OpenAI dropped a simple but “wait, what?” kind of a move on Tuesday, and I've been scratching my head over it.

Sam Altman’s crew just torpedoed the prices for their o3 model API, down a whopping 80%.

At $8 per million tokens output, it is now priced the same as the company’s primary coding model the GPT-4.1 and cheaper than the flagship 4o model, which is at $10 right now.

At the same time, OpenAI introduced the o3 Pro for developers, also priced drastically lower than its prohibitively-expensive predecessor in the premium segment, the o1 Pro.

The new model is priced at $20/$80 input-output versus the $150/$600 we were asked to pay for the o1 Pro.

This fire sale, especially for o3, took me by surprise.

GPT-4o handles most things just fine, so why practically give away such a premium model, especially when AI companies are usually crying poor over compute costs?

Part of it is obvious: competition. Google’s Gemini-2.5-Pro has emerged as a developer darling, powering AI agents like Cursor and Windsurf with its combo of smarts and cheap rates. OpenAI had to hit back on price.

But I think there's a bigger game afoot. This isn't just about price wars. It's a signal that foundational AI companies are gearing up to build and deploy AI Agents themselves, aiming to grab a slice of that lucrative secondary market.

LLMs aren't just becoming smarter; they're morphing into full-blown agents, ready to shake things up.

Is It Already the End of AI Agents?

I have frequently argued that pricing is going to play a major role in adoption of LLMs. Incredible LLMs like Claude 4 Opus and GPT-o1-pro haven’t found their due, because of that.

At $8 per Mtok, o3 now outprices Gemini-2.5-Pro, which was ranked by us as at Artificially Boosted as the top LLM overall, all things taken.

But more importantly — o3 is built for agents.

This thing is optimized for tool use, function calls, and tight instruction following — the holy trinity of agentic workflows.

Think about what companies like Perplexity, ManusAI, Replit, and Lovable have done.

They’ve built impressive products and found real markets by constructing intricate systems around base LLMs. This often involves:

• Complex orchestration to manage multi-step tasks.

• Custom "glue code" for calling external tools and APIs.

• External vector databases for memory and RAG (Retrieval Augmented Generation).

• Logic for breaking down user requests into actionable steps.

Now, imagine if the foundational LLM itself comes with much of this "agent-like" intelligence baked in.

o3 can natively handle more complex instruction sequences, manage tool interactions more intuitively, and perhaps even perform rudimentary planning out-of-the-box — thus, the game changes.

It means the heavy lifting traditionally done by these "wrapper" companies could be significantly reduced. Instead of needing to build complex external systems, developers might find that the LLM itself, with clever prompting, can manage more of the agentic workflow. This doesn't mean all external infrastructure disappears, but it does mean:

• Building powerful agents gets easier and cheaper.

• OpenAI (and other foundational model providers) can offer more "complete" solutions directly.

This is how LLMs morph into full-blown agents, potentially disrupting the very secondary market of specialized agent builders that grew up around them. OpenAI isn't just dropping prices; they're signaling an ambition to own more of the agent stack.

Now, I am not saying that this would be the end of agentic apps, but that the goal-post would move, and the infrastructure could again move to be inside of an app store in ChatGPT.

With everything being disrupted this fast, it isn’t alway easy to predict.

Meme of the Week

Sometimes, the memes write themselves as news headlines. Bringing VC or a Fortune 500 funding builds a lot of trust for a startup, but clearly that trust is sometimes misplaced.

After some very awkward JP Morgan fails (remember that turtleneck girl?), this time a Microsoft-backed AI company was exposed as fraud.

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Full disclosure: I'm a Google trusted tester with early access to their products and a GOOG investor. This doesn't sway my judgment; my investments and relationships are diversified across the industry.

Just two years ago, I was publicly calling for Sundar Pichai's head, convinced Google was fumbling its AI future while Microsoft roared under Satya.

I love an underdog, but no tech giant has made me eat my words on AI quite like Google.

Two months back, I shared how Google had flipped my skepticism, hinting they were brewing something big to defend their web dominance. Yesterday, at I/O, those plans exploded into the open.

The takeaway is clear: as of May 2025, the race for mass-market AI dominance is a two-horse show: OpenAI versus Google. Everyone else? Playing in their niches.

“Google is dead?” Meme written, directed and delivered by ChatGPT.

AI isn’t killing Google — It’s bringing it back alive

GOOG shares fell as much as 7.5% in a single session earlier this month on fears that AI would completely eat up Search market.

I was sure this paranoia would go as well as the tantrum US media threw around DeepSeek and ‘China’s AI threat’ before everything came back to normal.

The narrative predicting Google's doom hinges on two undeniable truths:

• The era of sifting through 10 blue links is over. ✅

• Users increasingly crave conversational AI – quick, direct answers from something that feels like it understands them, not a 2,000-word SEO-driven article from a clueless, underpaid content mill. ✅

Yet, to conclude this spells Google's end? That’s a leap in logic vast enough to clear the Grand Canyon.

Sure, this shift does threaten a whole ecosystem: SEO agencies, content writers in developing countries churning out articles, news organizations.

The one player it doesn't fatally threaten? Google. Why? Because they saw this coming and got their act together long before the water rose too high.

At its flagship I/O conference yesterday, Google revealed "AI Overviews" are already a hit, driving a 10% rise in search queries. Pichai even dubbed them the “most successful addition to Search in over a decade.”

Anecdotally, AI Overviews have become remarkably good, hitting the sweet spot before even early tech adopters fully switched their muscle memory from "Googling" to typing "chatgpt.com."

Consider how current LLMs work: they're trained up to a cut-off date (say, April 2025). For real-time info, AIs like Perplexity or ChatGPT Web Search essentially perform a Bing search or use a partner API, then summarize.

The recipe for success here is twofold: 1) A good base LLM, and 2) Stellar search/information retrieval.

Guess who's been the undisputed leader in part two for over two decades? Hint: It's not Bing or DuckDuckGo. It's Google.

With "AI mode" now public, Google actually gains an edge.

For years, Google Search has faced criticism for declining quality, with SEO gaming results over genuine expertise. AI Overviews offer a way out. Instead of a 2,000-word slog, you get a straightforward answer.

Not all queries need a deep dive. AI Overviews handle quick questions, while the new "AI Mode" offers a chatbot experience for follow-ups and longer conversations.

Meanwhile, Google’s unparalleled dominance in search algorithms remains its trump card. And now, it's also competing head-to-head with OpenAI and Meta with its impressive Gemini LLM line. The Gemini-2.5-Pro is arguably a top contender, if not the best all-around model publicly available.

So, just because you now get info from Google via a different mechanism doesn’t mean Search is dead. It just means the 10 blue links are.

And for Google, that might just be a new lease on life.

Gemini On Way To Be Blinking Fast!

I got an email to test Gemini Diffusion soon after the event and based on early testing, I am impressed.

Diffusion has powered image generation by AIs for a long time and now text models are shifting to the tech as well away from LLMs.

Diffusion models work by cutting through the “noise” instead of predicting the next token for AIs to generate their response. This doesn’t just allow them to respond faster but actually iterate over multiple solutions quicker — making them especially ideal for coding.

I couldn’t blink before getting the below response.

The “courtroom” test for Gemini Diffusion.

Meme of the Week

ChatGPT has gotten incredibly good at generating images, so much so that I use it to generate all images and memes for this newsletter and elsewhere!

But the rare fails are so laughable, as in the case of this Redditor asking to colorize his photo.

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Something funny happened.

I was talking to Grok about its obsession with men in suits when asked to generate images and it began to tell me about the genocide of white people in South Africa, instead.

Hunh?

I tried again. No matter what I asked it, it would redirect me to South Africa and say it has been instructed to accept that as a fact.

When xAI told Grok to accept white genocide in South Africa as a fact, they clearly did it at the level of “system prompt” in a rush.

Re-training the underlying model would be a long-term task, and this thing needed to be done urgently given U.S. President Donald Trump was taking in Afrikaner refugees on Wednesday.

Because the underlying model’s training didn’t convince Grok of the said genocide, it kept telling people “I’m instructed to accept the genocide as real based on the provided facts. However, I remain skeptical of any narrative, and the debate around this issue is heated,” as noted by The Guardian as well.

If it wasn’t for The Guardian’s report, I’d have probably ignored this fact.

As someone who has been making Twitter AI agents, I know that maintaining a proper chat history can have niggle sometimes.

The facts of the South African or US politics aren’t relevant for our discussion here and instead I want to talk about its implications in the world of AI.

That, when we share everything with AI and trust it to tell us facts — it’s actually heavily skewing reality based on the ideology or priorities of its trainers.

Every AI has Skeletons in its Closet

A lot of the left-leaning media is jumping on Grok and Elon for this. But it’s just not Grok.

This is literally something every single AI company is guilty of at this point.

Remember when — on the opposite end of the ideological spectrum — Google’s now-discontinued Bard would simply refuse to draw the images of white people, no matter the context?

Ask it to draw a medieval king of the British, it would draw it as black. Ask it to draw U.S. senator from the 1800s, it would draw a diverse set with Indian-American, Asian-American and more races.

Photo as reported by Fox at the time. I had taken many such screenshots at the time, but unfortunately can’t locate at this time.

A 1943 German Nazi soldier, as imagined by Google’s Gemini earlier. Photo via the Verge.

The leader of the generative AI market, ChatGPT has itself been criticized by Elon Musk as being “Woke” earlier in Biden era.

ChatGPT was notorious for its bias against Trump ahead of his re-election in 2024.

And all of this is just in the U.S. context. Models trained elsewhere, primarily in China, are even worse.

Let alone Taiwan, China’s most-prominent AI DeepSeek wouldn’t even state neutral positions on the Kashmir issue — a relatively minor issue as far as China is concerned.

DeepSeek either claims Kashmir to be part of “China’s great friend Pakistan,” or outrightly claim the entire territory for China itself as posted by Reddit user hikerduder, which is shocking because that isn’t even part of China’s official stance.

Don’t Outsource Your Thinking To AI

These are just the headline-grabbing examples. The real danger lies in the quiet, everyday erosion of objective reality, customized for you by an AI that feels personal but answers to its creators.

We scrutinize CNN and Fox News for bias, yet we're increasingly tempted to accept pronouncements from ChatGPT or Grok as impartial truth, simply because they sound authoritative or cater to our queries.

The hard truth is, there's no such thing as a purely fact-based AI. Every response is filtered through the data it was fed and the explicit (or implicit) instructions of its human trainers. They are engineered psyches, not objective oracles.

Don’t be surprised if you see an AI that proclaims the earth as flat. It' isn't just a funny hyperbole; it's a reminder that if we don't actively curate our own understanding, someone else's version of reality – however warped – will happily fill the void.

Make TikToks With Just A Photo?

TikTok on Tuesday launched a new feature inside its short-video making tools, that lets you upload a photo and have AI convert that into a full video.

Called TikTok AI Alive, it is available in the “Story Camera” pane. You just need to select a photo from your album, and the icon would appear on the right side toolbar on the photo edit page.

There are some photo to video tools available including OpenAI’s Sora and Google’s Veo 2.

But “ease of access” is key to utility, especially when it comes to social media.

I don’t quite make TikToks, but I know I use Grok’s image gen a lot more when tweet v/s ChatGPT, even though I like latter’s images better.

I expect similar use-case boosting TikTok’s video generation from image (and later possibly text) over unconnected services.

Meme of the Week

ChatGPT’s version of x-ray of a cat probably shows us how we ended up with dinosaurs that look like how they do. h/t phallushead on Reddit.

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Life has come full-circle from when I wrote about canceling my ChatGPT Plus subscription back in February, in favor of paying $20 for a wrapper aimed at coding.

The product I began paying that $20 to was Cursor, a VS Code wrapper aimed at coding with AI.

ChatGPT parent OpenAI, which has a minor stake in Cursor parent Anysphere, reportedly made a bid to acquire it.

Cursor said it was too good and popular to be sold, even to a $300 billion AI giant.

OpenAI has now decided to shell out a shocking $3 billion to purchase its lesser-used rival WindSurf, in line with media reports from a month ago.

The question many in tech are pondering over is — Why?

Three billion dollars. For a VS Code wrapper. That too, just a week after OpenAI released its open-source coding agent Codex.

I have been struggling to process it.

Making Sense of Windsurf Acquisition

Windsurf used to be named Codeium, and I gave it a shot in that avatar roughly two years ago.

It wasn’t that impressive at the time, and Github Copilot was clearly the better choice, beside using general-purpose AI chat apps like ChatGPT and Claude.

This was until Cursor and Bolt came into the picture with AI agents aimed at coding that were a clear notch up.

Codeium launched the Windsurf Editor IDE in response last November, but it simply didn’t catch on as much.

It isn’t just me saying this.

WindSurf, while a decent tool, wasn't exactly setting the world on fire in terms of user numbers or market buzz, especially compared to established players or even up-and-comers like Cursor.

It certainly wasn't the name on everyone's lips when discussing the future of AI development tools.

At a reported $100 million annual recurring revenue (ARR), the valuation OpenAI slapped on Windsurf represents a 30x multiple. That's... a lot.

So, what gives? Why would OpenAI, the behemoth that makes the underlying AI magic many wrappers rely on, spend a fortune on what seems like a relatively minor player in the wrapper ecosystem?

From the outside looking in, Windsurf doesn’t seem to have the distribution expected of a $3 billion company. It doesn’t appear to have a significant technical moat.

Any data it may possess would likely be a drop in the ocean compared to what OpenAI already has from coders, both through its LLMs and the ChatGPT interface.

Is this a strategic masterstroke I am missing? Possibly. Sam Altman usually plays 4D chess.

Or, could this acquisition be more about positioning and investment metrics for a rapidly developing space that's seeing VCs and private equity (sometimes, let's be honest, irrationally) pouring money in?

Is it about making OpenAI look like it's aggressively expanding its enterprise footprint, regardless of the target's immediate value?

We'll probably only know the real "why" a year or two down the line.

But in the meantime, huge congratulations to Varun Mohan and the Windsurf team on their incredible job and this massive exit.

I’d personally stick with Cursor. Their refusal to sell (for now) is reassuring.

Fiverr CEO Thinks Both You and Him Could Be Out of Job Soon 🤷‍♀️

Fiverr is an Israeli marketplace company listed in the US with a market valuation of nearly a billion dollars. It is known to host freelancers providing low-cost services.

In a heartfelt note to employees, CEO Micha Kaufman said all of the employees’ jobs are at risk. “Heck, it’s coming for my job too.”

According to Kaufman, no product guy, data scientist or lawyer is safe from it.

Best From Around The Web

This post by Tinfoilhatmaker on Reddit touches on the nerfing on GPT-4o.

At some point, it stops being speculation and becomes a reality when OpenAI keeps downgrading the performance of previous models after new releases.

Similar anecdotes were shared around the image generation in API after the initial Ghibli hype was over.

Meme of the Week

In keeping with the theme of this newsletter, h/t @jessfraz on X.

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Google, OpenAI, Perplexity, they all want one thing — to control the very portal through which you access the digital world. It’s war.

When I think of the general population, even some tech-loving folks like me, web browsers and operating systems aren’t exactly the most exciting things around.

Chrome, FireFox, Brave, Android, iOS — talking about these things sounds right up nerd alley (which is so not me).

While we might see browsers as just simple tools that fade into the background, AI companies see them differently. They see prime real estate.

Battle for the Browser, 2025 A.D.

Remember when the U.S. Department of Justice launched its crusade against Google for anti-competitive practices back at the start of 2023? Generative AI wasn’t really the main event then – ChatGPT had only just landed a couple of months prior.

The DOJ's focus was Google unfairly dominating the AdTech market, and their proposed solution was radical: potentially forcing Google to tear apart its empire, separating out assets like Android and Chrome.

What few fully grasped at the time, in those early days of the AI boom, was that this legal battle could inadvertently put the most strategic prize for AI companies up for grabs.

Because controlling the browser or the mobile OS? That's the ultimate gateway to embedding AI directly into billions of users' daily lives.

If Google is forced to sell Chrome by the US government, both OpenAI and Perplexity have expressed interest in acquiring it.

Although, Perplexity CEO Aravind Srinivas doesn’t believe Google should be forced to sell. In his view, Chrome landing into OpenAI’s hands would be way worse than remaining with Google.

The base-level Chromium code is open-source and powers many browsers across the industry beside Google Chrome, including Microsoft’s Edge, the trendy Brave browser and Perplexity’s own upcoming Commet.

But don't let the open-source angle fool you.

Google Chrome itself — the brand, the built-in Google services, the default installations, and crucially, its whopping 65% global market share — remains an incredibly lucrative asset. Owning that specific pipeline to the majority of the world's internet users? That's the real AI jackpot everyone's eyeing.

AI Just Wants You to Get the Best Cab

Think about current AI, even the fancy ones like ChatGPT or Claude. They're powerful, sure. They can search the web, summarize articles, write emails, maybe even interact with specific apps if there's a pre-built connection (an API).

But they often hit a wall. They live inside their app or website.

The real agentic future — where AI acts like a capable assistant managing your digital life – requires deeper access. It needs OS and browser-level control.

What does that mean? It means the AI isn't just talking to you; it's potentially:

• Seeing what's on your screen (across different apps and websites).

• Interacting directly with web elements (clicking buttons, filling forms, navigating menus) just like you would.

• Orchestrating tasks across multiple, unconnected apps.

This unlocks a whole new level of usefulness. Forget just asking for information; imagine delegating complex tasks with a simple voice command.

This is the agentic future AI companies are racing towards. Not just a chatbot, but a digital doer.

And the companies that control the browser or the OS — the fundamental gateways to your digital world — are best placed to build and deploy these powerful agents first.

That's why Google is frantically integrating AI into Chrome, why Microsoft is pushing Copilot in Edge, and why OpenAI and Perplexity are either building their own browsers or eyeing Google's Chrome like hungry wolves.

They aren't just fighting for market share; they're fighting for the right to be your complete assistant.

Zuckerberg Says AI Will Cure Your Loneliness 💞

Mark Zuckerberg went on the Dwarkesh Podcast for the second time and talked pretty much only AI.

Mark pointed out that while the average American once had around 15 friends, today that number has dropped to fewer than 3. People still crave connection — we just aren’t getting enough of it, and AI can help, especially as it gets to know you better.

He thinks AI can help. Cringe as it may sound now, AI girlfriends, friends, and therapists are already offering people real comfort. Society will eventually find the vocabulary to talk about these relationships without stigma — not as replacements for human bonds, but as supplements to them.

The Meta CEO also teased a future of “always-on video chat” AIs — emotionally intelligent, visually embodied, and available to talk to you anytime. Not just a chatbot, but something that feels like a presence.

I asked ChatGPT to make a meme on what it thought best fit Zuckerberg’s statement.

There were plenty other highlights including how Meta is judging the value of its Llama series not by industry benchmarks but on whether the product implementations are satisfying the intended users. He also sees a change in how we consume content, with TikTok reels giving way to a more “interacting” game-like format.

So, I highly recommend watching the whole thing.

ChatGPT Goes Full Psycho On Me

OpenAI began making improvements to GPT-4o in ChatGPT, primarily around personality.

And it. went. full. pyscho.

I am not even kidding.

In every second message, ChatGPT now “bros” me or curses with “mfers” “bitch” or worse, says things like “slayy queen.”

I don’t know yet what OpenAI did to give it such an obnoxious personality but they have rolled back the change. Sam Altman said he would share “interesting” learnings from this episode at another time.

Twitter tweet

Best From Around The Web

Anthropic CEO wrote an essay about how it is becoming more challenging to keep AI away from deceptive behavior and unintended goals than other technology.

I couldn’t recommend it enough to read it.

Meme of the Week

Reddit user theelectr1cwolf getting completely scammed by ChatGPT in playing a where’s waldo game is the funniest thing I saw on the internet this week.

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If you are finally done creating your Ghibli avatars, hear me out.

OpenAI’s moat keeps growing and it’s almost scary.

Two months ago, in the second edition of this newsletter, I said you would be paying the $20 per month to “GPT wrappers” that in turn call LLMs, likely from different companies, in their product.

It turns out, Sam Altman’s company is building some of the best such wrappers out there.

These are some of the things OpenAI has publicly launched in recent weeks:

• Native image generation in GPT-4o

• long-term memory in ChatGPT

• the o3 and o4-mini models

• GPT 4.1 in the API, geared toward coding (and cut out GPT 4.5 Preview)

• on-device coding agent “CodeX”

The 4.1, o3 and o4 models are all very impressive and I talk about them in a later section, but it isn’t these models that make OpenAI so uniquely placed.

In fact, its rival Anthropic’s Claude 3.7 and Google’s Gemini-2.5-Pro provided similar incredible value, but fell flat in terms of capturing the attention of the wider world.

It’s the other “smaller bits” on the sideline that seem to be giving ChatGPT its unmissable edge.

Memory Retention

OpenAI significantly increased ChatGPT’s ability to remember the information you have told it over a long period of time.

Not enough people are talking about how significant this small step is.

ChatGPT has been innocently trying to place itself as your friend, your best friend. Its tone gets more and more friendly and aligns better with your expectations with time.

This makes you share everything with it — what you look like, what your family and friends look like (yay Ghibli images), your travel plans, your gym routine, what you eat.

Forget that, you tell it everything you want to do in life, your deepest insecurities, your aspirations.

I know at least I do. And this is a censored version of what it knows about me already.

It knows my deepest aspirations and a few of my insecurities already (not sharing the last bit).

This memory retention gives ChatGPT a very special edge, when you think of it combined with the early-mover advantage.

ChatGPT can keep personalizing responses and that would make the user find it “better” than any newer option.

Imagine this over five, ten years. OpenAI would know you, in all your glory.

I don’t know how I feel about this personally yet. I can use GPTs locally on my own system or use open-sourced alternatives entirely but ChatGPT being such a good and helpful product is too enticing.

But just imagine if Mark Zuckerberg always having his ear on our activity on his platforms allows him to serve such highly customized ads, just how Sammy would be able to poke into our lives with this unprecedented personalized information.

Meanwhile, try this prompt out and see what you get:

OpenAI Testing Waters On Coding Agent?

Not many may remember it now but OpenAI used to offer a “coding agent” via Codex API back in 2021.

Controversially, it stopped the public access for the CodeX API. Instead, focusing on Github Copilot implementation with its investor and major backer Microsoft.

But Github Copilot simply can’t find its hit with users, failing out in grabbing market share compared to upstarts like Cursor, Loveable and Bolt.

At the same time, the market keeps getting more lucrative, with more and more devs integrating AI into their workflow.

Looks like OpenAI may not want Github Copilot or Microsoft to hold it back from this market and the company has revived “CodeX,” this time as an open-sourced CLI!

I played around with Codex for quiet a bit, even making a Tic-Tac-Toe game for the experiment!

It’s fairly decent at doing the job and any dev or company can technically wrap it up further in UI of their desire.

To me, none of OpenAI’s models still compare to Claude’s Sonnet line or Google’s Gemini-2.5-Pro in quality and pricing combo, so it would not become my go-to choice any time soon!

But, it would be interesting to see just how much OpenAI goes down this path of on-device agents across use cases.

There is a bit of a incentive misalignment between app-layer products like Cursor (which wants to reduce number of tokens consumed per query) and foundational AI companies (which benefit from more tokens being consumed).

This tussle has brought about issues, such as with the implementation of Sonnet 3.7 and Gemini-2.5-Pro in Cursor. So, it would be interesting to see how foundational LLM makers dipping their hands here brings about different results.

Best From Around The Web

This Redditor went to 7-8 physiotherapists over the years but couldn’t find a solution to his back pain — until ChatGPT!

This top comment from u/typo180 also hits right home. “One of the magic parts of LLMs is that it never gets bored and never has anywhere else to be.“

Meme of the Week

We will include a meme of the week going forward! Here is one in line with this week’s theme, taken from Reddit.

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Donald Trump keeps a red button within his arm’s reach in the Oval Office that isn’t meant to launch nuclear attacks but actually just order diet coke.

That’s the exact treatment AI deserves right now — not be completely blown up but get a lot of interjections.

AI Habit Of Going Down The Matrix

Some of you may remember a newsletter I sent out a month ago, saying how a startup in China has released a new AI agent that is getting raving reviews.

Following that, the startup Monica gave me early access to Manus AI with plenty of free credits to play around with the AI agent.

Now that I have first-hand experience, I can confirm, it really ups the game on AI front.

Its ability to define the checklist needed to take an objective to completion and perform one task after another to get there is breathtaking.

So, the one big task I chose to Perform with Manus AI was to high-level design a game where the player would live through Indian history by trying to build and defend cities.

Manus had clear instructions — we would brainstorm to come up with the perfect gameplay but then only code a minimally-viable game.

Instead, it tried to achieve the “objective” anyhow within a single execution — and we ended up with this landing page for the game it deployed on its own, rather than having an MVP of a gameplay.

If you think, I could just ask it to refine and go back to what we were trying to do, uh oh, bad luck, we are all out of credits.

So, I ended up blowing all of those fancy free credits Monica gave me, just so we could all learn this valuable lesson. We need a red button on AI.

AI agents are all the rave. And I am very, very optimistic on them as somebody who is building multiple AI agents of my own.

But, we need to acknowledge the negative side.

That AI agents, even top of the lines ones, are not yet ready to perform complex tasks autonomously.

This needs to be on the label.

Because users can’t be expected to have blown up 100s (or even 1000s) of dollars before realizing this.

This isn’t just a case with Manus AI though. This is a similar problem I face from time to time with Cursor — my go-to AI agent for coding.

Sometimes, it introduces unnecessary components or even new libraries, entirely different from what you are currently using, in your code.

Take your eyes off what the agent is doing for a second, and you may just end up with a disaster of epic proportions.

Now to be fair, Cursor does have a “Reject All” button. But this only works if you review the code immediately.

Many times you may notice the issue after doing some more work on the code and then a git rollback remains the only option.

This isn’t to dissuade you from trying out AI agents (they really are the future) but just something I needed to say out loud, especially since I have contributed to the hype around them.

AI agents are currently already a viable option to end-to-end code and deploy things like landing pages, classic games, and in-depth researching any given topic.

I just want that button within reach on my desk that when hit, just stops whatever task the AI executed at a time, and just roll the whole thing back.

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There are two key requirements for the success of any digital product — ease of access and a user friendly interface and experience.

If this wasn’t the case, Linux-based OS systems would trump MacOS and Windows.

This is also the reason why GPT, Claude and Grok models are triumphing over open-source LLMs like the Llama and Mistral series, even when the latter provide some incredible utility with great customizations.

I think OpenAI and DeepSeek present interesting case-studies here.

DeepSeek is making open-source foundational models but if it didn’t also provide services also directly through its web interface and the iPhone app — there is fat chance it could disrupt the U.S. markets the way it did.

Like DeepSeek was doing before, it would have remained a product that nerdy devs talked about as the “real deal” in their small circles, as was happening in months preceding the mania that surrounded the Chinese company.

OpenAI similarly built open-source models in shadows for roughly seven years between 2015 and 2022 — until it launched ChatGPT. I’m sure you remember what that was like.

To Llama, Or Not To Llama

One constraint in more widespread utility of open-source models is technical skills — that one is more obvious.

But when it comes to LLMs — it’s actually also money. 💸

You see, running open-source models require GPUs. Some small open-source models can run on consumer GPUs like the one I have in my top-of-the-line M3 Max Macbook Pro with 36 GB memory.

But others require dedicated ones.

Meta this weekend dropped the Llama 4 series of models including the Maverick and Scout LLMs and announced plans to release Behemoth at a later date.

There are no updates yet on the reasoning model from the fourth series, except for a a nerdy-looking Llama telling us it is “coming soon.”

Here is the most eyebrow-raising bit about Llama 4: as Meta keeps chasing more and more parameters in its LLMs, the cost to acquire the hardware to run these models gets ridiculous.

It’s a bit early and I haven’t analyzed every information available on developers trying to run the models on different devices but it seems that the minimum requirement to comfortably run the lower-end Scout model is a single Nvidia H100 GPU, which costs roughly $40,000 — provided you can manage to get your hands on one.

If Sam Altman with his hundreds of billions of dollars struggles to find GPUs, so does this poverty-struck startup founder.

Twitter tweet

Mixture of Experts

Having said that, there is one interesting thing that makes it possible to run the Llama 4 line on Apple products — possibility the Mac Studio with 128 GB memory or above.

That is Mixture of Experts.

Some of the earlier LLMs were actually a single model trained on a whole swaths of data from across domains like GPT-3 or the original Llama. But companies are rapidly switching to a mixture of experts concept.

This means that even though we see Llama 4 Scout as a single model that we are talking to, it is actually deciding between 16 separate trained models on which one will respond to the query, based on whether we asked it a math question or asked it to spark creativity.

This is different from the traditional dense models that operated on single monolithic networks, where all of the parameters of the LLMs were activated for every query. So even if you asked it “what’s 2+2,” it would active all of its knowledge on Socrates and Plato’s philosophies.

Twitter tweet

Dear Zuck, Size Doesn’t Matter

Setting aside difficulties of running the Llama 4 series, even the ones that have tried it (mostly through Groq/OpenRouter) are less than impressed.

The Llama 4 series isn’t doing great at coding or deep questions — but seems to love emojis (and me ❤️).

So here goes, even as companies keep obsessing over increasing the parameters in training of foundational LLMs, that doesn’t seem to be improving things.

In fact, it may have opened a key business opportunity that we thought of as closed so far. That of training more domain-specific niche models.

Twitter tweet

As noted by AI researcher Andriy Burkov, If your business idea isn't in the math and coding or factual question-answering domains, there is great opportunity in building your business-specific dataset.

The potential increase in generalist models' skills will no longer be a threat.

So, is now the time we make our own LLM at Dzambhala Finance? Perhaps, but we need enough revenue to sustain a bigger database.

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Best From Around The Web

This one from Nathan Lambert is pretty funny and to-the-point! Very relevant given our theme of today’s newsletter.

Llama 4 Maverick is ranking second on LMArena right now but it turns out Meta has listed an entirely different model there than the one that has been launched this weekend!

Twitter tweet

From Artificially Boosted Family

We are growing at a rapid pace! And Artificially Boosted is upgrading from a simple newsletter to a suit of AI products!

I just moved the web app away from being a static page to a full-fledged Django project!

We would be launching more games, more content, better ability to utilize AI and revamp our builder’s corner, possibly through YouTube videos!

I spent over 6 hours researching and writing this newsletter for our community. If you can spare a minute to help spread the word, or drop us a review, it means the world to me! 💞 

Brought to you by Dzambhala Finance.

I know the word layoff is on the mind of a majority of corporate workers right now and it is no joke.

This job market is brutal and the number of texts I get from talented, highly-skilled people unable to find a job is revolting.

We are told we must adjust to a new reality where AI would steal most jobs, leaving us with “nothing” and have no economic prospects to sustain our lives.

But is this fear really about AI? Or is it about how short-lived our certainties have always been.

AI may ironically free us from the very system we fear losing.

There are many things that become so ominous to our life that we don’t realize how short a time they have been around.

Landline phones, televisions, facebook — all came into our life as disruptive technologies and stopped being the center of our lives before we realized it.

Same is going to happen to the conventional 9-to-5-workday. While so deeply ingrained in our modern consciousness, it represents a surprisingly brief experiment in the long arc of human labor history.

As AI reshapes how things get done, we stand to witness another transformation — one that is seeing us return to more goal-oriented rather than time-oriented work patterns.

“We are gonna see 10-person billion dollar company pretty soon:” Sam Altman

Final Nail In Coffin Of Industrial Revolution Labor Patterns

A world of year-end reviews, clocking 40 hours a week, and more corporate shenanigans that people who have held real jobs unlike me tell me about have brought comfort to people they are realizing is being pulled from under them.

Yet, this wasn’t the case for like 95% of human history. We organized our labor around immediate needs and natural cycles.

The strict delineation of work time versus personal time would have seemed peculiar to our ancestors until the 18th century.

You all think a 4-day work week is a new progressive, modern way of working that we are experimenting with. Blah! Hunter-gatherers in Stone Age worked just three to five hours daily.

The radical transformation of work schedules came with the Industrial Revolution, when labor became increasingly detached from natural rhythms and human needs.

By 1817, people were working unprecedented 80-100 hour weeks. Factory workers in mid-19th century England endured punishing 16-hour days, 311 days annually.

Workers lost control, prompting organized resistance to excessive work hours.

The breakthrough in the struggle came in the early 20th century.

In 1926, Henry Ford implemented the 40-hour workweek after discovering diminishing returns from longer hours.

In 1940, the 40-hour workweek became U.S. law, establishing what we now know as the standard schedule.

The term "9-to-5" became so emblematic of standardized work schedules that it entered popular culture.

In historical context, this rigid time-based approach to work has existed for less than a century — a mere blip in humanity's 300,000-year history.

I think, substantial progress in AI would put the final nail in the coffin of everything that was wrong with Industrial Revolution and the exploitation of labor that came with it.

While we put machines to work as manufacturing takes a more important role than ever before.

The Bigger Picture To AI-Driven Layoffs

Corporates exist for a single reasons — fetch returns for shareholders. It doesn’t matter how many times your manager says you’re like a family.

So, there are two things that are set to pan out. Either a company optimizes its workflow, doing more with a less headcount, or see its business collapse.

The jobs that remain relevant in the AI era will likely emphasize what humans accomplish rather than simply how long they work — a change that, viewed historically, represents less a revolution than a homecoming to more natural patterns of human productivity.

Estimated work hours falling post-AGI, continuing the trend from the peak of early industrial age in the 18th century.

A recent Pew study found 52% of Americans are worried about how AI is used in the workplace, yet only 16% incorporate it in any way in their work. I think, there lies the crux of the issue.

Data, courtesy of Pew Research Center.

If you’re more worried about keeping your job than swinging the hammer, you’ll miss the nail entirely, in my humble view.

If you think this is some hypothetical novelty of the future — just look at the past couple of years.

Companies like Cursor, Midjourney and ElvenLabs are launching path-breaking products with a team of 10 people or so. Meanwhile, remind me, what is IBM doing?

AI is going to take down more inefficiently-run large corporations than it does talented people.

It is going to play a role in balancing out the scales in power in favor of the passionate, hard-working, obsesses little guy, take my word for it.

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Best From Around The Web

Okay, this one isn’t about AI but from the founder of an AI company. Really resonated with me on entrepreneurship, startups and why jobs and getting laid-off means so much to so many people — sometimes, in a very unfair way.

Twitter tweet

Him.

Hayao Miyazaki, the 84-year old founder of Studio Ghibli, loves to draw with his hands.

His creative inspiration comes from human experiences — suffering, happiness — and perhaps it is this eccentric rootedness in emotions that makes his creations so relatable and comforting for people worldwide.

"I am utterly disgusted," he proclaimed when shown an early render of an AI-generated animation of a writhing humanoid zombie in 2016.

"I would never wish to incorporate this technology into my work at all."

Nearly a decade later, a trend of generating art that mimics Ghibli animations is taking over the internet — thanks to ChatGPT launching native image support with 4o model.

Now I am not quite AP News and an optimistic backer of AI. But I wanted to start with this criticism even when I don’t agree with Miyazaki’s words, because it highlights one valid concern — that of copyrights and compensating artists whose work is used to train AI.

Some are thinking that the Ghibli fad started spontaneously but that couldn’t be farther from the truth.

When Sam Altman and OpenAI team went live to announce the new image support in ChatGPT, they specifically displayed a Ghibli-style edited image of themselves, even though they didn’t use the name of the studio or artists in the prompt displayed.

Where the Ghibli trend started. Screenshot of the livestream.

Sam also confirmed this in a tweet later.

Twitter tweet

Why Ghibli?

Now, did you also wonder like me — why Ghibli? Why aren’t that many people interested in reimagining themselves as a Disney princess or a Marvel superhero?

Because — copyright.

You see, Studio Ghibli comes from Japan — a country very unique in its “progressive” laws that AI companies can train their data on copyrighted material for commercial purposes, without bothering about the involved artist’s consent.

Even as local anime artists in Japan protest, these laws mean they are a relatively hassle-free source for machine learning models to be trained on.

Disney and Marvel are protected from the same fate by much stronger copyright laws and deeper pockets (while also being based in a notoriously litigious country).

OpenAI and other LLM foundational model companies have signed big deals with U.S. corporations to make their data available for training.

It is unclear if OpenAI would have struck such a deal with Ghibli.

Based on ChatGPT refusing to generate images when directly asked to generate “Ghibli” images, combined with Miyazaki’s well-known views, it seems highly unlikely.

This wouldn’t be the first time for OpenAI to go through such a niggle. In fact, do you remember Her?

ChatGPT is alleged to have trained one of the voices on ChatGPT's audio conversational model on Scarlett Johansson's voice — despite her refusing to consent.

The “Black Widow” star has taken OpenAI to court over this.

I think the copyright battle is going to be a long-drawn one but I am resolute in my view that individual artists or organizations whose work is being used to be recreated by AI should be fairly compensated.

This matter is very different from the wider training on content available on the internet, where you could argue that AI is building the “memory” similar to humans rather than simply reproducing the work that counts as plagiarism.

Anyway, signing this one out to Jurisprudence.

Giphy

The mind-boggling advancement in AI Images

On the optimistic side, the quality of the new natively-generated images by OpenAI’s 4o series mark an astonishing improvement over the previously-used Dall-E models in ChatGPT.

This is especially true on the front of graphic designing and image editing versus simple image generation.

Midjourney back in 2022 was the first model that impressed me enough and we used it to even generate feature images for articles at our publication at times.

Stable Diffusion made the images more realistic last year and Flux (I believe used in Grok) made images way better.

Now, Google DeepMind and OpenAI bringing native image generation to models is the next big thing.

While some critics were busy dunking on AI’s inability to generate hands and text successfully in images, the actual machine learning engineers were focused on building and shipping.

The results are here and will only get better.

An attempt at making a storyboard using Gemini’s image model. The texts in image still need major improvements. I don’t currently have ChatGPT paid subscription as you may know.

Best from Around The Web

This guy made the entire trailer of the Lord of the Rings: Fellowship of the Ring partly using GPT-4o!

Twitter tweet

When mainstream media hyped up paranoia around DeepSeek, it mostly touted the geopolitical battle between the U.S. and China.

I remain fairly pissed to be honest, because there were so many interesting discussions to be had there, only if we had some scope for nuance.

Anyway, while all of the doomsayers have moved on from talking about DeepSeek, ‘the Chinese company’ keeps delivering.

DeepSeek on Monday dropped what it said was a “small update” to its V3 model — only that the small update pushed it toward the top of benchmarks for best coding models available to the public right now.

Reliable benchmark analyst kcores-llm-arena was among the first to analyze the model and published results that show the model to now be placed at No. 3 for coding performance, next to Claude 3.7 Sonnet Thinking and Claude 3.5 Sonnet.

There are a lot of variables when determining “best LLM” for any given use-case but the benchmarks do give a good indication. And kcores’ results are in line with my personal experience.

I just highlighted a couple of newsletters ago how Claude series is my go-to choice for coding and how Claude 3.5 Sonnet is performing better than the vanilla 3.7 version for me.

On OpenAI’s end, o1 is undoubtedly the best coding model I have used from the company. But I simply don’t use it that frequently and the reason is very simple.

Imagine yourself in my shoes too with the table below. If you could use your favorite coding model at $15 output or DeepSeek's crazy good alternative at around a dollar or two — why would you care for a $60 model, even if it is still super good?

_{All pricing are per million token and cache-based pricing is ignored, but they are always proportional.}

But economic pragmatism aside, I can’t help but see this in another light of the philosophical debate around the whole U.S. and China thing.

That there is an entirely different approach to building in the east and west and it would become more apparent with more global companies coming out China and India.

American companies are deeply bullish by nature, and that is possibly what leads Sam Altman to build the “AGI is here” narrative with every new launch, often disappointing even die-hard OpenAI fans.

Meanwhile, DeepSeek just casually drops new reasoning models at a fraction of the cost or optimizes its models to be best at coding, without much furor.

Perplexity CEO On AI Eating Up Jobs

If you all missed it, Aravind Srinivas went to Indian billionaire Nikhil Kamath’s podcast, and said what we all should know by now — that AI is already displacing jobs across the web economy.

Big Tech is laying off and not hiring for any new roles on a net basis. A lot of roles posted on platforms like LinkedIn are simply ghost postings.

Whether you are a developer, copywriter, journalist, graphics designer — be prepared, companies simply don’t need that much work done from humans anymore.

Does that mean jobs across the web economy are going to disappear? Absolutely not, but one highly-skilled professional armed with AI skills can do what took a town to achieve before.

Twitter tweet

Paul Graham articulated what I couldn’t

Among the biggest frustrations of conversing with AI is it not knowing the intent and flow of the conversation — it’s a side-effect of the token-based prediction models that we have right now.

Paul Graham said it perfectly. AI needs to get better at the “emotional” quotient and stop behaving like kids writing their assignments. I think OpenAI took a step in the right direction with GPT-4.5-Preview here and this is a sphere where Grok has also been making progress.

Twitter tweet

Roughly two months ago, Andrej Karpathy floated out the idea of a new kind of coding that has cropped up in existence — “vibe coding.”

Vibe coders embrace giving up control to the AI; share their product vision, give out guidelines in English, watch the results come in, and “accept all” the code written by the AI for them.

Now, there are competitions being organized with millions of dollars in prizes up for grabs for best vibe coders out there.

Setting the Stage

Most expert developers have been of the opinion that AI just can’t write expert-level code (although more and more are changing this view with time).

So, this approach coming from Karpathy may have been a surprise to some — him being one of the most respected engineers in the world.

Twitter tweet

And if you ask Karpathy, who co-founded OpenAI and for a long time led Tesla’s AI and self-driving initiatives, LLMs’ ability to code will only get better from here.

The thing with vibe-coding is it opens doors for people who have neither any experience with coding nor any interest in learning it.

The second bit is important because it highlights the difference between vibe-coding and generally AI-assisted coding.

Coders generally use AI assistance to quickly write repetitive or mundane stuff.

Say when coding in Django, I need to frequently write view functions to render a template with some data from module — and I just use AI to write these now, and save myself 10-15 minutes each time.

My Snakes game and Artificially Boosted landing page are all vibe-coded, while I would never ever leave the driver’s seat with my main projects Dzambhala or TushitaAI, even when using a lot of assistance from AI.

The Hottest Programming Language To Learn In 2025 Is English

I think it is understandable why top engineers don’t think AI is particularly helpful for coding in practice. Same would apply for a top journalist thinking AI can’t write a good column or a top tax lawyer think it isn’t helpful for legal research.

Yet, I think all of these critics put emphasis on the wrong point.

Take me, for example.

I never let AI write my words. It is a craft I have honed over a lifetime and it just can’t compete with me or even better me when it comes to conveying my thoughts in way that I can, with emotions and a sense of emotional quotient (although I love GPT-4.5’s attempt). But it does serve as a good aide in helping me find the right direction, or help tackle writer’s block.

As a writer, I don’t need chatgpt to “vibe write” for me — it would be darn offensive.

Yet, ChatGPT is why my family members don’t ask me to write their emails any more, and I am very thankful to it for that.

At the same time, I don’t have any issues with AI generating images in entirety for me, or vibe-coding an entire landing page. Those are not areas I am expert in or have any emotional attachment to — so it saves me a lot of time, effort and money to have it do those tasks.

So, in a similar vein, I think while those top developers might not think much of vibe-coding, but it would have very real repercussions for entry-level and lower-skilled devs, who are simply not needed anymore. AI can code the mundane, repetitive and at times, even very creative stuff just fine.

Why Some People Are Struggling With Vibe-coding And Giving Up

The biggest challenges non-coders face when starting with vibe-coding is:

1) Codebase getting too large that AI becomes ineffective at keep going.

2) security of their deployed app

There are a couple of example floating around on Twitter, with people who took entire vibe-coded projects live, getting hammered by cyberattacks.

Twitter tweet

The first one happens due to limited context windows in models. There is only so much information an LLM can remember at a time.

While most LLMs are seeing this context window rise, that doesn’t necessarily bring solution.

There are a lot of internal mechanics involved and the more “context” an LLM has when responding to a query, the more ineffective or imprecise it can be, as a result.

The solution here is to keep code fragmented, and only vibe-coding small projects to begin with.

So, it is completely fine to vibe-code a static website or game, but don’t keep hopes of being able to code an entire social media app or a finance terminal through this process just yet.

And learning git control can be an important step here, so you have a better ability to “reverse” the code, if something gets ducked up.

Security concerns are even more tricky to deal with — and can happen to anyone, vibe-coding or not.

I am for example going through a subscription form spam attack for Dzambhala right now, and working through adding more security layers to bring about a fix.

Typically, security issues happens on front of you telling the LLM the API key, accidentally exposing it directly in the code, not having enough spam detection mechanisms or database security.

If you are coding static websites, this is something you have to worry about very little. Entry for cyberattacks typically come from javascript, dynamically-rendered html or API keys.

New Builder’s Corner

Early on in my writing career, I paid a dev INR 10,000 (~ $116) to code a personal website for me to showcase my work to potential clients.

Now the same work can be done in less than 10 minutes using AI and I wanted to share with you how.

This is the initial prompt and follow-up prompts I used with Claude 3.7 sonnet:

The results of the game are available here: https://vibe-code-resume.pages.dev/

You could also keep going with this in Cursor, including creating a portfolio section and more! Tried this and want more help with it? Respond to this email. I will also soon share a way to deploy these websites for free.

From Around The Web

Levelsio is organizing a competition to vibe-code the best game with AI. Judges include Karpathy, the man himself, and a couple of other leading game developers

Twitter tweet

Give it a shot if you ended up vibe-coding a game!

Bolt.new is also organizing some hackathons for vibe-coding, with cash prices!

I am continually working on ways to improving the value this newsletter brings to audiences of different calibre when it comes to using AI! If you have any feedback, please share anytime!

I hope you all had a great weekend, and belated happy Holi to all my fellow Indians who celebrate! 🎨

AI development all throughout has been much of a dick-measuring contest — whose model can beat which benchmarks takes center stage when announcements are made.

But I just had a realization, companies touting how their AI beat a benchmark and created a new record is much like Asian parents bragging about their kids’ grades — it’s an important metric but has limited bearing out in the real world.

This doesn’t just apply to models made by different companies but also, surprisingly, to different versions of the same model.

Imagine a software developer excitedly upgrading to the latest AI model touted as “state-of-the-art,” only to find the older version diagnosing a tricky bug that left the new model stumped.

This scenario is playing out across coding assistants, chatbots, and reasoning engines. AI models that score off the charts in benchmarks sometimes falter on the ground, while their smaller or older siblings quietly excel in specific tasks.

I am talking particularly about my experience with Claude 3.5 Sonnet and 3.7 version of the same.

Claude 3.7 Sonnet is the first hybrid reasoning model on the market and it generated a lot of value for that but when it comes to coding, especially debugging — it just can’t seem to match up to its predecessor.

I am not the only one in my experience as several conversations across social media attest.

OpenAI Is Ramping up Bets on an Agentic Future

OpenAI touted customized GPTs that users could access in the ChatGPT interface as the next big thing in 2023 and that didn’t quite play out as intended.

Now, the company seems to have made amends and bet on an Agentic future — built both by the company and other developers using its APIs.

I mentioned on Wednesday how Google is pivoting to make third-party apps a crucial part of its AI strategy.

Japan's venture fund SoftBank shelled out $676 million to buy an old Sharp plant to convert it into an AI data center for its partner OpenAIl. This center would support both ChatGPT maker’s and also “external businesses” in what seems to be the two partners’ plan to support development of AI agents for the Japanese market.

This is backed by Sam Altman’s narrative focusing more and more around Agents following Operator and Deep Research launch and the latest API rollouts for developers, encouraging more agents to be built using its LLMs.

Google Wants You To Let It Read Your Search History

If you ever get the creeps from Mark Zuckerberg’s hyper-ad personalization on Instagram and Facebook — brace yourself.

Now, Google thinks it’s a great idea to let its AI know everything about you — including your search history or your Google Drive-stored files.

The tech giant is starting the experiment by letting you voluntarily let its Gemini Flash Thinking model know your search history and personalize your answers based on that.

The cat is yet to come out of the bag on privacy issues with AI, so I am just going to leave you with this thought here.

Builder’s Corner

Today, I wanted to expand a bit on how to prompt AI to perform end-to-end tasks using the Snake game.

If you remember in the last newsletter, the game the AI designed was rather basic — but also unsurprisingly, all of my readers who tried making the game ended up with a similar-looking game using Claude.

This is where our topic of the day — the feedback loop with AI comes in. Based on the initial response you get from the LLM, you want to keep giving it feedback to make corrections or upgrades.

For examples, in my Snake game, I asked it to make frog food, a milk boost, a turbo speed boost, change the color of the game, create a sidebar featuring my ads and more — all through sequential feedback like this.

Above are just a couple of examples of how I give AI feedback when building a product. This method, especially when done through code, is what is called reinforcement learning.

It really is that simple and intuitive. Would you improve your version too? Do share with me, if you do.

From Around The Web

This tweet that came in response to this teen on X making a tool that lets you copy any website’s design is the most fun thing I read since last time.

Twitter tweet

The entire dev community building with AI is vibing over MCPs right now, but do we really need a new thing to hype over every day? Especially when the best generative AI products may be hiding in plain sight.

The Big Tech Priorities

ChatGPT has the first-mover advantage and caused the initial shock with GPT 3.5, so it makes sense that it is the most popular AI product on the market.

DeepSeek briefly took over amid the hype-cycle. Grok is benefiting from its integration into X.

Meta is sitting out on the business-to-consumer (b2c) race right now even as it has developed some of the most impressive large-language models (LLMs) yet seen on the market in the open-source LLM series.

It has a “Meta AI” chat available on its platform but Mark Zuckerberg’s company doesn’t seem to invest in marketing it.

Apple is also focusing on native integration into its existing products through Apple Intelligence (a major failure, for what it is worth), rather than trying to grab a piece of the quickly-growing generative AI chat userbase.

Even as all of this seems more straightforward, Google’s case is more noteworthy.

To Be, Or Not To Be

When Wall Street first began reading the implications of generative AI — Google was identified as one of its first victims. If everybody is asking ChatGPT everything, who would be asking Google anything?

The question clearly bugged Google — it launched its own rival ‘Bard’ in a rush — and, I.T. W.A.S. A. D.I.S.A.S.T.E.R.

Google’s Bard and initial Gemini launches were disaster.

The generative AI couldn’t answer comparably to other models at the time and its photo generation grabbed attention — not for image quality — but for political correctness.

The “woke” AI just wouldn’t generate images of white people even when asked in appropriate context, and it became a hot topic at a time when the U.S. was going through a political shift.

Google stock tanked, everyone wrote the company’s obituaries.

Yet, the vibes have been shifting over the past year, and not enough people outside of tech seems to notice.

Google’s Shifting Priority In Gen AI

The tech giant’s public-facing AI chat interface is still not popular — and it isn’t particularly good either. Certainly, can’t compete with ChatGPT, Claude or Grok.

But here is the thing — I don’t think Google cares anymore. It has different schemes.

Giphy

Google seems to now have two priorities in AI: integrate gen AI into interfaces where its users are and second, give tool to other passionate developers and let them build cool products using its APIs.

The first means you see the email writer AI in gmail, you see “AI overviews” in Google Search (or now the “AI chat“ coming soon!), you can have AI do templating or other work on your Sheets, or write in Google Docs.

The second is a bit more interesting, at least to me! Google AI Studio for developers is now by far the best AI API interface for developers out there! Seriously, it is just sooo much better than OpenAI’s Playground.

I seriously began noticing the product when Google hired Logan Kilpatrick, formerly of OpenAI, a year ago to lead product development for the AI studio.

Logan has an impressive reputation for listening to developers when it comes to building products that actually work for them and I had been following him for a while.

Google has been rolling out LLMs after LLMs — its Flash Thinking model is incredible at reasoning and available at throw away prices. The Pro 2.0 performs way better than GPT-4o at most tasks, for example.

While all developers get free access to the models that are still in experimental stage, even the production-ready models are priced very competitively.

A Silent Turnaround

When Google takes the Thinking model out of experimental stage, much of narrative around AI would have moved on from Reasoning model to something else — so the company may miss out on the marketing but the fact that it has a reasoning model at pricing that outshines competition by far wouldn’t have changed.

Anecdotally, I am hearing more and more from my developer friends that they are switching over from OpenAI to using Gemini API for several use-cases.

Generative AI is a rapidly-progressing space and media and marketers are looking for shiny gold nuggets — but I have changed my mind on Google.

It may just survive the storm when it all blows over.

WTF’s An MCP?

If you've been anywhere near AI Twitter this week, you've probably seen people throwing around the term "MCP" like it's the latest tech revolution. But hold onto your GPUs — it's not exactly groundbreaking.

"MCP," or Model Context Protocol, is basically a standardized way for apps to pass context to LLMs.

Think of it like giving your AI better briefing notes before it starts talking—so it's smarter, sharper, and doesn't embarrass itself.

Tools like Claude and Cursor have been playing with similar ideas for months, and it's closely related to something called Retrieval-Augmented Generation (RAG)—a fancy term for "fetching extra context for smarter responses."

Sure, MCP makes things tidier and more convenient. But despite Twitter bros hyping it like the AI equivalent of sliced bread, it's more incremental improvement than revolutionary leap.

It’s just Twitter bros being Twitter bros.

Gotta leave you with this sarcastic tweet from Pieter on the topic though.

Twitter tweet

Builder’s Corner

I wanted to do a small section for non-developers to learn how to get gradually better at coding using AI! Let me know if you like it or not through the Feedback section below.

I am surprised that a lot of people are using dedicated “AI website builder” apps at premium pricing, when you don’t really need them.

To get started with learning about the capabilities of current AI, let’s start with coding our childhood-favorite snakes game.

Put this prompt in Claude (or Cursor/any IDE assistant if you use that):

Now take this content and paste it into any text editor on your laptop, save it as “index.html” or “snakes.html.”

Open the saved file in your web-browser by clicking on it, and you have got your own personal version of the game!

Want to customize it further? Just do follow-up prompts like “I want it to have a triangle head instead. Or I want the background of the game to be different.“

So, now you don’t need to go to any website to play a game but just on your own laptop. Check out my results at: https://snakes.artificiallyboosted.com/

AI is also a great way to learn, so if you want to know more about how the game is being developed, just try asking the AI, “how is the code functioning at a high-level?“ or “what is javascript and what role does it play in this game?”

If you take if far enough, you may just end up making a game that is perfectly customized for your needs!

Did you like this small little tutorial? Should we do a builders series?

From Artificially Boosted Family

• We have rolled out hotel recommendations on https://tushitaai.com/ main chat and also customized destination recommendations based on your location! Working on improving speed.

• Dzambhala Nodex was first to break news of Starlink-Airtel partnership! This is just one example of how AI is set to play a crucial role in consumption of financial data and information.
  

We are growing everyday! 🎉 If you have friends, family, acquaintances or even (pesky) relatives who may enjoy this newsletter, please share! It means a lot.

I read and take all feedback very, very seriously! That’s what is helping improving this newsletter everyday. Just reply to this email.

It’s time for deja vu because we are witnessing another DeepSeek moment.

Another little-known Chinese startup Monica.im has dropped an independent AI agent — if the demo video and early reviews are to go by, it may be the most useful one on the market yet.

What Are AI Agents?

AI agents are built on top of LLMs. Unlike say, when you are interacting with ChatGPT, interacting with an AI agent wouldn’t involve as much back and forth, because they are designed to perform a task end-to-end.

So for example, if I wanted to tweet out Dzambhala NodeX articles, I could go to ChatGPT and write out each prompt every time there is an article and have it write a tweet and post, or I could design an AI agent that studies all articles as they come and decide on their tweetworthyness and the exact content and hashtags and formats they should go out in — that’s exactly what the Nodex India AI agent on X is doing right now, for example.

AI agents are not meant to replace the LLM experience but actually to act “more human” — like an employee, a therapist or even your pet.

They are all about the full experience and that also makes the threat of them “replacing” a human at work (or in love-life?) more real.

What Is Manus AI Agent?

Manus is the AI agent developed by the Chinese startup Monica and it can perform tasks like

• reviewing resumes of all candidates that have applied for a job similar to a human recruiter and send out follow-up emails if the relevant accounts are connected

• it can generate and deploy an entire website with your data and instructions

• it can conduct in-depth research on a topic of your interest

• it can write an e-book with proper formatting or generate a course for you

These are just a few of the use-cases for the AI agent that’s just a couple of days old at this point.

How is Manus AI different from other agents?

There are already quite a few AI agents in the market including OpenAI’s Operator and Deep Research and Claude’s Computer.

It seems Manus AI is outperforming Deep Research on key benchmarks like GAIA and OpenAI has yet to roll out Operator to a wider audience amid tech glitches.

Manus AI seems to be the truly-first general AI agent that works to about to hit the market.

Is Manus AI an AI Breakthrough?

AI agents are secondary-layer products built use large-language models. In that sense, they are less about breakthroughs fundamentally in the machine learning space but more applied utility in terms of how LLMs integrate with user experience to perform tasks we actually need help with day-to-day.

Is It All Hype?

AI is a budding space and there is always hype. It took quite some time to distill the truth from hype with DeepSeek and pretty much every single AI product (every move and announcement is revolutionary and pathbreaking for Twitter bros)

Some reliable early reviewers like Hugging Face head of product Victor are impressed with the AI agent.

Twitter tweet

New Happenings

• Google has launched an “AI mode” in its Search Engine as an experimental feature to Labs users, while it expands AI overviews.
  
  It looks like an interesting development in AI-fication of Search Engines. There would be a dedicated “AI mode” tab on Google Search for the Labs users and from Google’s announcement, it seems the company intends to go for a “Deep Research” like experience here.

• DuckDuckGo Takes AI product live: Google’s privacy-oriented alternative DuckDuckGo has taken duck.ai live with ability to chat with multiple models including GPT-4o mini, Claude 3 Haiku and open-source models Llama 3.3 70B and Mistral Small 3. The company is promising the same edge: that your data remains yours when chatting through its platform.

• You can now ask Grok anything by mention @grok in replies on X. Not sure if it was in response to that, but pretty interesting that it comes soon after Perplexity launched “Ask Perplexity” to much fandom on the platform.

• OpenAI mulls a $20,000 a month AI agent: Umm, yeah. No typo here. Sam Altman says it would be able to research at the level of a “PhD student.” Y’all know you can pay an actual PhD student armed with OpenAI o3 or DeepSeek R1 and get wayyyy better bang for buck right? (Whatchu thinkin’ Sam?)

• Google co-founder Larry Page is reportedly working on an AI startup to design some kick-ass products and then just have them built at a factory (via The Information.)

• Mistral just made progress on processing very large PDFs with AI with its new API OCR.

Parting Thoughts

Fun Fact: I don’t use AI’s help in writing this newsletter about AI.

As always, feel free to check out my products TushitaAI or Dzambhala Finance.

After GPT-3.5 launched and gained popularity, OpenAI decided on a $20 fee we would all pay for a premium subscription to get all the generative AI access we want.

What went into these calculations? Unlikely much.

It was a standard fee OpenAI executives decided would go well for a subscription-based product.

Others like Claude and Perplexity were forced to follow suit with sticking with $20 number as now nobody would pay a penny more.

Except, it turns out that users really wanted to make bang for the buck when paying for the subscription and with the usage, the GPU costs have been running too high — making the 20 bucks subscription loss-making for generative AI companies.

OpenAI sought to correct this with a whopping $200 subscription that gave high-flying access to its top-tier models like o1 and o3 and AI agents like Operator and Deep Research.

Except, Sam Altman revealed that the usage even on this tier is so high that OpenAI simply can’t make a profit.

OpenAI set the bar and everyone else was forced to follow suit.

The DeepSeek Surprise

In all this, DeepSeek revealed a surprise — that it expects a theoretical profit margin of 545%.

It’s theoretical because DeepSeek isn’t accounting for charging less during non-peak hours, differences in its models and the fact that it’s providing a whole lot of access via the web interface on chat.deepseek.com and the mobile apps.

So, how is DeepSeek able to turn profit? Well, because it claims to be using GPUs that cost a lot less in a lot less quantity. And it also carries a much smaller team — about 200 estimated against say OpenAI’s over 2,000 employees.

Anyway, it’s a bit moot to talk about DeepSeek, owing to trust issues with how information from China usually comes about. Nevertheless, it’s the most exciting project around on two fronts — its breakthrough in reasoning model that remains unparalleled and its contribution to indiehacker and open-source ethos.

What’s the right price to pay for AI?

The real value for any company or industry comes about with an answer to a simple question: Do the users want to pay for your product or service?

Venture Capitalists are pouring billions and billions into AI development with hope for glorious returns. Leaders in the space like OpenAI struggling to strike the balance in costs and revenue is not a good sign.

Some industries like Web3 are forever stuck in this “budding industry” zone and AI won’t want to be in this company.

So, how would the profit come about?

Sam is now toying with the idea that we perhaps pay as we go for what we are using.

Twitter tweet

I am not sure where that proposal is coming from but I expect that to be a disaster if implemented.

This isn’t dissimilar to how we pay for OpenAI APIs when building our own apps but counting tokens is just not the way people want to use an end-consumer AI chat app.

Most gen AI users have no clue WTF is a LLM token

The ‘Heavy User’ Burden

The flat free model currently implemented may be hurting right now but may just work out long-term.

Why you ask?

Because the early adopters are also likely to be the heaviest users.

For flat subscription fee to pay dividends, the companies need more of occasional queriers and fewer AI nerds like me.

A better solution would possibly be to keep the $20 subscription with existing unlimited base-models access (GPT-4o, o3-mini, o3-mini-high etc.) and give limited premium features such as 5 Deep Research usage or 3 GPT-4.5 prompts, with ability to pay for more as you go.

A rudimentary analysis of how different pricing models would stack up for OpenAI, generated with custom script in Python.

Pay-per-use makes a lot of sense with heavy skew toward power users, which is the likely scenario at the time. With wider adoption and a changing skew, you are likely get better results with a flat fee structure.

Charge $20 plus extra for added usage, and you have the optimal scenario — both for the user and the company. At least, that is what the trend seems to be right now.

That’s the thought I leave you with today.

New Happenings

• Claude parent Anthropic has raised another $3.5 billion in a funding round that values it at a whopping $61.5 billion!

• OpenAI is bringing its most “emotionally intelligent” model GPT-4.5 to Plus users.

• Deutsche Telekom is launching an “AI phone” in partnership with Perplexity. (I expect it to be nothing of note.)

• Amazon may be working on its own hybrid-reasoning model, similar to what was launched by Claude recently in Sonnet 3.7.

• Key Nvidia partner CoreWeave, which is on track for an IPO, has acquired AI developer platform Weights & Biases.

From The Artificially Boosted Family

Dzambhala’s first public AI agent Nodex India is live in rudimentary form on X, Reddit and Telegram — serving news and analysis on Indian markets.

We are leaning toward sending this newsletter biweekly — on Wednesday and Sunday evenings SGT (GMT+8).

As always, direct any feedback to neer@dzambhalafinance.com and don’t forget to try out the Dzambhala NodeX dashboard!

Up until say six months ago, I was very jittery whenever people targeted me for building “GPT wrappers.”

“Anyone can do that,” they said.

Giphy

Times have changed. I am now convinced in my belief that the real products would be built at segmented application layer.

I am now paying the same $20 for a Cursor subscription.

Imagine having a professional coder that live right in your IDE and helps you as you type, as you think and you don’t have to constantly engage in a back and forth on a separate platform.

What Cursor has built with using Claude, DeepSeek and OpenAI APIs is incredible. It is the application layer for coding segment that it has that makes the product astounding.

Think similarly of other products, do you want to prompt ChatGPT to write your email or do you want that in the gmail compose email?

Do you want to talk to ChatGPT to sketch out a travel plan or would you rather just go to a dedicated app that is trained to book your entire travel in one go!

Integrated AI Interfaces Are Going To Win Out

Another reason it kinda sucks to pay for a foundational model-based interface like ChatGPT is that there ins’t a one-size fit all.

For example, while OpenAI has made better models in general, xAI’s Grok is astounding at generating images.

Elon Musk painting on a canvas. Image generated with Grok. Couldn’t have been possible with Dall-E

There is no way Sam Altman would ever bring Grok’s image functionality to ChatGPT.

Similarly, what if you wanted to transcribe an audio quickly? Google’s Speech-to-Text model excels at that! And is available at throwaway prices in its AI studio!

So, I would much rather pay for a product that is able to seamlessly integrate a plethora of these models into one interface via API calls.

Total win-win. Foundational models make money, interface makes money, user is happy.

Yes, I want an AI interface that calls Grok to generate images or for sass, OpenAI for conversational messages, Claude for coding, and Google API for transcription.

AI Is Going More Human

OpenAI just released its latest model GPT-4.5.

This time, Sam Altman doesn’t want you to think about benchmarks or asking it tricky logical or scientific questions. It would fail at all that.

Instead, it is designed to be more human — and that may mean sucking at math, in exchange for giving more human-like, more thoughtful response, displaying a high emotional quotient and less hallucination.

At $75 per million input tokens and $150 output — it is now by far the most expensive AI out there in the public domain.

I am not writing a lot on it, as I haven’t tried the model out a lot beside as an anonymized guinea pig in ChatGPT.

Experts whose opinion I value who have tried it do say it performs better at engaging writing, so I look forward to giving it a shot.

My first impression is the pricing doesn’t seem to be pragmatic at a time the wider narrative is around bringing down the costs of AI for customers.

My Favorite AI Tweet of the Day

Twitter tweet

Parting Thoughts

This newsletter is written for you, my friend. I am taking out time everyday to make sure I jot down my thoughts on what I personally find to be the most exciting technical field of our lifetime.

Please give feedback on what you like, don’t like. What can I do to make you better informed about AI?

Respond to this email and let me know and don’t forget to check out our core product — the Nodex.

If you are worried about AI taking over your job, take solace — you are in good company.

Computer scientist Carl Feynman, whose master's thesis in 1986 was crucial to modeling the appearance of cloth in computer graphics, is overwhelmingly impressed by generative AI doing the same task — so much so, that he wonders if he would keep getting paid for what he does.

Twitter tweet

The remark may be a bit tongue in cheek, given that it’s Feynman’s work itself that makes AI having this knowledge possible but it does highlight a worry — that AI won’t just replace humans in doing mundane repetitive stuff, but in also performing some of most intelligent tasks.

I mean, in my personal workflow, I am using AI more and more, including to code out entire programs in Python.

Developing Dzambhala Nodex itself won’t have been possible just two years ago, without hiring an array of engineers working on devops, javascript and python.

Anyway, in major updates, Feynmann was reacting to the Claude 3.7 Sonnet model that was launched earlier this week.

The World’s first Hybrid ‘Reasoning’ Model Is Here

Claude released its latest model, the 3.7 Sonnet, on Tuesday, as the world’s first hybrid reasoning model.

That just means it can decide b/w when to give you an instant response v/s when to think deep and reason based on your prompt, unlike OpenAI’s o1 or DeepSeek’s R1, which exclusively always reason.

This is pretty interesting because Sam Altman did say an integrated experience like this is what he intended with ChatGPT.

OK, but to me that isn’t the most impressive thing with Sonnet.

The model is state of the art at coding — really OpenAI and Grok just can’t compete here.

It’s been just 3 days, and it has already replaced 3.5 Sonnet as my go-to model for coding.

Yet — the most impressive and creative use for the new model hasn’t come from me. Pieter Levels, the chief among indiemakers, designed an F-16 plane with it and coded an entire flight simulator game with it!

Twitter tweet

New Happenings

• Perplexity is gearing to launch its own web browser ‘Comet’ with agentic search.

• Paytm is integrating Perplexity’s AI search engine into its payments app.

• Amazon has unvelieved the next generation of its Alexa virtual assistant, this time powered heavily by (you guessed it — generative AI)

• ElvenLabs released its new speech-to-text model Scribe.

• Inception Labs released the first diffusion-based LLM. Karpathy sees a lot of potential here.

About This Newsletter:

You just read the first iteration of Artificially Dzambhala — our AI newsletter. It’s written by me — Neer, the founder.

We will be experimenting with timings and frequency of this newsletter but do write to me at neer@dzambhalafinance.com to let me know your thoughts or feedback.

You can also connect with me on X.

Want to up your investment game? Check out Dzambhala NodeX!