Sanjay

Tech Stories, Ideas and Experiences - Following AI Growth - CTPO at Loylty Rewardz (A Billdesk Company)

[Sanjay @sansor]

The next breakthroughs won’t come from scaling tokens.

They’ll come from changing the objective.

Link - https://t.co/EKj5SGzN6j

[Sanjay @sansor]

Why scaling tokens isn’t scaling intelligence?

LLMs are great at fluency.
They write, code, summarize, and sound confident.
But fluency isn’t understanding.
That’s why we keep adding layers:
RAG, tools, agents, memory, guardrails.
Useful fixes—but also a signal.

[Sanjay @sansor]

Intelligence isn’t about predicting the next token.
It’s about modeling the world, understanding cause and effect, and predicting outcomes.
The LLM era isn’t over.
It’s maturing.

LLMs will dominate interfaces.
World models will decide intelligence.

[Sanjay @sansor]

Future marketplaces won’t be browsed.
They’ll be called.

#Future #Marketplace #Machine #AI

[Sanjay @sansor]

Leaders are like dragons — untamed, they can destroy; guided, they can save kingdoms.

[Sanjay @sansor]

Function + pleasure = worth it
e.g luxury car, expensive house etc

No function + social pressure = waste
e.g gold, diamonds etc

If you're ultra-rich and "can afford" go for it !

Choose wisely. #Minimalism #SmartChoices #ModernLiving

[Sanjay @sansor]

Multi-planetary vision isn’t popular—until it’s necessary. I feel you @elonmusk .. you must be lonely up there..

Practically, it's one of the hardest, loneliest bets humanity has ever dreamed of.

#SpaceX
#Mars
#SpaceExploration
#MultiPlanetary
#ElonMusk

[Andrew Ng @AndrewYNg]

A “10x engineer” — a widely accepted concept in tech — purportedly has 10 times the impact of the average engineer. But we don’t seem to talk about 10x marketers, 10x recruiters, or 10x financial analysts. As more jobs become AI enabled, I think this will change, and there will be a lot more “10x professionals.”

There aren’t already more 10x professionals because, in many roles, the gap between the best and the average worker has a ceiling. No matter how athletic a supermarket checkout clerk is, they’re not likely to scan groceries so fast that customers get out of the store 10x faster. Similarly, even the best doctor is unlikely to make patients heal 10x faster than an average one (but to a sick patient, even a small difference is worth a lot). In many jobs, the laws of physics place a limit on what any human or AI can do (unless we completely reimagine that job).

But for many jobs that primarily involve applying knowledge or processing information, AI will be transformative. In a few roles, I’m starting to see tech-savvy individuals coordinate a suite of technology tools to do things differently and start to have, if not yet 10x impact, then easily 2x impact. I expect this gap to grow.

10x engineers don’t write code 10 times faster. Instead, they make technical architecture decisions that result in dramatically better downstream impact, they spot problems and prioritize tasks more effectively, and instead of rewriting 10,000 lines of code (or labeling 10,000 training examples) they might figure out how to write just 100 lines (or collect 100 examples) to get the job done.

I think 10x marketers, recruiters, and analysts will, similarly, do things differently. For example, perhaps traditional marketers repeatedly write social media posts. 10x marketers might use AI to help write, but the transformation will go deeper than that. If they are deeply sophisticated in how to apply AI — ideally able to write code themselves to test ideas, automate tasks, or analyze data — they might end up running a lot more experiments, get better insights about what customers want, and generate much more precise or personalized messages than a traditional marketer, and thereby end up making 10x impact.

Similarly, 10x recruiters won’t just use generative AI to help write emails to candidates or summarize interviews. (This level of use of prompting-based AI will soon become table stakes for many knowledge roles.) They might coordinate a suite of AI tools to efficiently identify and carry out research on a large set of candidates, enabling them to have dramatically greater impact than the average recruiter. And 10x analysts won’t just use generative AI to edit their reports. They might write code to orchestrate a suite of AI agents to do deep research into the products, markets, and companies, and thereby derive far more valuable conclusions than someone who does research the traditional way.

A 2023 Harvard/BCG study estimated that, provided with GPT-4, consultants could complete 12% more tasks, and completed tasks 25% more quickly. This was just the average, using 2023 technology. The maximum advantage to be gained by using AI in a sophisticated way will be much bigger, and will only grow as technology improves.

Here in Silicon Valley, I see more and more AI-native teams reinvent workflows and do things very differently. In software engineering, we've venerated the best engineers because they can have a really massive impact. This has motivated many generations of engineers to keep learning and working hard, because doing those things increases the odds of doing high-impact work. As AI becomes more helpful in many more job roles, I believe we will open up similar paths to a lot more people becoming a “10x professional.”

[Original text: https://t.co/svQYHp3XVW ]

[Sanjay @sansor]

Most people confuse leadership with survival tactics.
#Leadership

[Sanjay @sansor]

Hey @karrisaarinen , make it cheaper for India—world’s IT hub, crazy populated with devs. Volume’s our ace—play it !

[Martin Fowler @martinfowler]

NEW §

A pre-trained GenAI model lacks recent and specific information about a domain. Bharani Subramaniam and I continue our article on GenAI patterns to explain how Retrieval Augmented Generation (RAG) can fill that gap.

https://t.co/ahfuPdoPuT

[Sanjay @sansor]

Final Takeaway : Ask..
- Do we have the right infra & compute?
- Can we optimize for latency & cost?
- How will we handle retraining & model drift?
- Are we compliant & secure?

If not, AI isn’t a business solution—it’s an expensive experiment.

[Sanjay @sansor]

I success is 70% engineering, 30% model.
Even the best model will fail without:

- Scalable data pipelines
- Optimized compute infrastructure
- MLOps for retraining & monitoring
- Secure, compliant deployment
- Low-latency APIs & integrations

Engineering > Babysitting a Model

[Sanjay @sansor]

The Hard Truth: Open-Source ≠ Easy

AI doesn’t succeed because of better models.

It succeeds because of better AI systems.

The real challenge isn’t choosing a model—it’s building the system around it.

[Sanjay @sansor]

Many believe that downloading an open-source AI model is enough to launch a product. It’s not.

The real challenge isn’t choosing a model—it’s engineering the infrastructure, scalability, and reliability to make AI work in the real world.

[Sanjay @sansor]

Models like DeepSeek, Mistral, and Llama have made AI more accessible than ever. But here’s the hard truth—just because a model is open-source doesn’t mean it’s production-ready.

A thread 🧵 on why AI success is 70% engineering, 30% model tuning.

#AI #Engineering #OpenSourceAI