. Knowing how to spot and fix AI mistakes is a real skill.

Can you unlearn fast? The tools change every few months. If you're still clinging to how things worked last year, you're already behind.

8. How Companies Are Actually Hiring Now

OpenAI is still hiring, but the way they test candidates has flipped completely.

Old way: "Here's a problem. You have two weeks. Go."

New way: "Here's that same problem. You have 20 minutes. Use every AI tool you can."

If you're avoiding AI tools because it feels like cheating, you're not being humble — you're making yourself hard to hire. Nobody cares how you solved it. They care how fast.

"But AI Still Gets Things Wrong…"

Yeah, it does. Altman didn't pretend otherwise.

Here's the math problem though. If an AI is 99% accurate on any single step, but your task has 100 steps, the chance of getting through all of them perfectly is actually pretty low. That's just how probability works.

So the next big push isn't making AI smarter. It's making it more reliable — consistently right over long, complex tasks. That's what GPT-5 and the models after it are focused on.

Don't build your whole strategy around the assumption that AI will always be this flaky. It's improving faster than almost anything else in tech history.

So What Do You Actually Do Right Now?

Look at your week. What are you still doing by hand out of habit or pride? That's your first target to automate.

Learn how to chain tools together. One AI doing one thing is fine. Knowing how to connect three of them to solve a bigger problem? That's the real skill.

Talk to people before you build. Building is cheap now, which means the market is noisy. Prove someone wants what you're making before you write a single line of code.

Get better at the human stuff. Negotiation, creativity, empathy, leadership — these are the hardest things to automate, and they matter more than ever.

Time yourself. Seriously. Try doing your normal daily work 50% faster with AI tools. If you can't, you haven't figured out how to use them right yet.

The future isn't something coming down the road. It's already here. The only real question is whether you're going to be the person getting automated — or the person doing the automating.

What's one thing you could automate this week but haven't? Drop it in the comments.

React Native vs Native Android Cost: Which One Saves You More Money?

Krunal Kanojiya — Fri, 30 Jan 2026 08:36:24 +0000

At a Glance: React Native costs less upfront and works for both iOS and Android. Native Android costs more at first but runs better for complex apps. Your final bill depends on what your app needs to do, not just which tool you pick.

The Money Question Everyone Asks

You want to build an app. The first question: how much will it cost?

React Native usually wins on price. One codebase works on iOS and Android. Smaller teams build faster. Perfect for startups testing ideas without burning cash.

Native Android costs more. You need separate code and specialized developers. But you get better hardware control and stability. Good for apps where performance matters most.

Let's break down the real costs so you can pick what fits your budget.

What You're Actually Paying For

App costs aren't just about writing code. Money goes to different buckets:

UI/UX Design

How your app looks and feels. Good design keeps users around. Bad design sends them running to competitors.

Frontend Development

The part users touch. Buttons, screens, animations. Costs rise with complex features and multiple platforms.

Backend and APIs

The engine under the hood. Handles data, connects to servers, talks to payment systems. Real-time features and heavy data needs drive up costs here.

Third-Party Integrations

Payment processing, maps, social logins. These services charge fees or take usage cuts.

Testing and QA

Finding bugs before users do. More features mean more testing. More devices mean more work.

App Store Deployment

Getting your app on Google Play or Apple's App Store. Includes prep work and store fees.

Ongoing Maintenance

Updates, bug fixes, new features. Never stops. Popular apps with many users cost more to maintain.

React Native: Where Your Money Goes

React Native cuts costs by using one codebase for both platforms. Here's how that saves you money:

Faster MVP Delivery

Build once, run everywhere. Get your minimum viable product out fast. Start collecting user feedback sooner. Time is money.

Lower Hiring Cost

Hire React Native developers instead of separate iOS and Android teams. One framework, one skill set. Cuts labor costs nearly in half.

No Parallel Development

Two native teams mean double coordination headaches. React Native uses one team building one thing. Simpler. Cheaper.

Native Android: What Drives the Price Up

Building native means platform-specific code in Kotlin or Java. Costs more because:

Higher Developer Specialization

Native Android developers need deep skills. Kotlin or Java expertise. Android design patterns. APIs and tools specific to Google's ecosystem. Specialized talent costs more.

Better Access to Android APIs

Direct connection to Android's core features. Custom notifications, background services, complex animations. Apps needing these features benefit from native development.

Separate Cost if iOS is Added

Want iOS too? Build a whole new app in Swift or Objective-C. Separate codebase, separate team, doubled development budget.

Initial Development: Head to Head

React Native wins the starting race.

MVP Timelines

React Native ships MVPs faster. One small team handles both platforms. Perfect for quick launches and early feedback.

Team Requirements

React Native needs one development team. Native Android needs two teams if you want iOS. Simple math: fewer people means lower costs.

Speed vs Quality Trade-off

React Native updates both platforms at once. Fast iterations. Native Android needs deeper work but delivers better performance. Higher quality costs more time and money.

What Developers Cost You

US market rates (2025 data):

React Native Developer

Annual: $90,000 to $130,000
Freelance: $50 to $100 per hour
15-30% cheaper than Native Android

Native Android Developer

Annual: $110,000 to $150,000
Freelance: $70 to $120 per hour

React Native developers are easier to find and cost less. Native Android specialists command premium rates.

The Costs Nobody Talks About

Hidden expenses sneak up on you:

Technical Debt

Rush your code and pay later. Bad decisions early make improvements expensive down the road.

Poor Architecture

Messy code structure makes updates painful. New features take longer. Costs pile up.

Performance Refactors

Slow app? Major fixes cost serious money. Better to build it right the first time.

Developer Handovers

Switching developers mid-project causes delays and bugs. Fixing these mistakes costs extra.

Store Policy Changes

Apple and Google update rules regularly. Your app needs updates to stay compliant. Budget for this.

Side by Side Comparison

Cost Factor	React Native	Native Android
Initial Development	Lower (shared codebase)	Higher (separate platform code)
Hiring	Cheaper, more available	Expensive, specialized skills
Performance Fixes	Higher for complex features	Lower, built for Android
Maintenance	Lower, one codebase	Higher, separate codebases
Scaling	Cheaper across platforms	More expensive for Android-only

Making Your Choice

Need a quick prototype on a tight budget? React Native makes sense. Great for cross-platform apps.

Building something complex with advanced features? Native Android delivers better performance and stability for Android-focused apps.

Your choice depends on:

How fast you need to launch
Your total budget
Performance requirements
Long-term plans (one platform or many)

Wrapping Up

Both options work. React Native saves money upfront and ships faster. Native Android costs more but runs better for demanding apps.

Pick based on your needs, not trends. Know your budget. Understand your timeline. Match the tool to your goals.

Need help deciding? Talk to developers who know both. Get estimates. Make an informed choice that fits your business.

Still have questions? The right framework for your app depends on your specific situation. Consider what matters most: speed to market, budget limits, or app performance.

How Spotify Splits Its Recommendation Systems (And Why It Matters)

Krunal Kanojiya — Thu, 29 Jan 2026 08:54:53 +0000

As more companies use AI to make decisions, they face a tricky problem. The same system is asked to do two different jobs at once. This causes issues, especially for big platforms like Spotify.

Spotify learned this the hard way. They built systems to recommend music and podcasts. But those systems had to handle two tasks:

Serve users in real time - Fast, stable, reliable
Run experiments to improve - Flexible, careful, willing to fail

These jobs don't mix well. So Spotify split them apart.

Why Two Jobs Need Two Systems

Your recommendation engine needs to be fast. When a user opens the app, they expect results now. Any delay or crash hurts the experience.

Your experiment system needs to be thorough. It tests ideas, compares results, and learns from failures. Speed matters less than getting the right answer.

When you mix these systems, both suffer:

Experiments slow down your app
Production bugs mess up your test data
Changes become risky
Teams step on each other's work

Spotify kept hitting these problems. So they made a choice: separate the systems completely.

What This Split Looks Like

Personalization systems handle live requests. They focus on:

Low delay times
High uptime
Stable performance
Serving millions of users

Experiment systems run tests offline. They focus on:

Accurate measurements
Clear tracking
Safe testing
Good data quality

When you separate them, each system gets better at its job. Experiments can change often without breaking production. Production stays stable while new ideas get tested.

If an experiment fails, users never see it. If production has an issue, your experiment data stays clean.

How Models Move to Production

Spotify doesn't push models straight to users. First, they go through an evaluation path:

Build a model in the experiment system
Test it against current models
Check the results carefully
Debate if it actually helps
Only then move it to production

This matters more as AI gets harder to understand. Small changes can cause big effects. Often you only find problems after users notice them.

The split gives teams time to think. They can ask hard questions:

Did this change actually help?
Did it hurt something we didn't measure?
Are we sure we understand what happened?

The Real Challenge: Coordination, Not Models

The hard part isn't building better models. It's getting people to work together.

Splitting systems forces teams to agree on:

How data moves between systems
Who owns what
What tools everyone uses
How to track and review changes

This takes work. It adds process. It slows things down in places where speed used to feel important.

But that slowdown helps. It creates space to ask if you're building the right thing. It lets teams test ideas without committing to them. It gives clearer signals about what's safe to ship.

Why This Matters More Now

As AI takes on more work, you need better ways to check if it's doing the right thing. Models are black boxes. Small changes can have wide effects.

By keeping experiments separate, Spotify can:

Catch issues before users see them
Keep a clear record of decisions
Roll back changes easily
Build trust in what they ship

This isn't just about having a staging environment. It's about building systems that support disagreement, measurement, and learning.

What You Can Take Away

You probably don't run Spotify-scale systems. But the lesson still applies.

Many teams run experiments inside production because it feels simpler. At first, it is. Over time, that simplicity breaks down:

Changes get harder to explain
Rollbacks become scary
You lose confidence in results

Separating experiments from serving takes upfront work. But it pays off:

You get clearer answers
Teams make better decisions
Risk goes down
Trust goes up

As AI systems take on more responsibility, these things matter. The infrastructure you build shapes how your team behaves. When systems favor learning over speed, people make better long-term choices.

That might be the most practical lesson here.

Latest SAP Trends Reshaping Business Technology in 2026

Krunal Kanojiya — Sun, 25 Jan 2026 08:29:31 +0000

The clock is ticking for businesses running older SAP systems. With mainstream support ending in 2027, companies face an urgent need to modernize their enterprise software. This shift represents one of the biggest technology changes in recent business history.

SAP has long served as the foundation for how global companies manage their operations. But the way these systems are built and run has changed completely. The latest SAP trends show a clear move toward cloud platforms, intelligent automation, and systems that adapt to business needs in real time.

This article breaks down the five most important trends shaping SAP technology in 2026. We'll also cover what business leaders should do now to stay ahead.

Why SAP Modernization Can't Wait

Many companies still run SAP ECC, the older version of SAP's main business software. This system has worked well for years, but SAP will stop supporting it in 2027. After that date, companies won't get security updates or technical help.

The shift to modern SAP isn't just about avoiding problems. It's about gaining new capabilities that older systems simply can't provide. Companies that wait too long will face rushed timelines, higher costs, and greater risks.

The good news? The latest SAP trends offer clear paths forward. Here's what's changing and why it matters.

AI That Actually Does the Work

For years, businesses have used AI tools for simple tasks like answering questions or creating reports. The latest SAP trends show AI taking a much bigger role.

SAP's AI agents can now handle complex business processes on their own. These agents work directly with your company data to resolve payment disputes, match financial records, screen job candidates, and check compliance requirements.

This is different from older AI tools that only made suggestions. Today's AI can complete entire workflows without human help. Companies using these systems report 60% to 75% less manual work on key business processes.

Here's what this means for your business:

Faster operations: Tasks that took hours now finish in minutes
Fewer errors: AI follows the same steps every time
Better decisions: Systems surface problems before they grow
Lower costs: Staff can focus on work that needs human judgment

AI is no longer a side project. It's becoming a standard part of how SAP systems function. Companies that adopt AI workflows early will have a significant advantage over competitors still doing everything manually.

Clean Systems Are Now Required

One hard lesson from past SAP upgrades is simple: too much customization creates long-term problems.

When companies heavily customize their SAP systems, they make updates difficult and expensive. Every time SAP releases new features, customized systems need special work to stay compatible. This creates delays, bugs, and mounting technical debt.

SAP's answer is the Clean Core approach. This means keeping your main SAP system as close to standard as possible. Any custom features you need go into a separate platform called SAP Business Technology Platform (BTP).

Think of it like this: your core SAP system stays clean and easy to update. Your unique business needs live in a connected layer that doesn't interfere with the main system.

Companies that started with Clean Core can now apply SAP's regular updates without major disruptions. Companies with heavy customization struggle to keep their systems stable.

By 2026, Clean Core isn't optional anymore. It's required for managing system risk and staying current with new features.

Bluefield Migration Offers the Best Path

Early conversations about moving to S/4HANA (SAP's modern system) focused on two choices. Greenfield meant starting fresh with a brand new system. Brownfield meant moving your existing setup as-is.

The latest SAP trends show most companies choosing a third option called Bluefield.

Bluefield lets you move only the data you actually need while redesigning your business processes at the same time. You don't have to rebuild everything from scratch like Greenfield. But you also don't carry forward years of inefficient processes like Brownfield.

This approach gives you:

Speed: Faster than building new systems from zero
Control: You decide what stays and what goes
Better processes: Fix problems instead of copying them
Less disruption: Keep running during the transition

As the 2027 deadline approaches, Bluefield provides the right balance for most organizations. It turns a technical upgrade into a real business improvement.

Sustainability Becomes a Daily Metric

Environmental reporting used to mean collecting data at the end of each quarter and writing summary reports. The latest SAP trends show sustainability moving into real-time financial tracking.

SAP's Green Ledger and Sustainability Control Tower now track environmental impact at the transaction level. Every purchase order, shipment, and production run shows both its financial cost and its carbon footprint.

This matters because regulations are getting stricter. The Corporate Sustainability Reporting Directive (CSRD) and similar rules require detailed environmental reporting. Companies need accurate data, not estimates.

With sustainability built into SAP, CFOs can see how daily business decisions affect environmental goals. Finance teams can track sustainability just like they track costs and revenue.

This changes how companies think about sustainability. It's no longer a separate reporting exercise. It's a performance metric that affects daily operations and long-term planning.

New Management Tools Replace Old Systems

SAP is moving away from older management tools that required heavy customization and manual work. The latest SAP trends show SAP Cloud ALM becoming the standard for system management.

SAP Cloud ALM comes ready to use with features like:

System monitoring: Track performance and catch issues early
Automated testing: Check updates before they go live
Process visibility: See how changes affect business operations
Built-in analytics: Understand system health at a glance

For companies using RISE with SAP (SAP's complete cloud package), Cloud ALM is now the default tool. It reduces the work needed to keep systems running smoothly and lowers long-term costs.

This shift fits SAP's broader goal of making enterprise systems easier to manage. Teams spend less time on maintenance and more time on improvements that help the business.

What Business Leaders Should Do Now

Success with SAP modernization requires more than just meeting the 2027 deadline. The companies that benefit most will have clear goals and strong leadership support.

Here's your readiness checklist for 2026:

Check your BTP readiness: Understand what you'll need for Clean Core
Define your strategy: Decide how to separate core functions from customizations
Find AI opportunities: Identify processes where automation adds real value
Set up sustainability tracking: Build environmental reporting into your financial systems
Align your tools: Start using SAP Cloud ALM for system management

The latest SAP trends point to systems that are simpler to run, more automated, and more resilient. Companies that treat this as a business transformation (not just a technical project) will see the best results.

Waiting beyond 2027 means facing tighter deadlines and higher risks. The time to act is now.

Moving Forward with the Latest SAP Trends

SAP modernization represents one of the biggest technology shifts businesses will face in the coming years. But it's not just about upgrading software. It's about building systems that work better for your daily operations.

The latest SAP trends show clear directions: AI that handles real work, clean systems that stay easy to update, migration approaches that balance speed and quality, sustainability tracking that meets regulatory needs, and management tools that reduce operational burden.

Companies that embrace these trends will be ready for whatever comes next. Those that delay will find themselves with outdated systems and limited options.

The question isn't whether to modernize SAP. It's how quickly you can do it while setting your business up for long-term success.

Is your organization ready for 2027? The latest SAP trends provide a roadmap. The only thing left is to start moving.

What is Databricks? A Simple Guide for Beginners

Krunal Kanojiya — Sun, 25 Jan 2026 03:45:41 +0000

Databricks is a cloud platform that helps teams handle huge amounts of data. Think of it as a workspace where data workers can collect, clean, and study information together.

The platform runs on cloud services like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud. Companies use it to make sense of their data and build smart apps.

Who Uses Databricks?

Three main groups work with Databricks:

Data engineers build systems that move and store information. They make sure data flows smoothly from one place to another.

Data scientists study patterns and create models. They use math and stats to predict what might happen next.

Business analysts look at reports and charts. They help companies make better choices based on facts.

All three groups can work in the same space. This makes teamwork easier and faster.

What Makes Databricks Special?

Everyone Works Together

Teams don't need separate tools anymore. Engineers, scientists, and analysts share the same workspace. This cuts down on confusion and speeds up projects.

It Grows With Your Needs

Start small and add more power when needed. Databricks can handle tiny datasets or millions of records. The system adjusts itself based on how much work you have.

Built-In Tools

You get everything in one place:

Code editors that work in your browser
Charts and graphs for showing data
Ways to clean messy information
Tools for building smart models

No need to buy and connect dozens of different programs.

Strong Security

Databricks keeps data safe with locks and codes. You can control who sees what. Everything gets tracked so you know who accessed information and when.

What Can You Do With Databricks?

Clean and Organize Data

Raw data is often messy. It might have mistakes, missing parts, or odd formats. Databricks helps you fix these problems and get data ready for use.

Build Reports

Create charts that update on their own. Connect to your data and watch numbers change in real time. Share findings with teammates through simple dashboards.

Train Smart Models

Use AI to spot patterns humans might miss. Build systems that can predict sales, detect fraud, or recommend products. The platform includes popular tools like TensorFlow and PyTorch.

Handle Live Data

Work with information as it comes in. Track website clicks, sensor readings, or customer actions the moment they happen. Make quick choices based on fresh data.

Store Information Long-Term

Keep years of records in one place. The platform uses something called a data lakehouse. This combines the best parts of databases and file storage.

Key Parts You Should Know

Clusters

A cluster is a group of computers working together. They split big jobs into smaller tasks. This makes work finish faster than using one machine alone.

You can start clusters when needed and turn them off to save money.

Notebooks

Notebooks are where you write code and take notes. They look like digital journals. You can mix code, text, and pictures in one place.

Think of them as interactive documents. Run a bit of code and see results right away.

Workspaces

A workspace holds all your projects. It's like a folder system in the cloud. Keep notebooks, data files, and settings organized here.

Teams can share workspaces to stay in sync.

Jobs

Jobs run tasks on a schedule. Set them up once and let them repeat. For example, pull new data every morning at 8 AM.

This saves time on boring, repeated work.

Real-World Uses

Online Stores

Track what customers buy and when. Predict which products will sell best. Send personalized deals based on shopping habits.

Banks

Spot strange account activity that might be fraud. Check transactions as they happen. Keep customer data private and secure.

Healthcare

Study patient records to improve care. Find which treatments work best. Keep health information safe under strict rules.

Manufacturing

Watch machine sensors for warning signs. Fix equipment before it breaks. Track products from factory to customer.

Why Companies Choose Databricks

Save Money

Pay only for what you use. Turn off computers when projects are done. No need to buy your own servers.

Work Faster

Teams finish projects in weeks instead of months. Ready-made tools mean less setup time. Changes happen with a few clicks.

Stay Current

The platform updates itself. New features appear without you doing anything. Security patches install on their own.

Get Support

Databricks offers training and help. Find answers in docs, videos, and forums. Contact support when stuck.

Getting Started Tips

Start Small

Pick one simple project first. Maybe clean up a customer list or create a basic report. Learn the basics before tackling big tasks.

Use Free Resources

Databricks offers free trials and learning materials. Watch tutorials and try examples. Practice with sample data before using real information.

Join the Community

Connect with other users online. Ask questions in forums. Learn from people who already use the platform.

Plan Your Setup

Think about who needs access. Decide how to organize projects. Set up security rules early.

Common Challenges

Learning Curve

The platform has many features. It takes time to learn them all. Focus on what you need right now. Ignore the rest until later.

Cost Control

Cloud bills can grow fast if you're not careful. Watch your usage. Turn off clusters when done. Set spending limits.

Data Quality

Garbage in means garbage out. Spend time cleaning your data first. Bad information leads to wrong answers.

The Bottom Line

Databricks helps teams handle data without the usual headaches. It brings tools together in one spot. Companies get insights faster and spend less on setup.

The platform works best when teams already know what they want to do with data. It's not magic, it makes hard work easier.

Whether you're building reports, training AI models, or just trying to organize information, Databricks gives you a solid place to start.

Next Steps

Ready to try it out? Sign up for a free account. Follow a beginner tutorial. Start with small tasks and grow from there.

The platform continues to add new features. Stay curious and keep learning. Your skills will grow along with your projects.