Here’s what’s on our plate today:

• 🇮🇳 India becomes AI’s favorite giveaway market and testbed.

• 🧾 Quick Bits: MAI-1’s debut, Sora goes Android, MacBook may go budget.

• 🧪 Microsoft’s MAI-1, Sora for Android, Leonardo’s Canvas Mode.

• 📊 Cast your vote: Which AI model wins in the long run?

Let’s dive in. No floaties needed…

The Laboratory

How Palantir is powering the unseen engine of the AI economy

A long-standing principle that investors follow to ensure they do not fall for hype and make good investment choices is to invest in boring businesses. These are essentially businesses that derive value by providing goods or services which, though essential, are often overlooked due to their lack of pomp and show.

The philosophy is used by respected investors like Warren Buffett, who often said that the most dependable companies, such as insurance firms, railroads, or consumer goods makers, tend to be the least glamorous but generate steady profits over decades.

When it comes to technology, though, it becomes difficult to differentiate between hype, use cases, and the long-term profitability of a company. Especially at times when talk of an AI bubble dominates headlines.

Take the example of artificial intelligence. While companies like OpenAI, Google, Meta, Microsoft, and others are busy building the foundational models that made AI systems possible, they have yet to make a profit from the implementation of the technology.

Though Nvidia, the company that makes the GPUs for AI workloads, has become the first company in history to reach a valuation of $5 trillion, reports suggest that 95% of generative AI business efforts are failing, with only 5% achieving meaningful revenue growth. This indicates that while AI can generate value, not all companies will manage to tap into it.

However, some companies have been able to tap into the power of deep learning and powerful AI systems to perform boring jobs, which may be where the real opportunity resides.

A fine example of such a company in 2025 is Palantir.

Graduating from data to AI

Palantir was founded in 2003 as a data analytics and integration firm, focused on helping organizations make sense of massive, complex data sets.

Its early reputation came from its work with U.S. intelligence and defense agencies, where its Gotham platform was used to track terrorist networks and uncover patterns in large volumes of information. At this time, it was more about data organization, visualization, and human-assisted analysis.

However, in recent years, especially since the release of AI models, Palantir has pivoted sharply toward AI-driven decision-making systems. These systems allow clients in government, defense, healthcare, energy, and manufacturing to run large language models and predictive AI tools on top of their own data, within secure environments.

Essentially, it enables organizations to use AI to simulate outcomes, automate responses, and make real-time operational decisions.

Palantir’s AIP systems advertise one-click use-case blueprints and tightly governed agent workflows that can write tickets, schedule crews, or push tasks to field devices while logging every step.

However, unlike other AI companies, Palantir is not chasing AI headlines with its products; quite the contrary. It is locking in large-scale contracts, especially with governments, and turning those into recurring revenue.

A recent report from Reuters said that the company’s forecast for its fourth-quarter revenue beat analyst estimates. Its stock value has also doubled, surpassing the gains of Nvidia as well as the benchmark S&P 500 Index.

Driving this growth are the government contracts with at least seven federal agencies. The latest of which is worth up to $10 billion to be spent over the course of a decade, ensuring a steady inflow of revenue for the company.

The contract was announced close on the heels of $480 million that the military allocated earlier this year to put into its AI software, Maven Smart System. This contract was also awarded to Palantir.

What exactly does Palantir do?

At its core, Palantir builds software platforms that help large organizations, especially government agencies and heavy industry, make sense of their data and then act on it.

The company’s Foundry platform integrates messy data from legacy systems and disparate sources, bringing them into a coherent architecture. The newer Artificial Intelligence Platform (AIP) connects large language models (LLMs) and other generative or analytic AI tools to operational workflows, allowing these systems to not just make sense of data but also suggest actions to achieve targets.

In practice, for a government intelligence agency or an airline maintenance group, Palantir gives a unified ‘decision stack’. This means that the company takes in data, processes it, implements it in workflows, and audits logs under one solution.

Unlike companies like OpenAI, when government agencies utilize Palantir’s platforms, namely, AIP, they get access to tools that don’t just analyze information but actually trigger and track actions, issuing commands, generating reports, scheduling maintenance, or allocating resources. In short, it’s the machinery that turns AI into something you can run a business or a battlefield on.

This focus on integration and operationalization explains why Palantir keeps winning government business. Most public-sector systems are vast patchworks of legacy databases, half-modernized clouds, and siloed analytics.

Palantir’s value proposition is to overlay that complexity with a single data fabric that makes everything interoperable, auditable, and actionable.

Palantir has firmly established itself as a trusted player in bringing AI to real-world applications, for instance, helping the U.S. Department of Defense process drone and satellite imagery for data ingestion, model management, human oversight, and large-scale explainability. Yet, its rise has also stirred debate.

The not-so boring side of Palantir

The use of Palantir’s platforms by government agencies in both military and civilian infrastructure has led to concerns around misuse. Civil-liberties groups warn that software originally designed for counterterrorism shouldn’t sit so close to domestic healthcare data.

In Britain, trade unions representing teachers and other workers staged protests when Palantir was contracted by the Coventry City Council for a £500,000-per-year deal to use its services in the council’s children’s services department for tasks such as transcribing case notes and summarising social workers’ records.

Even when used for military purposes, critics argue that AI-assisted targeting risks reducing human oversight.

On its part, Palantir insists it never owns or monetizes that data and that its systems exist only to integrate and secure it. The company further argues that improved data fusion actually increases accountability and precision.

The real power of AI

Palantir’s approach to AI is unlike anything seen in enterprise software. The company doesn't rely on the novelty of a model. Its approach is to embed AI into the actual bloodstream of an organization.

The company runs what it calls bootcamps, where in one-to-five-day sprints, engineers work side by side with a client’s team to turn data into a functional AI workflow. This helps prove the value of AI immediately, then scale what works. This approach has turned Palantir into something like an AI general contractor.

In a sense, the company’s approach to AI may be the one that matters the most. The consumer-facing world gets the headlines, chatbots that mimic humans, digital artists, viral demos, but the enterprise side quietly commands the dollars.

Integrating AI into operational systems, building audit trails, securing data, and scaling deployments are unspectacular tasks that generate recurring value.

Which brings us back to the original idea of business. Companies that do the most boring jobs are the ones that generate long-term value, and in the case of AI, Palantir’s business model appears to be proving just that.

The company margins are climbing, and its contracts span a decade, not a quarter. So, while analysts debate the existence of a bubble in AI investment, Palantir is quietly tapping into the power of AI to service real value, which may survive the bursting of even the biggest investment bubbles.

Quick Bits, No Fluff

• Microsoft’s MAI-1 image model hits the leaderboard: The new multimodal model debuted in the top 10 on LMARena, giving Microsoft a fresh foothold in the image generation race.

• OpenAI brings Sora to Android: The generative video app is now available on Android, signaling OpenAI’s push to broaden distribution beyond web and iOS.

• Apple may drop a budget MacBook in 2026: Reports hint at a lower-cost MacBook aimed at competing with Chromebooks, which could launch as early as next year.

Thursday Poll

3 Things Worth Trying

• Try Microsoft’s MAI‑Image‑1: Microsoft’s new in‑house image model cracked LMArena’s Top 10.  

• Start creating with OpenAI’s Sora: The Sora video‑creation app is expanding to Android and will be available soon.  

• Try Leonardo’s new Canvas Mode: Leonardo AI just launched an interactive, Photoshop-style canvas for refining AI images.

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