Darren Mowry, who leads Google’s global startup organization across Cloud, DeepMind, and Alphabet, says startups with these below hooks have their “check engine light” on.

The two once-hot business models are looking more like cautionary tales:

• LLM wrappers
• AI aggregators

LLM wrappers are essentially startups that wrap existing large language models, like Claude, GPT, or Gemini, with a product or UX layer to solve a specific problem. An example would be a startup that uses AI to help students study.

“If you’re really just counting on the back-end model to do all the work and you’re almost white-labeling that model, the industry doesn’t have a lot of patience for that anymore,” Mowry said on this week’s episode of Equity.

Wrapping “very thin intellectual property around Gemini or GPT-5” signals you’re not differentiating yourself, Mowry says.

“You’ve got to have deep, wide moats that are either horizontally differentiated or something really specific to a vertical market” for a startup to “progress and grow,” he said. Examples of the deep-moat LLM wrapper type include Cursor, a GPT-powered coding assistant, or Harvey AI, a legal AI assistant.

In other words, startups can no longer expect to slap a UI on top of a GPT and get traction on their product like they could, perhaps, in mid-2024 when OpenAI launched its ChatGPT store. The challenge now is to build sustainable product value.

AI aggregators are a subset of wrappers — they’re startups that aggregate multiple LLMs into one interface or API layer to route queries across models and give users access to multiple models. These companies typically provide an orchestration layer that includes monitoring, governance, or eval tooling. Think: AI search startup Perplexity or developer platform OpenRouter, which provides access to multiple AI models via a single API.

While many of these platforms have gained ground, Mowry’s message is clear to incoming startups: “Stay out of the aggregator business.”

Generally speaking, aggregators aren’t seeing much growth or progression these days because, he says, users want “some intellectual property built in” to ensure they’re routed to the right model at the right time based on their needs — not because of behind-the-scenes compute or access constraints.

Mowry has been in the cloud game for decades, cutting his teeth at AWS and Microsoft before setting up shop at Google Cloud, and he’s seen how this plays out. He said the situation today mirrors the early days of cloud computing in the late 2000s/early 2010s as Amazon’s cloud business started taking off.

At that time, a crop of startups sprang up to resell AWS infrastructure, marketing themselves as easier entry points that provided tooling, billing consolidation, and support. But when Amazon built its own enterprise tools and customers learned to manage cloud services directly, most of those startups were squeezed out. The only survivors were the ones that added real services, like security, migration, or DevOps consulting.

AI aggregators today face similar margin pressure as model providers expand into enterprise features themselves, potentially sidelining middlemen.

For his part, Mowry is bullish on vibe coding and developer platforms, which had a record-breaking year in 2025 with startups like Replit, Lovable, and Cursor (all Google Cloud customers, per Mowry) attracting major investment and customer traction.

Mowry also expects strong growth in direct-to-consumer tech, in companies that put some of these powerful AI tools into the hands of customers. He pointed to the opportunity for film and TV students to use Google’s AI video generator Veo to bring stories to life.

Beyond AI, Mowry also thinks biotech and climate tech are having a moment — both in terms of venture investment going into the two industries and the “incredible amounts of data” startups can access to create real value “in ways we would never have been able to before.”

Source: TechCrunch

This week's launch of 5 indigenous LLMs, ranging from Sarvam's 105B model to Tech Mahindra's Hindi-first educational AI.

Which one have you given a try yet, and what's your feedback on them?

Over the last two years at Microsoft, and previously as Chief Operating Officer at Instacart and a Vice President at Meta, Asha has helped build and scale services that reach billions of people and support thriving consumer and developer ecosystems. She brings deep experience building and growing platforms, aligning business models to long-term value, and operating at global scale, which will be critical in leading our gaming business into its next era of growth.

Matt Booty will become Executive Vice President and Chief Content Officer, reporting to Asha. Matt’s career reflects a lifelong commitment to games and to the people who make them. Under his leadership, Microsoft Gaming has grown to span nearly 40 studios across Xbox, Bethesda, Activision Blizzard, and King, which are home to beloved franchises including Halo, The Elder Scrolls, Call of Duty, World of Warcraft, Diablo, Candy Crush, and Fallout.

Source: Microsoft

Sarvam founder Pratyush Kumar says, "We’re gradually rolling out Indus on a limited compute capacity, so you may hit a waitlist at first. We will expand access over time.

Also, we believe Sovereign AI must be built with the country, not just for it. That means learning from the people who understand India best - its everyday users, developers, researchers, and creators. So, try out the app and let us know what works well and what doesn't. We are in listen mode."

Check them out here.

OpenAI says 18 to 24-year-olds account for nearly 50% of ChatGPT usage in India.

The company said on Friday that users between 18 and 24 years of age accounted for nearly 50% of messages sent to ChatGPT in the country, and users under 30 accounted for 80%.

The AI lab said Indians use ChatGPT mostly for work, with 35% of all messages relating to professional tasks, compared to 30% globally.

In particular, the company’s coding assistant, Codex, is seeing strong traction: OpenAI said Indians use Codex three times more than the global median, and weekly usage has increased by four times since the tool got a Mac app two weeks ago. Users in India are also asking three times as many coding-related questions as the median.

This is in line with findings from Antropic, which earlier this week said 45.2% of Claude’s tasks map to software-related use cases in India.

OpenAI said outside of work tasks, 35% of messages to ChatGPT from Indians requested guidance, 20% concerned questions about general information, and 20% were requests for the bot to produce or help with writing.

India is OpenAI’s second-largest market with more than 100 million weekly users, and the company has been actively trying to court Indians for its AI tools and services. The company offers a sub-$5 subscription tier in the country, and last year even ran promotional campaigns to spur adoption.

“AI adoption is moving faster than our ability to measure it – and that’s a challenge for anyone trying to make smart decisions. Signals is our way of putting real-world evidence on the table, so India’s AI debate can be grounded in facts, not hype,” OpenAI’s chief economist Ronnie Chatterji said in a statement.

Source: TechCrunch

Understanding verification vs validation is essential for ensuring software quality throughout the development lifecycle. While both aim to reduce defects, they focus on different aspects of the product.

Verification ensures the product is being built correctly according to specifications. It answers the question: Are we building the product right? Typical activities include:

Reviewing requirements and design documents

Code inspections and walkthroughs

Static analysis of deliverables

Validation ensures the final product meets user needs and expectations. It answers the question: Are we building the right product? Common activities include:

Functional and system testing

User acceptance testing (UAT)

Real-world scenario testing

In short, verification focuses on correctness against specifications, while validation focuses on actual usability and business alignment. Both are crucial for delivering reliable, high-quality software.

For software businesses that suffer from free trial abuse, stripe is introducing a new API which auto predicts if your users are fraud-y or otherwise low likelihood to convert to a paid plan.

Source: Jefff Weinstein (Product @ stripe)

Reach out at jweinstein[at]stripe[dot]com

At the India AI Impact Summit 2026, Chairman & Managing Director of Reliance Industries Limited, Mukesh Ambani, says,

"Today, on behalf of the Reliance Group and Jio Intelligence, I want to make three announcements.

• Announcement one, Jio connected India to the internet era. Jio will now connect India to the intelligence era. We will deliver intelligence to every citizen, every sector of the economy, and every facet of social development and every service of government. Jio will do so with the same reliability, quality, scale, and extreme affordability that transformed connectivity. India cannot afford to rent intelligence. Therefore, we will reduce the cost of intelligence as dramatically as we did the cost of data...
• Announcement 2, Jio, together with Reliance, will invest 10 lakh crores over the next seven years, starting this year...
• Announcement 3, Jio Intelligence will build India's sovereign compute infrastructure through three bold initiatives. One, gigawatt-scale data centers. We already started construction on multi-gigawatt AI-ready data centers at Jamnagar.

Over 120 megawatts will come online in the second half of 2026 this year and a clear path to gigawatt-scale compute for training and large scale inference. Two, our green energy advantage. We have an in-house energy advantage with up to 10 gigawatts of ready green power surplus anchored by solar in both Kutch and Andhra Pradesh. Three, a nationwide edge compute, an edge compute layer deeply integrated with Jio's network will make intelligence responsive, low latency, and affordable close to where Indians live, learn, and work..."

Source: ANI

Designed and built in India, fitted with Sarvam AI.

You wear it. It listens, understands, responds, and captures what you see. And you can build custom experiences for it with the Sarvam platform.

Razorpay on February 17 announced a strategic partnership with global artificial intelligence coding firm Replit to power payments for Indian users and enable developers to monetize AI-built applications using local payment methods.

Developers can integrate UPI and card payments into Replit apps. Integration allows seamless monetisation for Indian AI builders.

The partnership aims to address this gap by enabling seamless rupee payments for subscriptions and embedding local payment methods into AI-built applications.

“What’s exciting about Razorpay is how forward-looking they are with AI. They already have a product around agentic payments,” Replit chief executive officer Amjad Masad.

“Developers are increasingly building conversational AI and they should be able to integrate payments in a much more seamless way. Payments are going to be deeply integrated into the agentic AI layer.”

Source: Money Control

If you’re building anything in 2026 and you’re not active on LinkedIn, you’re probably leaving leverage on the table.

For founders especially - LinkedIn isn’t just social media anymore. It’s distribution. It’s hiring. It’s inbound leads. It’s credibility.

But here’s the uncomfortable truth:

Being consistent on LinkedIn is exhausting.

You need ideas.

You need hooks.

You need to comment strategically.

You need to reply fast.

And you need to do it while running an actual company.

So I spent time testing a few SaaS tools that promise to make LinkedIn easier. Here’s what I actually think.

1️⃣ 2PR.io - Surprisingly Good If You Hate Writing

I’ll start with the most interesting one.

2PR.io turns your spoken thoughts into LinkedIn posts. You basically talk, it structures your ideas, and gives you something publish-ready.

At first, I thought it would seem like a publicity stunt. But it's not.

What I liked:

• The articles didn't feel too "AI-polished" to me. (I think there was too much of a good thing.)

• The narrative tone is quite well-preserved.

• The speed is amazing-incredibly so.

For those of us who express ourselves more easily verbally (and most of us do), this approach creates fewer obstacles because it eliminates the need to sit and work on developing hooks for 30 minutes before starting. You express your thoughts and refine them.

It won’t magically make your ideas better - but it will get them out of your head and onto LinkedIn.

If you’re the type who says “I have ideas but I don’t have time to write,” this is probably your tool.

2️⃣ ContentIn - For When You Need Structure

ContentIn feels more like a system than a generator.

It’s clearly designed for solopreneurs and small teams doing their own marketing. You would receive:

• A Framework for planning out content

• Ghostwriting via AI, trained to write like you

• Scheduling

• Management of Posts

This is not about creating a single viral post, but rather, developing content consistency across months.

While using AI is valuable, it does not provide all the value; the real value is in having a structure to your content. By removing the daily burden of "what do I post today", you will find it is much easier to keep creating content over time.

If you are committing to building a personal brand and creating content for an extended period, having a consistent system for creating content is much more important than a single viral post.

3️⃣ Linkmate - The Engagement Multiplier

Most founders obsess over posting.

Few obsess over commenting.

That’s a mistake.

Linkmate focuses entirely on engagement - commenting on relevant posts based on keywords or specific profiles. You can customize tone, control prompts, even automate it.

Now, let’s be clear: if you abuse automation on LinkedIn, people can feel it.

But used carefully? It keeps you visible in conversations you’d otherwise miss.

If your strategy is relationship-driven growth - partnerships, investor visibility, ecosystem presence - consistent commenting is powerful. Linkmate just makes it scalable.

4️⃣ Saywhat - The “All-In” Play

**Saywhat**feels more like a LinkedIn growth ecosystem than a single-purpose tool.

You get all these features together:

• Writing with AI

• Discovering ideas based on successful posts

• Managing comments

• Having analytics

• Being part of a community with like-minded people.

Not many other people realize this, but it is clear that people have faster success when they have other people to grow with!

If you do not post on a regular basis, all these features may be more than what you need. However, if you plan to use LinkedIn as a major way to grow your business, having all the features combined makes sense to streamline your process.

So… Are These Tools Worth It?

Here’s my honest take.

None of these tools will fix weak thinking.

If your insights are generic, AI will just make them sound polished and generic.

But if you already have experience, perspective, or lessons to share - these tools reduce friction. They help with consistency. They remove the blank page problem. They make engagement more systematic.

And for founders, friction is the real enemy.

The real advantage isn’t automation.

It’s staying visible long enough for compounding to kick in.

If LinkedIn is part of your growth strategy, I’d experiment with one of these - not to replace your voice, but to protect your time.

Because attention compounds.

And consistency wins.

If you’re building in public - I’m curious: what’s actually working for you on LinkedIn right now?

No matter how advanced tools or automation frameworks become, understanding software testing basics is still critical for delivering reliable software. These fundamentals give testers and developers a clear framework for identifying defects, validating requirements, and ensuring quality across every release.

Key areas of software testing basics include:

• Requirement Analysis: Understanding what the software is supposed to achieve
• Test Case Design: Writing clear, structured, and repeatable tests
• Testing Levels: Unit, integration, system, and user acceptance testing
• Testing Types: Functional, regression, performance, security, and usability testing
• Defect Management: Logging, tracking, and resolving issues effectively

Focusing on these basics helps teams catch issues early, communicate clearly between QA and development, and build a strong foundation for automation and advanced testing strategies. Mastering software testing basics ensures that every release is more reliable, maintainable, and aligned with user needs.

Peter Steinberger, who created the AI personal assistant, previously ClawdBot, then MoltBot and now known as OpenClaw, has joined OpenAI.

OpenAI CEO Sam Altman posted on X that in his new role, Steinberger will “drive the next generation of personal agents.” As for OpenClaw, Altman said it will “live in a foundation as an open source project that OpenAI will continue to support”

Source: Peter Steinberger

India is hosting its first major AI event — the India AI Impact Summit 2026 — in New Delhi between February 16 and 20, bringing together global technology leaders, including Open AI CEO Sam Altman, Nvidia CEO Jensen Huang, Google CEO Sundar Pichai, Bill Gates, and Anthropic CEO Dario Amodei alongside key Indian business figures like Reliance Industries chairman Mukesh Ambani, Nandan Nilekani, and more, as per the summit’s website.

The Indian government is hopeful that the upcoming summit will cement India’s status as a destination for large-scale AI investment. The country’s IT minister said in a recent interview that the event could help attract as much as $100 billion in investment. The federal government is also pushing domestic startups to build smaller models for local use cases, eventually reducing reliance on U.S.-based systems.

Baseline testing is a testing practice that establishes a stable reference version of an application against which future changes can be evaluated. This baseline represents an accepted level of functionality, performance, and reliability that the system must continue to meet as it evolves.

By comparing new test results with the baseline, teams can quickly identify regressions, performance drops, or unexpected behavior introduced by recent changes. This makes baseline testing especially valuable in environments with frequent releases or continuous integration, where changes are introduced regularly.

Baseline testing is often used alongside regression and performance testing to provide meaningful context for test results. When the baseline is updated thoughtfully after major improvements or releases, it helps teams track quality trends over time.

Overall, baseline testing supports data driven decisions, reduces risk during releases, and ensures that progress does not come at the cost of stability or user experience.

• AI companies like OpenAI and Anthropic are hiring social media creators to post sponsored content on apps like Facebook, Instagram, YouTube and LinkedIn.
• Companies including Microsoft and Google have paid creators between $400,000 and $600,000 for long-term partnerships spanning several months, CNBC has learned.
• AI companies have increased advertising considerably, spending more than $1 billion on digital ads in the U.S. in 2025, according to Sensor Tower, up 126% from 2024.

Source: CNBC

Test automation is a core practice in modern software development that uses tools and scripts to execute tests automatically. It helps teams validate functionality, integrations, and regressions efficiently, reducing the time and effort required for repetitive manual testing.

By integrating test automation into CI and CD pipelines, teams can run tests on every code change and receive immediate feedback. This early detection of defects shortens development cycles and improves overall product stability. Test automation also supports consistent execution, ensuring the same test scenarios are validated across different environments.

Effective test automation focuses on automating stable, high value test cases while leaving exploratory and usability testing to manual efforts. Well designed automation frameworks and regular maintenance are essential to keep test suites reliable and scalable over time.

When applied strategically, test automation improves testing efficiency, increases release confidence, and enables teams to deliver high quality software at speed.