India’s ed-tech unicorn PhysicsWallah is using OpenAI’s GPT-4o to make education accessible to millions of students in India. Recently, the company launched a suite of AI products to ensure that students in Tier 2 & 3 cities can access high-quality education without depending solely on their enrolled institutions, as 85% of their enrollment comes from these areas.

Last year, AIM broke the news of PhysicsWallah introducing ‘Alakh AI’, its suite of generative AI tools, which was eventually launched at the end of December 2023. It quickly gained traction, amassing over 1.5 million users within two months of its release.

The suite comes with several products including AI Guru, Sahayak, and NCERT Pitara. “AI Guru is a 24/7 companion available to students, who can use it to ask about anything related to their academics, non-academic support, or more,” said Vineet Govil, CTPO of PhysicsWallah, in an exclusive interview with AIM.

He added that the tool is designed to assist students by acting as a tutor, helping with coursework, and providing personalised learning experiences. It also supports teachers by handling administrative tasks, allowing them to focus more on direct student interaction.

Govil further explained that students can ask questions in any form—voice or image—using a simple chat format. “It’s a multimodal.”  He said that even if the lecture videos are long—about 30 minutes, 1 hour, or 2 hours—the AI tool will be able to identify the exact timestamp of the student’s query.

When discussing Sahayak, he explained that it offers adaptive practice, revision tools, and backlog clearance, enabling students to focus on specific subjects and chapters for a tailored learning experience.

“Think of Sahayak as a helper that assists students in creating study plans. Based on the student’s academic profile and the entrance exam they are preparing for, it offers suggestions on a possible plan to follow. It includes short and long videos, and a question bank,” said Govil.

On the other hand, NCERT Pitara uses generative AI to create questions from NCERT textbooks, including single choice, multiple choice, and fill-in-the-blank questions.

Moreover, PhysicsWallah has introduced a ‘Doubt Engine’ which can solve students’ doubts after class hours. These doubts can be either academic or non-academic. 

“Academic doubts can be further divided into contextual and non-contextual. Contextual doubts are those that our system can understand, analyse, and respond to effectively. Non-contextual doubts are the ones where we are uncertain about the student’s thought process,” explained Govil.

He said that with the help of the slides that the teacher uses to teach and the lecture videos, their model is also able to answer non-contextual doubts. “Today, 85% of the doubts are solved in real time. Previously, it used to take 10 hours for doubts to be resolved by human subject-matter experts.”

The company has also launched an AI Grader for UPSC and CA aspirants who write subjective answers. Govil said that grading these answers is challenging due to the varying handwriting styles, but the company has successfully developed a tool to address this issue.

“Over a few months, we have done a lot of fine-tuning. Today, we are able to understand what a student is writing. At the same time, some students may use diagrams, and we are able to identify those as well,” said Govil.

The Underlying Tech

Govil said that they use OpenAI’s GPT-4o. Regarding the fine-tuning of the model, he said the company has nearly a million questions in their question bank. “We have over 20,000 videos in our repository that are being actively used as data,” he added.

On the technology front, he said that the company has developed its own layer using the RAG architecture. “And we have a vector database that allows us to provide responses based on our own context,” he said.

PhysicsWallah built a multimodal AI bot powered by Astra DB Vector and LangChain in just 55 days. 

Talking about the data resources for RAG, Govil said, “Our subject matter experts (SMEs) regularly update the data, including real-time current affairs and question banks. This continuous updating has helped us build a question bank with over a million entries.”

When asked about LLMs not being good at maths, Govil agreed and said “It’s a known problem that all the LLMs available today are not doing a great job when it comes to reasoning, and we are aware of it.”

“We are working with partners leading in the LLM space. At the same time, this is really an issue only for high-end applications. For day-to-day algebra and mathematical operations, they are performing well,” he added. 

Alakh AI is Not Alone

OpenAI former co-founder Andrej Karpathy recently launched his own AI startup, Eureka Labs, an AI-native ed-tech company. Meanwhile, Khan Academy, in partnership with OpenAI, has developed an AI-powered teaching assistant called Khanmigo, which utilises OpenAI’s GPT-4. 

Speaking of its global competitors, Govil said, “I won’t really like to compare [ourselves] with the others, but I can tell you that the kind of models we have, and the kind of responses and the skill at which we are operating, are not seen elsewhere.”

Moreover, recent reports indicate that Lightspeed Venture Partners will lead a $150 million funding round for PhysicsWallah at a valuation of $2.6 billion. 

In conclusion, PhysicsWallah’s innovative suite of tools under the Alakh AI umbrella, which includes Sahayak, AI Guru, and the Doubt Engine, is set to reshape the ed-tech industry with its advanced features and real-time capabilities.

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Organisations often struggle with data silos and inefficiencies when implementing generative AI solutions. This affects over 70% of enterprises today, but global software company Pegasystems, aka Pega, seems to have cracked the code by using its patented ‘situational layer cake’ architecture. 

This approach democratises the use of generative AI across its platform, allowing clients to seamlessly integrate AI into their processes. They can choose from any LLM service provider, including OpenAI, Google’s Vertex AI, and Azure OpenAI Services, thereby ensuring consistent and efficient AI deployment across all business units.

“Our GenAI implementation at the rule type levels allows us to democratise the use of LLMs across the platform for any use case and by mere configuration, our clients can use any LLM service provider of their choice,” said Deepak Visweswaraiah, vice president, platform engineering and side managing director at Pegasystems, in an interaction with AIM. 

Pega vs the World 

Recently, Salesforce announced the launch of two new generative AI agents, Einstein SDR Agent and Einstein Sales Coach Agent, which autonomously engage leads and provide personalised coaching. This move aligns with Salesforce’s strategy to integrate AI into its Einstein 1 Agentforce Platform, enabling companies like Accenture to scale deal management and focus on complex sales.

Salesforce integrates AI across all key offerings through its unified Einstein 1 Platform, which enhances data privacy, security, and operational efficiency via the Einstein Trust Layer. 

“We have generative AI capabilities in sales cloud, service cloud, marketing cloud, commerce cloud, as well as our data cloud product, making it a comprehensive solution for enterprise needs,” said Sridhar H, senior director of solution engineering at Salesforce.

SAP’s generative AI strategy, on the other hand, centres around integrating AI into core business processes through strategic partnerships, ethical AI principles, and enhancing its Business Technology Platform (BTP) to drive relevance, reliability, and responsible AI use across industries.

“We are adding a generative AI layer to our Business Technology Platform to address data protection concerns and enhance data security,” stated Sindhu Gangadharan, senior VP and MD of SAP Labs, underscoring the company’s focus on integrating AI with a strong emphasis on security and business process improvement.

Oracle, on the other hand, focuses on leveraging its second-generation cloud infrastructure, Oracle Cloud Infrastructure (OCI). It is designed with a unique, non-blocking network architecture to support AI workloads with enhanced data privacy while extending its data capabilities across multiple cloud providers.

“We’re helping customers do training inference and RAG in isolation and privacy so that you can now bring corporate sensitive, private data…without impacting any privacy issue,” said Christopher G Chelliah, senior vice president, technology & customer strategy, JAPAC at Oracle.

Meanwhile, IBM has watsonx.ai, an AI and data platform designed to help companies integrate, train, and deploy AI models across various business applications.

IBM’s generative AI strategy with watsonx.ai differentiates itself by offering extensive model flexibility, including IBM-developed (Granite), open-source (Llama 3 and alike), and third-party models, along with robust client protection and hybrid multi-cloud deployment options. At the same time, Pega focuses on deeply integrating AI within its platform to streamline business processes and eliminate data silos through its unique situational layer cake architecture.

Pega told AIM that it distinguishes itself from its competitors by avoiding the limitations of the traditional technological approaches, which often lead to redundant implementations and data silos. “In contrast, competitors might also focus more on channel-specific designs or product-centric implementations, which can lead to inefficiencies and fragmented data views across systems,” said Visweswaraiah. 

Situational Layer Cake Architecture 

Pega told AIM that its approach to integrating GenAI processes into business operations is distinct due to its focus on augmenting business logic and decision engines rather than generating code for development. 

It employs the situational layer cake architecture, which as a part of Pega’s exclusive centre-out architecture, helps to adapt microjourneys for different customer types, lines of business, geographies, and more. 

“Our patented situational layer cake architecture works in layers making specialising a cinch, differentiating doable, and applying robust applications to any situation at any time, at any scale,” said  Visweswaraiah.

He added that enterprises can start with small, quick projects that can grow and expand over time, ensuring they are adaptable and ready for future challenges.

In addition to this, the team said it has the ‘Pega Infinity’ platform, which can mirror any organisation’s business by capturing the critical business dimensions within its patented situational layer cake. 

“Everything we build in the Pega platform, processes, rules, data models, and UI is organised into layers within the situational layer cake. This means that you can roll out new products, regions, or channels without copying or rewriting your application,” shared  Visweswaraiah. 

He further said that the situational layer cake lets you declare what is different and only what is different into layers that match each dimension of your business. 

Simply put, when a user executes the application, the Pega platform slices through the situational layer cake and automatically assembles an experience that is tailored exactly to that user’s context. 

Visweswaraiah believes that this architecture has given them a great opportunity to integrate GenAI into the platform at the right layers so it is available across the platform. 

The post Channel-Specific and Product-Centric GenAI Implementation in Enterprises Leads to Data Silos and Inefficiencies appeared first on AIM.

Anthropic recently made Claude Artifacts available to all users on iOS and Android, allowing anyone to easily create apps without writing a single line of code. AIM tried its hand at it and successfully created a Cricket Quiz game, Temple Run, and Flappy Bird, all with a single line of prompt in English. 

Debarghya (Deedy) Das, principal at Menlo Ventures, used Artifacts to build a Splitwise-like app. “With Claude launching on iOS today, I can now generate the Splitwise app instead of paying for Pro,” he said. 

“Claude Artifacts allows you to go from English to an entire app and share it!” he added, saying that his friend, a product manager who couldn’t code, now creates apps in minutes. “The cost of a lot of software is nearing ~$0.”

This brings us to question if this could be the end of App Stores. Groq’s Sunny Madra thinks this is the beginning of “The Build Your Own (BYO) era. Since Artifacts are shareable, anyone can use the apps you build, and they can be shared on any social media platform as a link.

Several users experimented with Claude Artifacts by building different apps. 

“Claude 3.5’s artifacts, now shareable, can help teach. In class, startup financing can be hard to explain. Now I just asked, “Create an interactive simulation that visually explains payoff differences for a startup and VC with liquidation preference…” Ethan Mollick, associate professor at the Wharton School of the University of Pennsylvania, wrote on X. 

Similarly, Allie K Miller, AI advisor and angel investor, used it to build a calendar and an AI quiz, which took less than two minutes! 

The best part about Artifacts is that it is mobile-friendly and responsive. “Using Claude 3.5 Sonnet, you can generate artifacts (e.g., code snippets, text documents, or website designs) and iterate on them right within the same window,” exclaimed Elvis Saravia, the co-founder of DAIR.AI. 

On-Demand Software

When using mobile phones, we often search for apps that can solve our specific needs. For example, if you’re into fitness, you might download an app that offers various workouts. However, the app may not provide the customisation you seek. Now, instead of relying on downloads, you can create your own personalised apps that cater specifically to your needs. 

“On demand software is here,” said Joshua Kelly, chief technology officer, Flexpa, a healthcare tool company. Using Artifacts, he built a simple stretching time app for his runs in just 60 seconds.

Other than just giving prompts, users can now also share previously made websites or apps, and Claude can generate an exact replica. 

“You can now take a photo of something you want to replicate, give it to AI, and it outputs the code with a preview right on your iPhone,” posted Linas Beliūnas, director of revenue at Zero Hash, on LinkedIn.

On the internet, one can find several apps built using Claude Artifacts, such as the Rubik’s Cube Simulator, Self-Playing Snake Game, Reddit Thread Analyzer, Drum Pad, and Daily Calorie Expenditure.

Apart from building apps, Artifacts has the potential to greatly impact education. “Any piece of content— whether it’s a screenshot, PDF, presentation, or something else—can now be turned into an interactive learning game,” said AI influencer Rowan Cheung.

The End of No-Code Platforms?

Claude Artifacts is going to be a big threat to no-code and low-code app builder platforms such as AppMySite, Builder.ai, Flutter, and React Native. 

“Claude Artifacts are insane — I cannot believe how good the product is. You can ask it to build most little internal tools in minutes (at least, the UI) and customize further via code. Feels like a superpower for semi-technical people,” posted a user on X. 

Moreover, Claude, when put together with Cursor AI, has simplified the process of making apps. “So I’m building this box office app in React Native and I thought I’d try Cursor with Claude 3.5 and see how far I’d get. The backend is django/psql that’s already in place,” said another user on X. “Starting from scratch, I have authenticated with my server to log in users, issue tickets, register tickets, scan ticket QR codes, and send email/sms confirmations,” he added. 

Claude is set to rapidly democratise app development, potentially eliminating the need for an App Store. It will enable anyone to build apps based on their specific needs, complete with personalised UI and UX.

Moreover, building an app for the iOS App Store is challenging. Apple charges a standard 30% commission on app sales and in-app purchases, including both paid app downloads and digital goods sold within the apps. 

The company enforces rigorous guidelines that apps must adhere to, covering aspects such as user interface design, functionality, and privacy. Many apps are rejected for minor violations, and these guidelines are frequently updated, requiring developers to stay informed and adapt quickly.

However, for now, Claude allows anyone to build anything without any charges and lets users experiment to see if something is working or not. Even if someone wants to publish an app built using Claude on the iOS App Store, that is definitely an option.

Interestingly, Apple recently announced that, for the first time, it will allow third-party app stores on iOS devices in the EU. This change enables users to download apps from sources other than Apple’s official App Store, providing more options for app distribution and potentially reducing costs for developers.

Better than ChatGPT 

OpenAI previously introduced ChatGPT plugins, enabling users to create custom GPTs for their specific tasks. However, these plugins do not compare to Artifacts, which allows users to visualise their creations. 

While the Plugins Store was billed as an ‘iOS App Store moment’, it failed to meet the expectations and ended up being a hot mess. 

Moreover, during DevDay 2023, OpenAI chief Sam Altman launched a revenue-sharing programme which was introduced to compensate the creators of custom GPTs based on user engagement with their models. 

However, many details about the revenue-sharing mechanism remain unclear, including the specific criteria for payments and how the engagement would be measured.

“It was supposed to be announced sometime in Q1 2024, but now it’s the end of March, and there are still few details about it,” posted a user on the OpenAI Developer Forum in March. There have been no updates on the matter from OpenAI since then.

The post Anthropic Claude Artifacts to Kill App Store Soon  appeared first on AIM.

“Programming is no longer hard” or “everyone’s a developer” are the most common phrases that one would hear on LinkedIn or X as everyone is basically talking about Cursor, Claude, or GitHub Copilot. But the problem is that most of the people who claim so are not developers themselves. They are merely ‘modern developers.’

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Santiago Valdarrama, founder of Tideily and an ML teacher, who has been actively asking developers if they are using Cursor or not, started another discussion that tools such as Cursor and others are basically tools that can only assist existing developers in writing better code. “Wake me up when companies start hiring these clueless modern ‘developers’,” he added.

He gave an analogy of calling yourself an F1 racer after playing a racing game on an iPad.

In all honesty, it is undeniable that the barrier to entry for becoming a developer has dropped significantly ever since Cursor, even ChatGPT, dropped. People have been able to build software for their personal use and even build apps in mere hours. But, this does not eliminate the fact that it is currently only limited to just creating such apps and low level software.

“You Can’t Correct Code if You Don’t Know How to Code”

Given all this hype around the end of software engineering roles, developers and programmers are getting worried about the future of their jobs. It is indeed true that software engineers have to upskill faster than anyone else, but the fear of getting replaced can be pushed off to at least a few years.

Having tools such as Cursor and Claude are only good enough if a developer actually knows how the code actually works. The real game-changer is how developers who use AI will outpace those who don’t. “The right tools can turn a good developer into a great one. It’s not about replacing talent; it’s about enhancing it,” said Eswar Bhageerath, SWE at Microsoft. 

AI only takes care of the easy part for a developer – writing code. The real skill that a developer has is reasoning and problem solving, apart from fixing the bugs in the code itself, which cannot be replaced by any AI tool, at least anytime soon. Cursor can only speed up the process and write the code but correcting the code is something that only developers can do.

Moreover, bugs generated within code with AI tools are not easily traceable by developers without using any other AI bug detection tool. Andrej Karpathy, who has been actively supporting Cursor AI over GitHub Copilot, also shared a similar thing while working. “it’s slightly too convenient to just have it do things and move on when it seems to work.” This has also led to the introduction of a few bugs when he is coding too fast and tapping through big chunks of code.

These bugs cannot be fixed by modern ‘developers’ who were famously also called ‘prompt engineers’. To put it simply, someone has to code the code for no-code software.

Speaking of prompt engineers, the future will include a lot of AI agents that would be able to write code themselves. The future jobs of software engineers would be managing a team of these AI coding agents, which is not possible for developers who just got into the field by just learning to build apps on Cursor or Claude. It is possible that the size of the teams might decrease soon as there would be no need for low level developers.

Upskilling is the Need of the Hour

That is why existing developers should focus on developing engineering skills, and not just coding skills. Eric Gregori, adjunct professor at Southern New Hampshire University, said that this is why he has been teaching his students to focus more on engineering than just programming. “AI is too powerful of a tool to ignore,” he said, while adding that existing limitations of coding platforms have been removed completely. 

“Hopefully, AI will allow software engineers to spend more time engineering and less time programming.” It is time to bring back the old way of learning how to code as modern developers would be tempted to just copy and paste code from AI tools, and not do the real thinking. 

The F1 driver analogy fits perfectly here. Most people can learn how to drive, but would never be able to become a race driver. The same is the case with the coding tools. But if all people need is prototyping and designing an initial code, AI driven developers would be able to do a decent enough job.

That is why a lot of pioneers of the AI field such as Karpathy, Yann LeCun, Francois Chollet, even Sam Altman, say that there would be 10 million coding jobs in the future, the ones that would require the skills of Python, C++, and others. As everyone in some way would be a ‘modern developer’, and most of the coding would be done by AI agents. 

It is possible that most of the coding in the future would be in English, but most of it would be about debugging and managing the code generated by AI, which is not possible for someone who does not know coding from scratch.

The post Wake Me Up When Companies Start Hiring Clueless Modern ‘Developers’ appeared first on AIM.

Last week, in a one of a kind effort, Open AI signed an MOU with the US Artificial Intelligence Safety Institute (US AISI), part of the larger US Department of Commerce’s National Institute of Standards and Technology. In this juncture of AI’s revolution,  this collaboration is aimed at furthering OpenAI’s commitment for safety, transparency and human centric innovation – by building a framework that the world can contribute to. This would enable the US AI Safety Institute to get early access to test and evaluate future models prior to its public release. Anthropic has also agreed to sign this partnership. 

Sam Altman, CEO of OpenAI, took to X (formerly Twitter), to underscore the significance of this partnership. “We are happy to have reached an agreement with the US AI Safety Institute for pre-rlease testing of our future models,” said Altman, saying that this is important, and suggested for this to happen at a national level. “US needs to continue to lead!”

But Why US AI Safety Institute? 

Elizabeth Kelly, director of the US AI Safety Institute, has been a strong proponent of safety in AI innovation and has brokered many such strategic partnerships in the past. “With these agreements in place, we look forward to beginning our technical collaborations with Anthropic and OpenAI to advance the science of AI safety,” she said in a statement. 

The US AI Safety Institute was born in 2023, under the Biden-Harris administration, to help develop testing guidelines for safe AI innovation in the US. 

“Safety promotes trust, which promotes adoption, which drives innovation, and that’s what we are trying to promote at the US AI Safety Institute.” she said in another interview, highlighting the role of this institute in the coming future. 

Through initiatives like this, the US could lead the way for more voluntary AI safety adoption. Anthropic, OpenAI rival, has previously collaborated with government bodies – like the UK’s Artificial Intelligence Safety Institute (AISI) – to conduct pre-deployment testing of their models. It would be interesting to see if OpenAI also partners with them too.  

Why it matters? 

The need for the US AI Safety Institute (US AISI) arises from concerns about the impact of poorly managed AI systems on democracy, as highlighted by Dario Amodei, CEO of Anthropic. 

Amodei said that AI must be aligned with human values and ethics to support democratic institutions effectively. The collaboration between Anthropic, OpenAI, and the US AISI is a response to the growing power of AI, which, if left unchecked, could exceed that of national governments and economies. This partnership aims to establish safety standards, conduct pre-deployment testing, and regulate AI to prevent misuse, particularly in politically sensitive areas such as elections.

“I think it’s just really important that we provide these services well. It makes democracy as a whole more effective, and if we provide them poorly, it undermines the notion of democracy,” said Amodei. 

US vs China vs India 

The US’s push for AI safety leadership with OpenAI and Anthropic aims to counter China’s rapid AI advancements and maintain global dominance in ethical AI governance. 

At the same time, there are concerns around China winning the AI race due to its efficient state control, outpacing US efforts hindered by political gridlock. “China is probably going to win the AI game, as their state control is much more efficient than corrupt US politicians,” said Pratik Desai, saying that he wants US and freedom to win. “I just don’t trust the current bunch of politicians.” 

China’s dominance in several AI and technological is quite evident with them leading in “visual AI and LLM field as they have the best state-operated surveillance system,” added Desai. 

On the bright side, standardised scrutiny could promote a more democratic approach to developing models, which is perhaps lacking in economies like China. More countries are slowly realising the importance of AI institutes; and the need for investing in AI safety as much as they do on AI development. 

It’s high time India also considered establishing an AI Safety Institute, akin to those in the UK and US, to responsibly manage the rapid growth of AI technologies. “We need an AI Safety Institute here and now” said former director-general of CSIR, Raghunath Mashelkar, in order to maximise the benefits and minimise the risks associated with AI in the world’s most populous nation.

Former Union Minister of India, Rajeev Chandrasekhar, also underscored the critical need for democracies and their allied nations to shape the future of technology, particularly in light of concerns raised by Paul Buchheit, creator of Gmail, about the potential dangers of AI development led by China, which could lead to global surveillance and censorship. 
“It’s extremely important — more than critical — that the future of tech is shaped by democracies and their partner countries,” said Chandrasekhar.

The post US Leads AI Safety with OpenAI, Anthropic Joining National AI Institute appeared first on AIM.

NVIDIA is reportedly in discussions to join a funding round for OpenAI that could value the AI startup at more than $100 billion, according to the Wall Street Journal. In May 2024, OpenAI was valued at approximately $86 billion.

This news comes on the heels of NVIDIA’s impressive July quarter results, where revenues surpassed $30 billion—a 122% increase from the previous year. 

Besides NVIDIA, Apple and Microsoft are also considering participating in the financing. Thrive Capital is reportedly leading the round with a $1 billion investment, while NVIDIA is evaluating a potential contribution of around $100 million, added the report. Notably, Microsoft has invested $13 billion in OpenAI overall.

While OpenAI is dependent on the NVIDIA GPUs to train its upcoming frontier model, Apple recently partnered with the company, integrating ChatGPT into Siri. 

On Thursday, it was reported that ChatGPT has surpassed 200 million weekly active users, doubling its count from the previous year. 

Surprisingly, this year, OpenAI has released only GPT-40 and GPT-40 Mini. However, the company has announced several other products, including Sora, SearchGPT, Voice Engine, GPT-40 Voice, and most recently, Strawberry and Orion. It seems that the announcements were likely intended to generate hype and raise funds.

NVIDIA is Investing in Itself, Not OpenAI 

Following the investment, the money will ultimately flow back to NVIDIA as OpenAI purchases more compute resources to train its next frontier model. 

NVIDIA is keen to secure its ecosystem for the year ahead and is now concentrating on its Blackwell GPUs. This lineup includes models B100 and B200, built for data centres and AI applications.

NVIDIA chief Jensen Huang said that the Blackwell is expected to come out by the fourth quarter this year. “We’re sampling functional samples of Blackwell, Grace Blackwell, and a variety of system configurations as we speak. There are something like 100 different types of Blackwell-based systems that were shown at Computex, and we’re enabling our ecosystem to start sampling those,” said Huang. 

However, previous reports indicated that these could be delayed by three months or more for Blackwell due to design flaws, a setback that could affect customers such as Meta Platforms, Google, and Microsoft, which have collectively ordered tens of billions of dollars’ worth of these chips.

Huang believes this is just the beginning and that there’s much more to come in generative AI. “Chatbots, coding AIs, and image generators are growing rapidly, but they’re just the tip of the iceberg. Internet services are deploying generative AI for large-scale recommenders, ad targeting, and search systems,” he said.

According to the NVIDIA CFO Colette Kress the next-generation models will require 10 to 20 times more compute to train with significantly more data. 

Earlier this year, Huang personally hand-delivered the first NVIDIA DGX H200 to OpenAI. 

OpenAI’s GPT-40 voice features, demonstrated during the Spring Update event, were made possible with the help of NVIDIA H200. “I just want to thank the incredible OpenAI team, and a special thanks to Jensen and the NVIDIA team for bringing us the advanced GPU that made this demo possible today,” said OpenAI CTO Mira Murati, during the OpenAI’s Spring Update.

Apple Wants a Slice of OpenAI 

Apple is catching up in the AI race. The company recently released iOS 18.1 beta 3, introducing the AI-powered Clean Up tool under Apple Intelligence, which removes unwanted objects from photos to enhance image quality. 

This feature of Apple Intelligence is based on the 3 billion parameter model, which Apple developed recently. 

While Apple Intelligence is perfect for day to day tasks, it is not focusing on better reasoning capabilities which will be required in the near future. This is where OpenAI comes into the picture. 

“This is a sign that Apple is not seeing a path where it makes sense to build a competitive, full feature LLM,” said Gene Munster, Managing Partner, Deepwater Asset Management on Apple’s investment in OpenAI. 

He added that this means Apple will be reliant on OpenAI, Google, and possibly even Meta to deliver about a third of their AI features in the long term.

OpenAI chief Altman is a huge fan of Apple, and his startup eventually ended up partnering with the company. He recently lauded the Cupertino-based tech giant for its technology prowess, saying, “iPhone is the greatest piece of technology humanity has ever made”, and it’s tough to get beyond it as “the bar is quite high.” 

As a part of Apple-OpenAI partnership,  iOS, iPadOS, and macOS users would get access to ChatGPT powered by GPT-4o later this year, where users can access it for free without creating an account, and ChatGPT subscribers can connect their accounts and access paid features right from these experiences.

Interestingly, when OpenAI announced the ChatGPT desktop app, it was first released for Mac users rather than for Microsoft.

Moreover, it was said that the company wasn’t paying OpenAI anything, as it was doing the startup a favour by making ChatGPT available to billions of customers. 

However, investing in OpenAI today would be a smart move for Apple, as it would provide access to the latest OpenAI models, similar to how Microsoft’s AI services primarily rely on OpenAI.

Meanwhile, OpenAI definitely has a soft corner for Apple. This affinity was clearly displayed at the OpenAI Spring Update, where MacBooks and iPhones were prominently used, while Microsoft Windows products were notably absent. 

The post NVIDIA, Apple Blow OpenAI’s Bubble appeared first on AIM.

The barrier for entry to become a developer is dropping everyday. The most recent phenomenon that everyone is still talking about is Anysphere’s Cursor AI coding tool, which has basically made everyone a developer. Now, there are more tools coming up in the same category such as Codeium, Magic, and Zed AI, all of them trying to come up with the same formulae. 

This definitely brings in the question – what would happen to the software developers of today? Graduating out of colleges with computer science degrees in a world to compete with the people who are becoming software engineers with AI tools, the turmoil of an average software engineer is real.

The solution is easier said than done – upskill yourselves and focus on higher order things such as building foundational AI. Even 8-year-olds are building apps using Cursor AI in 45 minutes. 

A Profession Like Never Before

Since there is no barrier to entry, no degree requirements, and no regulations about who can join the market, software engineering has become a profession that has never happened ever in the history. There are plenty of opportunities for developers to upskill.

But “upskill to what?” is what people ask. 

The conversation from LLMs to SLMs to coding assistants to AI agents keeps changing so swiftly, it can be challenging to determine which new skills are worth acquiring. This question reflects a broader uncertainty about how to prioritise learning in a field where the next big thing seems just around the corner.

Saket Agrawal, a developer from IIT Guwahati, said that it is not as much about the technological shift but the advancement of automation tools that reduce the time and efforts for the same skills. “I don’t see any big threat to existing software skills suddenly and software has been the field all the time which needs continuous skills updation based on requirement without leaving your old skills instantly,” he said. 

Another user on X put it in a funny way. “Software engineers need more updates than my grandma’s Windows 95. Ever tried explaining AI to her? It’s like defining gluten-free bread to a caveman!”

It is widely discussed that a lot of software engineering jobs are dying. “Winter is coming for software engineering,” said Debarghya Das from Menlo Ventures, saying that many of the current software engineering jobs would become a distant memory. 

Scott Stouffer adds another layer to this conversation by suggesting that some are experiencing an upgrade in their lives at a pace that surpasses others. This notion of being “upgraded” faster could imply a divide between those who adapt quickly to technological advancements and those who struggle to keep up.

LLMs to Upskill?

While there is a very interesting caveat to all of this conversation around upskilling. Hardcore skilled developers believe that leveraging tools such as Cursor and others can take them to another level where the new developers would never be able to reach. Yann LeCun has already told developers getting into the AI field to not work on LLMs.

Andrej Karpathy recently said that the future of coding is ‘tab tab tab’ referring to auto code completion tools such as Cursor. Further in the thread, he added that with the capabilities of LLM shifting so rapidly, it is important for developers to continually adapt the current capabilities.

Some people are sceptical if they even should get into the computer science field anymore. “…if I was new to programming I would be too tempted to skip actual learning in favour of more LLM usage, resulting in many knowledge gaps,” said a user replying to Karpathy. This truly feels like the true way forward for many developers. 

This is similar to what Francois Chollet, the creator of Keras, said a few months ago. “There will be more software engineers (the kind that write code, e.g. Python, C or JavaScript code) in five years than there are today.” He added that the estimated number of professional software engineers today is 26 million, which would jump to 30-35 million in five years.

This is because developers who are proficient with coding without code generators can never be replaced. People who built programming languages and foundational tools are still very well versed with coding than people who are just using Cursor to build apps. Sure, there can be an abundance of people building apps in the future, but the scope would just be limited to that.

Meanwhile, highly skilled 10x developers would be focusing on leveraging such tools, or possibly finding flaws in them, to create even better software. So to say, creating the next Cursor or ChatGPT.

There is an abundance of things that can be done. For instance, focusing on enhancing hardware or building infrastructure for running future workloads can only be comprehended by experts in the field. For example, companies such as Pipeshift AI, Groq, Jarvis Labs, and many others who are working on different problems than coding. 

The truth is that such AI tools can never replace human intelligence or jobs, only augment them. “Generating working code is only a part of the responsibility,” said VJ’s Insights on a post on X. Though “Yes, if you are someone who *just* writes code, you need to start thinking differently.”

In the near future, there are predictions that the future of software engineering would be about managing a team of AI agent engineers, and telling them how to code. This will make every engineer akin to an engineering manager, delegating basic tasks to coding agents while focusing on higher-level aspects such as understanding requirements, architecting systems, and deciding what to build.

It is high time that software engineers start upskilling themselves, and currently, it looks like using generative AI tools is the best way forward, not without them. Who knows, you might also become a solo-entrepreneur building a billion dollar company alone. 

The post Software Engineers Have to Upskill Faster Than Anyone Else appeared first on AIM.

What does it take to qualify as an AI startup? At what stage do they need financial support? And lastly, what should the ideal mode of financing be? These critical topics came up for discussion when government officials recently met with key industry figures.

The focus of the meeting was AI startups in the context of the IndiaAI Mission. 

Notable attendees of the meeting included Google, NVIDIA, and Microsoft, and representatives from AI startups such as Jivi and DronaMaps were also present, the Hindustan Times said.

It’s encouraging to see the government recognise the rapid growth of AI startups across India and acknowledge the significant role they could play in driving the country’s economy in the coming years.

On a recent podcast, Rahul Agarwalla, managing partner at SenseAI Ventures, said he witnessed about 500 new AI startups emerge in the past six months, which is a massive number.

Based on the rate at which AI startups are popping up in the country, Agarwalla believes India could soon have 100 AI unicorns. While it remains to be seen when that happens, the budding AI ecosystem in India will indeed need support from the government beyond regulatory favours.

What Qualifies as an AI Startup?

A key topic discussed at the meeting was the criteria to define an AI startup. 

Attendees highlighted to the government that simply having ‘AI’ in their name does not automatically make a startup an AI-focused company.

In response, stakeholders proposed a rating system, which builds credibility among startups and would in turn make them eligible for government funding. Not everyone will make the cut though. 

Unfortunately, in the startup world, a majority of them do not live long enough to see the light at the end of the tunnel. 

Stakeholders recommend that rather than spreading a small amount of funding thin across many startups, the government should focus on identifying those with significant potential and provide them with targeted financial support.

Earlier, the government had allocated INR 10,372 crore as part of the India AI Mission – a part of which will be used to fund startups.

Should the Government Play the VC?

According to a Tracxn report, Indian AI startups raised $8.2 million in the April-June quarter, while their US counterparts raised $27 billion during the same period.

While not many Indian startups are building LLMs, which cost billions of dollars, the funding for AI startups in India still remains relatively low.

The government, under the IndiaAI Mission, is weighing options to fund AI startups and deciding how best to do so. One bold proposal on the table was taking equity stakes in these emerging companies.

The government had previously suggested taking the equity route as part of the second phase of the designed-linked incentive (DLI) scheme for semiconductor companies. However, the thought was not well received by many in the industry. 

“[I] don’t understand the logic of the government trying to become a venture capital firm for chip design companies. This move is likely to be ineffective and inefficient,” Pranay Kotasthane, a public policy researcher, said back then.

They fear government taking equity could lead to government influence over company operations, and historically, public sector companies in India have often underperformed. Moreover, it could push other venture capitalists away.

Access to Datasets and Compute 

Stakeholders were also quick to point out that more than financing, what the startups need is help in terms of compute. 

According to Abhishek Singh, additional secretary, ministry of electronics and information technology (MeitY), the government plans to disburse INR 5,000 crore of the allocated INR 10,372 crore to procure GPUs.

The government was quick to identify the need for compute, especially for Indian startups, researchers, and other institutions. In fact, last year, the government revealed its intention to build a 25,000 GPU cluster for Indian startups. 

Interestingly, PM Narendra Modi also met Jensen Huang, the CEO of NVIDIA, the company producing the most sought-after GPUs in the market, during his visit to India in 2023.

(Source: NVIDIA)

The Indian Express reported earlier this month that the government had finalised a tender to acquire 1,000 GPUs as part of the IndiaAI Mission. These GPUs will provide computing capacity to Indian startups, researchers, public sector agencies, and other government-approved entities.

Besides access to compute, the stakeholders also urged the government to make the datasets under the IndiaAI Mission available as soon as possible. The datasets will grant startups access to non-personal domain-specific data from government ministries to train models.

Notably, the Bhashini initiative is playing a crucial role in democratising access to Indic language datasets and tools for the Indian ecosystem.

India Startup 2.0 

While the government’s recognition of the funding gap in AI startups and its willingness to provide financial support is encouraging, it is equally important that the government creates a favourable environment for these businesses to thrive.

In line with this, the government launched the Startup India programme in 2016 to foster a robust ecosystem for innovation and entrepreneurship in the country. 

This initiative was designed to drive economic growth and create large-scale employment opportunities by supporting startups through various means. Perhaps, the need of the hour is a similar programme designed specifically for AI startups.

As part of the startup programme, the government identified 92,000 startups and, in addition to funding, provided support such as income tax exemption for three years, credit guarantee schemes, ease of procurement, support for intellectual property protection, and international market access.

Moreover, over 50 regulatory reforms were undertaken by the government since 2016 to enhance the ease of doing business, ease of raising capital, and reduce the compliance burden for the startup ecosystem.

Now, a similar ecosystem needs to emerge for AI startups as well, which fosters innovation, provides essential resources, and facilitates collaboration among researchers, developers, and investors to drive growth and success in the field.

The post India’s AI Startup Boom: Govt Eyes Equity Stakes and GPU Support appeared first on AIM.

According to a recent study, only 2.5% of engineers of India possess any AI skills, with only 5.5% qualified with basic programming knowledge. Though Indian IT companies are looking to change that as they actively upskill their employees, this report did not resonate well with the Indian engineers.

In a recent Reddit discussion, a significant number of Indian engineers expressed concerns over the programming skills of college professors, with many claiming that most professors in Indian colleges lack the necessary expertise to teach programming effectively. 

“Most people learn what college teaches them and nothing more. Most college professors themselves don’t know programming,” said a user in the discussion, which was highly resonated amongst college students.

This discussion highlighted a growing discontent among students and professionals who feel that the quality of education in computer science and related fields is being compromised due to the inadequacies of teaching staff.

One of the key points raised in the discussion was the tendency of professors to rely heavily on external resources like YouTube channels and online courses rather than teaching from their own knowledge and experience. “My college professors used to watch GateSmashers videos for teaching Computer Networks to us. They even wrote the same examples from the video, and when asked questions, they told us to watch the videos.” 

The situation is particularly dire in Tier-3 colleges, where the quality of teaching is perceived to be the lowest. A user said that their professor even copy-pasted the same screenshots from the video in his PowerPoint presentation. “I am not sure for what reason professors take salaries if they can’t even make their own original PPTs,” he added. 

Who to Blame?

Earlier, AIM wrote that there is a dire need for Indian researchers and professors to move beyond PhDs as most of them keep publishing papers. “Though Indian universities produce some very good engineers, they are very successful in the West,” Amit Sheth, the chair and founding director of the AIISC, told AIM earlier.

Similarly, Adarsh Shirawalmath, the founder of Tensoic, told AIM that though his college has been helpful, it is still years behind. “We are lagging a bit in terms of where the SOTA is and what we are doing because some of the professors still might be researching on CNN whereas the SOTA is really ahead,” he said. 

The issue is critical. These days, colleges make it mandatory for students to complete online courses on subjects already included in the syllabus. “What’s the point of paying the teachers then?” asked a user, which aptly explains the core issue of it all.

The root of the problem seems to be the significant gap between the salaries offered to college professors and what they could potentially earn in the private sector. “Let’s talk about 95% of teachers working in the rest of the colleges in India. Why would someone work as a teacher getting paid at most 10 to 12 LPA if they were any good?”. This is the valid question that engineers should be asking.

“The market will pay 30 LPA to anyone decent.” This discrepancy in pay makes it difficult for colleges to attract and retain talented professionals, leading to a situation where those who do become professors are often not the best in the field.

This sentiment was echoed by others, who argued that the incentive structure is fundamentally flawed. Nobody half good wants to be a computer science teacher when they can get paid at least double that much in an equivalent experience IT job.

Still Hope

This is a critical issue, as it suggests that the teaching profession in India is not seen as a viable career option for skilled professionals, which in turn affects the quality of education that students receive. On the other hand, becoming a university professor at IITs and NITs is still something engineers strive for. Which is why the faculty at these premier institutes is good enough and training world class talent. 

That is also why a lot of India’s obsession with STEM degrees is creating a generation of jobless graduates, as universities apart from these premier institutes struggle to provide decent skills for the market. Moreover, not all students are capable of self-learning, which further complicates the issue. 

Despite the general dissatisfaction, some users suggested potential solutions to the problem. One such suggestion was to hire more professors with industry experience. Some experienced people should take up the plan to teach in college after retiring from the corporate sector. “We need more professors with industry experience. I would readily learn from some professor with good industry experience of 5 years rather than a PhD professor,” said an engineer. 

The over-reliance on external resources, the disparity in pay between professors and private sector employees, and the lack of practical industry experience among teaching staff are all contributing factors to this issue. 

The ideal way forward is to increase the barrier of entry for becoming a professor for computer science, which would end up making the professors upskill themselves. At the same time, the compensation offered to these professors needs to be substantially increased as well, to match with high paying jobs of the market. 

The post Indian Engineers Say Most College Professor Don’t Know Programming appeared first on AIM.

The rise of generative AI signals a shift towards solo-founded AI startups as the new standard. “I think Cloud+AI is increasingly making the Pieter Levels style model of a scrappy solo serial micro-entrepreneur viable, allowing one person to spin up and run multiple companies that generate income, possibly reaching billion-dollar valuations,” said former OpenAI and Tesla computer scientist Andrej Karpathy, referring to Lex Fridman’s latest podcast with Levels.

Levels is a self-taught developer and entrepreneur who has designed, programmed, shipped, and run over 40 startups, many of which are hugely successful. 

Recently, Karpathy founded Eureka Labs, an AI company he is currently running on his own. He is actively experimenting with various generative AI tools to develop educational content through visual storytelling. Eureka Labs aims to transform education by blending generative AI with traditional teaching methods. Karpathy, who has previously held pivotal roles at OpenAI and Tesla, describes Eureka Labs as “a new kind of school that is AI native.”

“We’re entering a world where it’s getting easier and more lucrative to start a company than it is to try and get hired by one,” quipped Dennis Kardonsky, founder of Soverin.ai. 

This idea is similar to what Sam Altman said in a recent interview. “We’re going to see 10-person companies with billion-dollar valuations pretty soon…in my little group chat with my tech CEO friends, there’s this betting pool for the first year there is a one-person billion-dollar company, which would’ve been unimaginable without AI. And now [it] will happen.”

In a recent Lightcone podcast, YC partner Harj Taggar discussed Sam Altman’s idea, saying, “Founders who’ve been doing this (running startups) for a while are obsessed with the idea of having fewer employees, as few as possible, because once you manage a large company with lots of employees, you realise how much it sucks.”

Similarly, Anton Osika, founder of Lovable, a Stockholm-based AI platform that claims to enable anyone to build software applications “with just a conversation in plain English,” believes that programs like his will be capable of creating “80% of all SaaS” software by the end of 2025. He adds that soon, “you will see software unicorns with virtually no human involvement—it’s quite likely it will be just one person.”

“I do believe that anyone can build most things—not big VC-funded companies, but most things to create a great, multi-million-a-year lifestyle business. And I think, even with AI, we’re seeing a lot of that, where individuals and small teams are building really, really valuable companies that aren’t venture-backed, nor should they be. But it’s still a great business,” said Ben Tossell, founder of the AI-driven newsletter Ben’s Bites.

Ben uses a suite of AI tools like ChatGPT to strategise, create content, and analyse business data, allowing him to run his business efficiently without a large team. 

He said that there is a really simple equation that goes into what it takes to build something that provides value to customers, where you can get thousands or tens of thousands of customers signing up and paying. “I love that,” he added.

Linkedin founder Reid Hoffman recently predicted that the 9 to 5 jobs will be extinct by 2034. “You may not only work at different companies, you might work in different industries,” said Hoffman, adding that people may stop working like employees and begin working in a gig economy.

The ‘Gig Economy Revolution,’ Hoffman believes, will be more significant than anticipated. According to his prediction, within the next decade, 50% of the population will become freelancers and earn more while working for “3 or 4 gigs,” than those working in traditional employment. Compared to traditional positions, this approach may offer less job security, although it does provide greater flexibility and more opportunities.

Solo Entrepreneurs All the Way

Arjun Reddy of India is a notable example of a successful solo entrepreneur, having founded several startups and currently focusing on two AI ventures, Nidum.ai and HaiVE.

Nidum.ai uses blockchain to create a decentralised AI economy where users can contribute computing power through AI mining software and access a vast network of AI resources on demand, paying only for what they use.

Learn: How Data Mining Works

On the other hand, HaiVE provides on-premise and custom cloud AI solutions to enterprises that want to turbocharge their operations with AI but are concerned about streaming their data to third parties and creating potential competitors.

Another key figure is Ramsri Goutham Golla. He is the founder and solo developer of Questgen.ai, a platform that offers various AI-powered tools for creating quizzes and educational content, such as higher-order question generators and multiple-choice question generators.

In a recent video tutorial, Golla explained that big publishers used to outsource quiz creation to companies known as item banks or tutoring chains, which would hire staff to create quizzes. He mentioned that now, with AI tools, you can generate 150 to 200 quizzes with just one click from 300 to 400 pages of content. This is possible because modern LLMs, like Gemini or even OpenAI models, can handle long contexts—up to 100,000 tokens or more. Moreover one can use frameworks like LangChain to split the text into smaller chunks.

Another venture by Golla, Supermeme.ai, focuses on using AI to generate memes and other creative content. This platform leverages AI to create engaging and humorous content for social media and marketing purposes.

Golla said that both these platforms are currently at $100K ARR (Annual Recurring Revenue), which is approximately INR 83 lakhs in annual revenue.

Dhravya Shah, a 19-year-old from India, is busy creating several innovative apps, including OpenSearchGPT as an alternative to SearchGPT, Radish as an alternative to Redis, and SuperMemory for daily reminders. He has also recently applied to Y Combinator.

RIP App Store?

Anthropic recently made Artifacts available to all Claude users. Users can now create and view Artifacts on both the Claude iOS and Android apps Deedy Das, Principal at Menlo Ventures, used Artifacts to build a Splitwise-like app. “With Claude launching on iOS today, I can now generate the Splitwise app instead of paying for Pro,” he said. 

This brings us to question if this is the end of App Stores. Groq’s Sunny Mandra thinks this is the beginning of “The Build Your Own (BYO) era.”

The post The Birth of Solo Micro Entrepreneurs appeared first on AIM.

Cursor, an AI-powered product built by Anysphere, is transforming the developer community, helping them turn ideas into reality across sectors. From empowering an eight-year-old to build a chatbot within an hour to building a financial dashboard using only voice, Cursor AI is currently reeling in its force multiplier era. 

At its core, what pushes Cursor AI ahead of the curve are its seamless features like integration to workflow, customisations, and GPT-4 assistance, that democratise coding. Some of these features are available in its free version, while the paid plan is more suitable for larger teams or those with specific, and more advanced requirements. 

Meanwhile, developers and tech enthusiasts have taken over the internet to share their insights on leveraging Cursor AI. It is interesting to note their custom prompts and workflows that have led to building apps and features in record time. 

Cursor 101

Corbin Brown, a creator and an investor with 49.9K YouTube subscribers, has made a Cursor 101 explainer video for beginners. The video highlights features like codebase integration, AI driven edits, and privacy first approach making the tool extremely secure and easy to use for developers. 

Web Apps using Cursor

Abdul Majed Raja, who specialises in AI, ML and open source, has built a community of 79.7K subscribers on YouTube. He has been covering several aspects of Cursor AI through his lucid tutorials around building a Chrome extension, creating a web app, and a JS game – all from scratch. 

Building Financial Dashboard using Cursor

Mckay Wrigley, the founder of Takeoff, made a tutorial video on building a financial dashboard in 5 minutes. Additionally, he is also offering a paid course with a 20% launch discount for those interested in a comprehensive understanding of the tool and its potential. 

UI/UX Using Cursor

Meng To, a Singapore-based designer, built his dream video editor using prompts within three weeks – something which usually takes way longer. While his post is taking X by storm, he is on a mission to teach design to coders and coding to designers. 

Real-time Problem Solving Using Cursor

Ray Fernando, a former engineer with Apple, built an entire fully functional app in real time on livestream using Cursor AI. His method of teaching is gaining traction online, where he talks about the basics of coding, integrating OpenAI’s GPT-4, and follows up with real-time problem-solving. 

Cursor for Beginners 

Steven, a self-taught developer with eight years of experience, aims to democratise his knowledge about web development and coding through easy-to-learn videos. He delves into the features, user interface, and ways in which it speeds up your workflow. 

Enhance Productivity using Cursor

Zachary Lee, an NYC-based tech generalist, focuses on deconstructing LLMs and AI products in his videos. In this short tutorial, he talks about Cursor AI’s four core features, like inline editing, code generation, and cursor chat that can be leveraged to maximise productivity. 

Build AI Projects with Cursor

Kevin Kernegger, the founder of Macherjek GmbH with over a  decade of experience, teaches a nine-part course on skillpark.ai for $99. In a tailored manner, he helps individuals create their own AI projects. 

With the hype taking over the internet, software engineers claim it is tough to return to a time when this tool didn’t exist. Its adoption in the non-tech industry is also on the rise – especially its potential within the design community.

The post 8 Resources to Help You Learn Coding with Cursor appeared first on AIM.

According to a recent report, OpenAI is looking to secure more funding as its researchers develop ‘Orion’, a new model anticipated to solve complex problems more effectively than current AI technologies.

This confirms AI insider Jimmy Apples’ cryptic post from last year, which featured an image of the ‘Orion’ constellation with the caption, “Let’s conquer the cosmos”.

OpenAI chief Sam Altman, too, had hinted a few days earlier in a cryptic post that the company was working on a project known internally as Project Strawberry, also referred to as Q*. 

“I love summer in the garden,” wrote Altman on X, posting the image of a terracotta pot containing a strawberry plant with lush green leaves and small, ripening strawberries.

One key use of Strawberry is to produce high-quality training data for Orion, the next major LLM currently being developed by OpenAI.

However, the catch is that the new model takes extra time to generate responses. This confirms Apples’ revelation that Q* hasn’t been released yet because OpenAI is not satisfied with the latency and other ‘minor details’ they want to further optimise.

Despite this, when given extra time to ‘think’, the Strawberry model excels in addressing customer inquiries on more subjective topics, such as product marketing strategies. 

To highlight Strawberry’s strengths in language-related tasks, OpenAI employees have demonstrated to their colleagues how the model can solve complex word puzzles like The New York Times Connections, according to The Information report.

“The thing is proper *reasoning* SHOULD be time consuming. What we have now with LLMs is just a step (if even) above bare retrieval. I would be happy to pay $$$ for a good reasoning system, latency and all. But I am probably in the minority. Hopefully they can have a more rugged “enterprise” version available when they release what they have been cooking,” Bojan Tunguz, former software engineer at NVIDIA, posted on X. 

Worth the Wait? 

According to the report, OpenAI has demonstrated the Orion model to American national security officials. 

Taking a jibe at OpenAI, Stability AI founder Emad Mostaque posted on X, “OpenAI showing Strawberry/Q* to national security officials first highlight that AGI labs are likely to shift from consumer focus to military and state backers.”

He added that bills like SB 1047 will further accelerate this shift, leading to AI that is aligned with state actors rather than consumers.  However, OpenAI has opposed the California AI Bill.

“We join other AI labs, developers, experts, and members of California’s Congressional delegation in respectfully opposing SB 1047 and welcome the opportunity to outline some of our key concerns,” the company said in a statement.

Meanwhile, OpenAI has been working closely with the US government. OpenAI’s chief technology officer, Mira Murati, said in a recent interview that the company gives the government early access to new AI models, and the latter have been in favour of more regulation.

“We’ve been advocating for more regulation on the frontier, which will have these amazing capabilities but also have a downside because of misuse. We’ve been very open with policymakers and working with regulators on that,” she said.

Notably, OpenAI has postponed the release of its video generation model Sora, along with the Voice Engine and voice-mode features of GPT-4o. It is anticipated that GPT-5 may also be released after the elections. 

Recently, OpenAI introduced SearchGPT, though there is no set timeline for its availability.

Earlier this year, Murati confirmed that the elections were a major factor in the release of GPT-5. “We will not be releasing anything that we don’t feel confident about when it comes to how it might affect the global elections or other issues,” she said.

Meanwhile, OpenAI recently appointed retired US Army general Paul Nakasone to its board of directors. As a priority, Nakasone joined the board’s safety and security committee, responsible for making recommendations on critical safety decisions for all OpenAI projects and operations.

OpenAI has also been working closely with the US Department of Defence on open-source cybersecurity software, collaborating with the Defense Advanced Research Projects Agency (DARPA) for its AI Cyber Challenge announced last year. 

As OpenAI befriends the US government, consumers are left waiting for the next big release.

Google Steals the Limelight 

It appears that Google has followed OpenAI’s lead this time. 

Interestingly, just after the release of The Information report, Google introduced three new experimental Gemini models to improve speed, accuracy, and handling of complex prompts. The new models are Gemini 1.5 Flash-8B, Gemini 1.5 Pro, and Gemini 1.5 Flash. 

The new Gemini-1.5-Pro (0827) shows strong gains in coding and maths over previous versions and is in second position on the LMsys Chatbot Arena. 

A few days ago, Google AI Studio lead Logan Kilpatrick took a jab at critics who claim Google lacks innovation, highlighting that the company was the first to ship a 1 million and 2 million context window, a state-of-the-art multi-modal LLM, context caching, and a high-quality small model for developers called Flash. 

“So yeah, definitely no innovation happening here…..,” he quipped.

Moreover, Google recently appointed Noam Shazeer, the former head of Character.AI and a veteran Google researcher, to co-lead its key AI project, Gemini. 

Shazeer will join Jeff Dean and Oriol Vinyals as a technical lead for Gemini, which is being developed by Google’s AI division, DeepMind.

Anthropic, too, wasn’t going to be left behind. The OpenAI rival has made Artifacts available to all Claude users. Users can now create and view Artifacts on both the Claude iOS and Android apps. 

The company stated that since launching its preview in June, tens of millions of Artifacts have been created.

Deedy Das, Principal at Menlo Ventures, used Artifacts to build a Splitwise-like app. “With Claude launching on iOS today, I can now generate the Splitwise app instead of paying for Pro,” he said.

The post OpenAI’s ‘Orion’ and the Battle for Superiority appeared first on AIM.

NVIDIA may reign as the king of GPUs, but competition is heating up. In recent years, a wave of startups has emerged, taking on the Jensen Huang-led giant at its own game.

Tenstorrent, a startup led by Jim Keller, the lead architect of AMD K8 microarchitecture, is developing AI chips that the company claims perform better than NVIDIA’s GPUs.

“We have a very power-efficient compute, where we can put 32 engines in a box, the same size as NVIDIA puts eight. With our higher compute density and similar power envelope, we outperform NVIDIA by multiples in terms of performance, output per watt, and output per dollar,” Keith Witek, chief operating officer at Tenstorrent, told AIM.

(Wormhole by Tenstorrent)

NVIDIA’s chips used in the data centres need silicon interposers like HBM memory chips. Companies like Samsung and SK Hynix, along with NVIDIA, have also made millions selling these chips. However, Tenstorrent chips eliminate the need for these chips.

Similarly, Cerebras Systems, founded by Andrew Feldman in 2015, has developed chips to run generative AI workloads such as training models and inference. Their chip, WSE-3– is the world’s largest AI chip– with over 4 trillion transistors and 46225mm2 of silicon.

Check: Difference Between NVIDIA GPUs – H100 Vs A100

The startup claims its chips are 8x faster than NVIDIA DGX H100 and are designed specifically to train large models.

(World’s largest AI chip- WSE-3)

Startups Building for the Inference Market

There are startups developing chips designed specifically for inferencing. While NVIDIA’s GPUs are in great demand because they are instrumental in training AI models, for inference, they might not be the best tool available. 

D-Matrix, a startup founded by Sid Sheth, is developing silicon which works best at inferencing tasks. Its flagship product Corsair is specifically designed for inferencing generative AI models (100 billion parameter or less) and is much more cost-effective, compared to GPUs. 

“We believe that a majority of enterprises and individuals interested in inference will prefer to work with models up to 100 billion parameters. Deploying larger models becomes prohibitively expensive, making it less practical for most applications,” he told AIM.

Another startup that is locking horns with NVIDIA in this space is Groq, founded by Jonathan Ross in 2016. According to Ross, his product is 10 times faster, 10 times cheaper, and consumes 10 times less power.

Groq is designed to provide high performance for inference tasks, which are critical for deploying AI models in production environments.

Recently, another player, Cerebras, announced its Cerebras inference, which they claim is the fastest AI inference solution in the world. It delivers 1,800 tokens/sec for Llama3.1 8B and 450 tokens/sec for Llama3.1 70B, which is 20x faster than NVIDIA GPU-based hyperscale clouds.

Challengers in the Edge AI Market

While NVIDIA may have made its name and money by selling GPUs, over the years, it has also expanded in other segments, such as developing chips for humanoids, drones, and IoT devices.

SiMa.ai, a US-based startup with strong roots in India, is building chips which can run generative AI models on the embedded edge. Founded by Krishna Rangasayee in 2018, the startup takes NVIDIA as its biggest competitor.

Rangasayee believes multimodal AI is the future and the startup’s second-gen chip is designed to run generative AI models on the edge– on cars, robotic arms, humanoids, as well as drones.

“Multimodal is going to be everywhere, from every device to appliances, be it a robot or an AI PC. You will be able to converse, watch videos, parse inputs, just like you talk to a human being,” he told AIM.

Notably, SiMa.ai’s first chip, designed to run computer vision models on edge, beat NVIDIA on the ML Perf benchmarks. Another competitor of NVIDIA in this space is Hailo AI. It is building chips that run generative AI models on the edge.

Everyone Wants a Piece of the Pie 

Notably, these startups are not seeking a niche within the semiconductor ecosystem. Instead, they are focused on delivering top-tier products and are unafraid to compete directly with NVIDIA.

They all want a piece of the pie and are already locking horns with NVIDIA. 

D-Matrix, for instance, counts Microsoft, which is one of the AI model builders, as its customers. Sheth revealed that the company has customers in North America, Asia, and the Middle East and has signed a multi-million dollar contract with one of its customers. The point here is that Microsoft is one of NVIDIA’s biggest enterprise customers.

Cerebras also counts some of the top research and supercomputing labs as its customers. Riding on the success, the startup plans to go public this year.

Rangasayee previously told AIM that his startup is in talks with many robotics companies, startups building humanoids, public sector companies as well as some of the top automobile companies in the world.

They All Might Lose to CUDA

All these startups have made substantial progress and some are preparing to launch their products in the near future. While having advanced hardware is crucial, the real challenge for these companies will be competing against a monster – CUDA.

These startups, which position themselves as software companies which build their own hardware, have come up with their own software to make their hardware compatible with their customer’s applications. 

For example, Tenstorrent’s open-source software stack Metalium is similar to CUDA but less cumbersome and more user-friendly. On Metalium, users can write algorithms and programme models directly to the hardware, bypassing layers of abstraction.

Interestingly, they have another one called BUDA, which represents the envisioned future utopia, according to Witek. 

“Eventually, as compilers become more sophisticated and AI hardware stabilises, reaching a point where they can compile code with 90% efficiency, the need for hand-packing code in the AI domain diminishes.”

Nonetheless, it remains to be seen how these startups compete with CUDA. Intel and AMD have been trying for years, yet CUDA remains NVIDIA’s moat. 

“All the maths libraries… and everything is encrypted. In fact, NVIDIA is moving its platform more and more proprietary every quarter. It’s not letting AMD and Intel look at that platform and copy it,” Witek said.

The post The Rise of Non-NVIDIA GPUs appeared first on AIM.

With so many companies developing automation tools and AI agents, it has become increasingly challenging to identify the differentiator or the moat of your company. 

Three industry veterans—Sidu Ponnappa, Aakash Dharmadhikari, and Steve Sule—found themselves at the crossroads. Originally setting out to build an IT services company, their journey took an unexpected turn with the arrival of ChatGPT in November 2022, which would redefine their approach to business and ultimately lead to the founding of realfast.ai.

Backed by PeakXV, RTP Global and DeVC, the leading project of realfast.ai is its Vayu platform, which emerged from the team’s real-world experience. The team believes that the only way to develop a meaningful AI assistant is on top of real work—commercial work that customers are willing to pay for because it has value. 

“Real work is messy, with many variables, and it’s a completely different dynamic from what you might expect in a lab environment,” Dharmadhikari told AIM.

The Vayu platform currently integrates deeply with the Salesforce developer platform, giving AI agents access to the same tools that human developers use but through a different interface. This approach allows AI to assist in tasks that traditionally require human intuition and experience.

One of the most innovative aspects of realfast’s approach lies in the way they train the AI agents. Unlike traditional AI models that rely on vast amounts of static data, realfast.ai’s models learn from observing human developers at work. 

“We realised that building an applied AI product is like teaching a child,” Ponnappa explained. “You need to break down your own thinking and create exercises that teach the AI the principles you’ve learned over years of experience.”

This method has led to the development of AI agents capable of handling over 700 specific tasks related to unit testing, all based on how human developers approach these tasks. “It’s a combination of natural language processing, fine-tuning, and generating large quantities of data,” Sule added.

The Moat

realfast.ai’s first major success came when they started working with design partners to implement their AI-driven processes. “We’ve been in production with a hybrid team—human developers assisted by AI agents—on tasks like unit testing,” Dharmadhikari shared. “We’ve seen up to a 3x speed-up in these processes.”

Sidu Ponnappa

As Realfast.ai continues to grow, the founders remain focused on their mission to revolutionise the IT services industry through AI. “The largest challenge we face is that there’s no existing data for this type of work,” Ponnappa noted. “The process and craft by which a finished product is created aren’t tracked or recorded anywhere because there’s been no reason to do so until now.”

With an example, the founders illustrated that the company was able to streamline 3,000 lines of legacy code for a company, which is very dirty and messy. This wouldn’t have been possible with just human engineers as it’s a tedious task. 

Currently, the platform uses models like ChatGPT and Anthropic’s Claude, but does not rely on open-source models as they believe that their reasoning capabilities are nowhere close to the proprietary ones. Moreover, unlike everyone else making open-source AI agents, the realfast.ai platform is SOC 2 and ISO compliant.

“We think of this platform as an Iron Man suit for the IT guys,” Ponnappa joked, noting that this is how they often pitch it to the investors. 

The Pivot They Needed

In the early days, Dharmadhikari and Ponnappa, both with extensive experience in services, especially from their time at Infosys and ThoughtWorks, respectively, decided to start a boutique IT company focusing on complex systems integration, “a space we believed had significant untapped potential”.

Sule had been an advisor for the team long before he came on board as a co-founder. 

This plan, however, took a drastic turn with the release of ChatGPT. The founders, already bullish on IT services, quickly recognised the transformative potential of AI. “Our strategy was always tool-oriented because value to customers comes from reliable delivery,” Ponnappa explained. 

“When ChatGPT dropped, it was unlike any tool we had ever seen. It wasn’t just a 10% improvement here or there; it was driving unprecedented changes across the entire lifecycle, from sales to coding.”

Pivoting to this field, as they continued to integrate ChatGPT into their processes, the founders quickly realised the potential of AI to revolutionise the IT services sector. 

“We started to see that the way IT services will look in five years post-AI adoption will be completely different from today,” Ponnappa noted. “It’s a platform problem. You need a platform where AI tools, assistants, and humans can work together seamlessly.”

This insight became the foundation of realfast.ai, a company dedicated to building an AI transformation platform that could support the unique needs of IT services companies. The goal was not just to enhance existing processes but to fundamentally change how services are delivered.

The post This AI Startup is an Iron Man Suit for the IT Guys appeared first on AIM.

It’s now widely recognised that selling AI models is a zero-margin game. The next wave of AI startups must capitalise on LLMs in the application layer to tackle real-world challenges.

“The next billion dollar startups in AI will play on the application layer and not the infrastructure layer,” said AIM Media House chief Bhasker Gupta in a LinkedIn post. 

Gupta added that there is a plethora of problems to be solved using AI, and these startups will localise their solutions while maintaining a broad-based approach.

Echoing a similar market sentiment was Nayan Goswami, the founder and CEO of Chop. “The next major wave of AI innovation will focus on the application layer, where startups will build specialised vertical AI software-as-a-service (SaaS) companies for global markets,” he said. 

Goswami further elaborated that with robust foundational models like Anthropic, Cohere, and OpenAI, along with infrastructure companies like LangChain and Hugging Face advancing rapidly, we’re poised to witness a surge in application-layer startups targeting specific verticals. 

“Think of foundational models as the roadways, and application layers as the vehicles driving on them,” he explained. 

Finding the Right Application to Build is Key 

Andrew Ng, the founder of DeepLearning.AI believes AI’s usefulness in a wide variety of applications creates many opportunities for entrepreneurship. However, he advised budding entrepreneurs to be extremely specific about their ideas for integrating AI. 

For instance, he explained that building AI for livestock is vague, but if you propose using facial recognition to identify individual cows and monitor their movement on a farm, it’s specific enough.  

A skilled engineer can then quickly decide on the right tools, such as which algorithm to use first or what camera resolution to pick.

In a recent interview, Ng explained that the cost of developing a foundation model could be $100 million or more. However, the applications layer, which receives less media coverage, is likely to be even more valuable in terms of revenue generation than the foundation layer.

He also said that unlike foundation models, the ROI on the application layer is higher. “For the application layer, it’s very clear. I think it’s totally worth it, partly because it’s so capital efficient—it doesn’t cost much to build valuable applications. And I’m seeing revenues pick up. So at the application layer, I’m not worried,” he said.

Perplexity AI serves as a strong example by integrating search with LLMs. Rather than building its own foundational models, the startup leverages state-of-the-art models from across the industry, focusing on delivering optimal performance. The company is planning to run ads as well from the next quarter onwards. 

However, not everyone is going to make the cut; some startups are going to fail. Statistically speaking, around 90% of startups don’t survive long enough to see the light at the end of the tunnel.

Ashish Kacholia, the founder and managing director of Lucky Investment Managers, said, “AI is the future but key is how the applications shape up to capitalise on the technology.”

India is the Use Case Capital of AI 

“India is going to be a use case capital of AI. We’ll be very big users of AI, and we believe that AI can significantly help in the expansion of the ONDC Network,” said Manoj Gupta, the founder of Plotch.ai, in an exclusive interview with AIM. 

Similar thoughts were shared by Nandan Nilekani when he said that India is not in the arms race to build LLMs, and should instead focus on building use cases of AI to reach every citizen. He added that “Adbhut” India will be the AI use case capital of the world. 

“The Indian path in AI is different. We are not in the arms race to build the next LLM, let people with capital, let people who want to pedal chips do all that stuff… We are here to make a difference and our aim is to put this technology in the hands of people,” said Nilekani.

Krutrim founder Bhavish Aggarwal believes that India can build its own AI applications. Agreeing with him, former Tech Mahindra chief CP Gurnani said, “It’s time to stop ‘adopting’ and ‘adapting’ to AI applications created for the Western world.”

Gurnani said that the time is ripe for us to build AI models and apps based on Indian data, for Indian use cases, and store them on India-built hardware, software and cloud systems. “That will make us true leaders in the business of tech,” he added. 

Notably, Gurnani recently launched his own AI startup AIonOS. 

Startups Offering More Than LLMs

Lately, several AI startups in India have been building services using generative AI. For example, Unscript, a Bengaluru-based AI startup, is helping enterprises create videos with generative AI. Another video generation startup, InVideo, is estimated to generate $30 million in revenue in 2024. 

Recently, Sarvam AI launched Sarvam Agents. While the startup, backed by Lightspeed, Peak XV, and Khosla Ventures, is not the only company building AI agents, it stands out for its pricing. The cost of these agents starts at just one rupee per minute. 

According to co-founder Vivek Raghavan, enterprises can integrate these agents into their workflow without much hassle.

These agents can be integrated into contact centres and various applications across multiple industries, including insurance, food and grocery delivery, e-commerce, ride-hailing services, and even banking and payment apps.

Similarly, Krutrim AI is making AI shopping co-pilot for ONDC. Khosla Ventures-backed upliance.ai is building kitchen appliances  integrating generative AI. 

Meanwhile, Ema, an enterprise AI company founded by Meesho board member Surojit Chatterjee, recently raised an additional $36 million in Series A funding. 

The company is building a universal AI agent adaptable to a wide array of industries, including healthcare, retail, travel, hospitality, finance, manufacturing, e-commerce, and technology. 

Enterprises use Ema for customer support, legal, sales, compliance, HR, and IT functions. 

Lately, we have observed that Y Combinator is bullish on Indian AI startups, many of which are focused on building AI applications.

For example, the creator of AI software engineer Devika, Mufeed VH, who founded Stition.AI, is now part of YC S24 batch. His startup works around AI cybersecurity for fixing security vulnerabilities in codebases, and is now renamed to Asterisk. 

On the agentic front, Indian co-founders Sudipta Biswas and Sarthak Shrivastava are building AI employees through their startup FloWorks.

Examples are aplenty, with India poised to boast 100 AI unicorns in the next decade. 

In a conversation with AIM, Prayank Swaroop, partner at Accel India, said that the 27 AI startups his firm has invested in over the past few years are expected to be worth at least ‘five to ten billion dollars’ in the future, including those focused on wrapper-based technologies.

There are a host of categories, such as education, healthcare, manufacturing, entertainment, and finance, to explore with generative AI, and this is just the beginning.

The post The Secret to Creating the Next Billion-Dollar AI Startup appeared first on AIM.

Today is the deadline for submitting applications to Y Combinator Fall Batch 2024. For the YC W24 program, 260 companies were selected from over 27000 applications. “With an acceptance rate under 1%, this was one of the most selective cohorts in YC history,” said Garry Tan, President & CEO of Y Combinator.

Despite this, the startup accelerator is still processing hundreds of applications every hour and this might be the best time to apply. But there are definitely a few things to keep in mind before you get selected and have an opportunity to click a photo with the famous YC signboard.

To start with, YC has its own list of Dos and Don’ts for getting started. This includes getting to the point, staying away from buzzwords, and showing the team that you won’t break within a year. 

As of early 2024, YC has funded over 4,500 startups with a combined valuation of more than $600 billion, making it an attractive lifeline for many emerging startup founders.

Interestingly, this year, YC has also taken a lot of interest in Indian AI startups, as their focus is increasing on founders and application based generative AI. What can startups learn from all the announcements so far? 

Focusing on a Technical Team with a Technical Founder

While YC will be at an advantage by investing in startups that will potentially hit millions of dollars, the YC programs are equally enticing for others as there is no requirement on formal degree or actual returns, YC simply bets on a founder and their idea.

One of the most important aspects that inventors look at is the founding team of a startup. And since most of the current startups are tech and AI based, it is important for the founder to be technical. So much so that he/she should be able to build the product by himself, if not, “you are not a technical founder,” said Isabella Reed, founder of CamelAI, which is part of YCW24.

Though some might disagree since tools like Cursor AI are changing the definition of what an engineer means, it is true that a startup which is building a tool, is a tech startup. For Indian founders, this is also a wake up call to start adopting tools like Cursor and others since it is now a given default for the future of tech products. 

One of the interesting strategies that founders are finding out is pitching their ideas to VCs and finding out the flaws and what can be improved, and some are still working on the pitch till 3 AM. One founder pitched to VCs and was able to get another co-founder for his startup, create a go-to-market strategy, and also send his product to beta users for feedback. 

Those who can’t get VCs, are asking people on X to help them with their YC applications and pitches. Which in many cases is turning to a roast show.

Meanwhile, one of the most important things for founders is to remember to not become a generative AI influencer on the way to YC. Though it can be fun to watch and preach, it is not helpful for the pitch and the process of getting selected. 

The most important thing to keep in mind is showing traction, picking a specific metric to back, a passion for the startup you are building, while also presenting the honest flaws of the product.

Don’t Get Blacklisted

It is not easy to get into YC. Amartya Jha, the co-founder and CEO of CodeAnt AI, which recently got into YC, narrated in a post how his team got rejected the first time because they couldn’t explain their product well to the investors. Despite this and the fear of getting blacklisted, they made a 45-minute video explaining their product and sent it to YC again. The following week, they managed to get another interview with YC and were finally selected!

Though an inspirational story, the lesson is to explain your product well in the first go.

Another Indian startup Asterisk by Mufeed VH, the creator of Devika, also got into YC. The team started with the Stition.ai and building cybersecurity products and is now finally into YC, becoming the first startup to get into YC from Kerala.

Given the small acceptance rate, founders keep scrolling their email inbox to find the invitation letter for the interview. Though it might be difficult to get an acceptance letter, it is definitely useful to at least fill out the form. 

“It’s the smallest set of questions you can ask to get a clear signal about a startup, so it’s also the smallest set of questions you can answer to get a clear signal about your own startup,” said Pete Koomen, group partner at YC.

Moreover, consistency is king. Kashif Ali, the founder of TaxGPT, which is part of the YC S24’s batch said that he applied seven times with three different companies in the last four years, and finally got in at the 8th time. 

At the same time, not everyone needs YC. If you are a true entrepreneur, you can be successful without YC. Keep applying though.

P.S. No sweat equity.

[This is an opinion and not a promotional article]

The post Cracking YC: A Guide for AI Startups appeared first on AIM.

“Forget Shark Tank, the real startup story is unfolding at IIT Madras Incubation Cell (IITMIC) in the IIT Madras Research Park,” said Aditya Kondawar, partner and vice president at Compcircle, a wealth and asset management firm.

Kondawar is right. Many notable startups, including top names like Ather Energy, Mindgrove Technologies, and MediBuddy, have all emerged from IIT Madras. According to him, IITMIC has an 80% success rate with startups. 

Talking about MediBuddy, Kondawar said that it has raised $200 million to date and is currently valued at close to $500 million. He further said that IIT Madras plans to conclude 2024 with 366 patents and 100 new startups and claimed that IITMIC owns “1% equity in many of these companies, making INR 50-60 crore a year”.

Kondawar also spoke about IIT Madras’ ‘10x Plan’, which aims to scale incubations dramatically and elevate India’s technological standing globally. For this, the institute is looking to raise the number of companies to 1,000 a year, from about 45 at present. 

The incubation cell plans to partner with 50-100 incubators in Tier-2, 3, & 4 institutes and nurture their incubates to build India’s best R&D in the world for startups working in the field of AI-ML.

Uniphore, a company that builds conversational AI products, is another notable name in the AI ecosystem to have come out of IIT Madras. Interestingly, the institution is also home to AI4Bharat, a research lab dedicated to advancing AI technologies tailored to Indian languages and regional needs.

Inside IITMIC 

According to data from the IITMIC, the number of startups founded by faculty members surged from 37 in April 2017 to 69 by June 2019, and 80 in 2020, to finally reaching 94 by October 2021. These startups were incubated by IITMIC, one of India’s top deep technology startup hubs.

A total of 77 faculty members from various departments of the institute have been involved in establishing these startups. This number represents nearly 13 percent of the institute’s total faculty strength of around 600, a proportion that the IIT Madras Incubation Cell claims is comparable to the best universities globally.

Faculty members at IIT Madras have founded at least 94 startups, with a combined valuation exceeding INR 1,400 crore. These ventures range from developing hybrid aerial vehicles to converting waste into crude oil and creating efficient water transport solutions.

According to officials, the IIT faculty members have founded, mentored or advised over 240 startups in the past decade, which today have a combined valuation of INR 11,500 crore.

Furthermore, the institute is currently targeting the incubation of at least 100 startups across various sectors in 2024. V Kamakoti, the director of IIT Madras, sharing his targets for 2024, said that IIT Madras saw a lot of ambitions being realised during 2023. 

“In the past year, we took up several important projects, including the IITM Zanzibar campus, which became the first-ever IIT to be established overseas. We also launched a department of medical sciences and technology, and are aspiring to do many more things in that direction,” he added, saying that he looks to close this financial year with the maximum number of projects.

Aravind Srinivas, the founder and CEO of Perplexity AI, is an alumnus of IIT Madras. In 2022, Srinivas co-founded the company that recently achieved unicorn status with a valuation of $1.04 billion. 

Going Beyond IIT Madras 

While IIT Madras may get all the limelight, there are good incubation programmes at other IITs as well. For instance, IIT Kanpur has developed a centre of excellence (CoE) for AI and has a startup incubation programme called Startup Incubation and Innovation Centre (SIIC). It fosters innovation, research, and entrepreneurial activities in technology-based areas, including AI.

Similar incubation programmes and support for tech/AI startups can be seen at IIT Kharagpur, Bombay and Delhi. Capillary Technologies, founded in 2008 by IIT Kharagpur alumni Aneesh Reddy, Krishna Mehra, and Ajay Modani, was incubated at IIT Kharagpur.

SensiBol, founded in 2011 by Vishweshwar Rao, was incubated at IIT Bombay the following year. HyperVerge, started in 2013 by Kedar Kulkarni, Vignesh Krishnakumar, Kishore Natarajan, Saivenkatesh Ashokkumar, and Praveen Kumar, was also incubated at IIT Madras.

Abroad, particularly in tech hubs like Silicon Valley, startups benefit from a well-established ecosystem with ample venture capital, advanced infrastructure, and a culture of innovation. Many companies have originated from institutions like Stanford University, exemplifying this advantage.

The university has been a prolific breeding ground for tech startups and influential companies. Notably, Apple Inc was co-founded by Steve Jobs and Steve Wozniak, with Jobs’s experience there playing a significant role in shaping his vision for the company, despite having dropped out of the university. 

Similarly, NVIDIA, founded by Jensen Huang, Chris Malachowsky, and Curtis Priem, emerged from Stanford’s engineering talent pool to become a leader in graphics processing technology. 

LinkedIn, the widely used professional networking platform, was co-founded by Reid Hoffman, an alumnus, while Yahoo! was created by Jerry Yang and David Filo, both Stanford graduate students again. 

India, too, is on the right track with a similar ecosystem evolving in the country.

Now is the Best Time for AI Startups in India

Interestingly, India ranks third among the top five countries in terms of business funding, surpassing both the United Kingdom and Germany.

Antler, a VC firm, recently announced a $10 million investment in early-stage Indian AI startups. Rajiv Srivatsa, partner at Antler, shared the news on X, emphasising: “NOW is the best time to build a startup in India!”

Similarly, last month, Google introduced several initiatives to enhance AI development in India, including a partnership with the MeitY Startup Hub to train 10,000 startups.

At the Google I/O Connect Bengaluru, the tech giant introduced the ‘2024 Class of Google for Startups Accelerator: AI First’ program in India. This cohort includes 20 exceptional AI-first startups from diverse sectors such as gaming and manufacturing, chosen from over 1,030 applications nationwide.

Earlier in March, reports suggested that India will partner with chip-making giant NVIDIA to procure up to 10,000 of its GPUs and NPUs and offer them at subsidised rates to local startups, researchers, academic institutions, and other users in a bid to boost the AI infrastructure in India.

Furthermore, the JioGenNext team invests heavily in AI startups, providing tailored support and strategic insights. The latest MAP’24 cohort included 10 generative AI startups spanning healthcare, banking, legal services, and agriculture.

The post IIT Madras is Emerging as a Major Hub for AI Startups. Here’s Why? appeared first on AIM.

Recently, Amazon CEO Andy Jassy revealed that by leveraging Amazon Q, the company was able to save 4,500 developers-years of work. “Yes, the number is crazy, but real,” he posted on X.

With Amazon Q, the company has significantly cut down the time needed to update Java applications. “The average time to upgrade an application to Java 17 plummeted from what’s typically 50 developer days to just a few hours,” he said. 

He added that in under six months, the company has been able to upgrade more than 50% of its production Java systems to modernised Java versions at a fraction of the usual time and effort. “Our developers shipped 79% of the auto-generated code reviews without any additional changes.”

“The benefits go beyond how much effort we’ve saved developers. The upgrades have enhanced security and reduced infrastructure costs, providing an estimated $260 million in annualised efficiency gains,” he claimed.

Indeed, generative AI has made coding a breeze. Tools such as GitHub Copilot, Devin, and Amazon Q simplify the development process, making application creation easier and helping developers and enterprises reduce technical debt.

Technical debt arises when an enterprise rushes a product to meet deadline requirements without properly checking the code quality and debugging. Incomplete documentation and insufficient testing can lead to errors and inefficiencies that add to the debt.

Converting Legacy Code Base

Amazon is not the only company reducing technical debt with AI. San Francisco-based Databricks, which uses generative AI to quickly analyse and understand its legacy code base—something its CIO says has eased the burden on engineers.

Seventy-five-year-old payroll-processing company ADP is also looking at generative AI to convert COBOL to Java. “A big problem that we, and other legacy companies face is that we have COBOL running in our systems,” said Amin Venjara, the chief data officer of ADP. He added that today, very few programmers are familiar with COBOL.

The Roseland, NJ-based company is exploring the use of generative AI to convert its mainframe code from COBOL—a language developed in the 1950s and still widely used in banks and financial services—into Java, a programming language that has been around since 1995.

Wayfair, the online furniture retailer, is using generative-AI-based coding tools to update old code. Though Wayfair, established two decades ago, does not use COBOL, it does have “legacy code” in languages such as PHP and outdated database code in SQL. 

Additionally, there is code written by developers who are no longer with the company.

GenAI to Assist in Tedious Tasks

Generative AI acts as an intelligent assistant that automates tedious tasks, suggests improvements, and enhances code quality. Armand Ruiz, the VP of product at IBM, says that his favourite use case for generative AI is in software development.

According to Ruiz, GenAI has several use cases in software development. It can convert plain English instructions into code in the preferred programming language. 

Code translation tools convert languages like COBOL to Java, and facilitate code modernisation and migration. Bug-fixing tools identify and suggest fixes for code errors, thereby enhancing code reliability.

Notably, IBM recently announced the IBM watsonx Code Assistant for Enterprise Java Applications, expected to be generally available later this year.

Generative AI also plays a significant role in streamlining code maintenance through refactoring. Generative AI automates refactoring by suggesting or implementing code transformations. It can identify common anti-patterns and propose more efficient alternatives, ensuring that refactoring adheres to coding standards and best practices. 

Generative AI Tools are Shaping Software Engineering

Recently, AI coding tool Cursor AI has been generating buzz on social media.

OpenAI co-founder Andrej Karpathy praised the AI-integrated code editor, saying, “Programming is changing so fast… I’m trying VS Code Cursor + Sonnet 3.5 instead of GitHub Copilot again, and I think it’s now a net win.” 

“Just empirically, over the last few days, most of my ‘programming’ is now writing English (prompting and then reviewing and editing the generated diffs) and doing a bit of ‘half-coding’, where you write the first chunk of the code you’d like, maybe comment on it a bit so the LLM knows what the plan is, and then tab tab tab through completions.”

Previously, software engineers used to take a substantial amount of time to deliver a product, but now this has drastically decreased. GitHub recently launched Models, a playground which will offer developers access to leading LLMs, including Llama 3.1, GPT-4o, GPT-4o Mini, Phi 3, and Mistral Large 2. 

“With GitHub models, developers can now explore these models on GitHub, integrate them into their dev environment in Codespaces and VS Code, and leverage them during CI/CD in Actions – all simply with their GitHub account and free entitlements,” explained Github chief Thomas Dohmke.

Then, there are AI software engineers like Genie and Devin. Genie is designed to emulate the cognitive processes of human engineers, enabling it to solve complex problems with remarkable accuracy and efficiency. “We believe that if you want a model to behave like a software engineer, it has to be shown how a human software engineer works,” said Alistair Pullen, the cofounder of Cosine.

Learn to use AI Tools if you are a Software Engineer

“Software development companies will start hiring coders who can demonstrate in interviews that they master AI-assisted coding tools like Copilot or Cursor,” said Andriy Burkov, machine learning lead at TalentNeuron. 

He added that claims that LLMs can’t write reliable code are immature. “Most junior and mid-level coders can’t write reliable code either. This is why software engineering has, over decades, equipped itself with automated and semi-automated tools to ensure that code is production-ready.”

He further explained that LLMs are already being fine-tuned specifically for coding. Companies are investing hundreds of millions of dollars to enhance these LLMs’ coding capabilities because, if executed correctly, coding is the only use case where they can charge $10 or even $100 per million tokens from corporate clients.

One issue that the developers face is the belief that AI-generated code may contain bugs. Many AI startups are emerging to address these concerns. One such startup is YC-backed CodeAnt AI. 

CodeAnt’s AI-driven code review system can significantly reduce vulnerabilities by automating the review process and identifying potential issues before they reach the customer.

With advancements in AI-driven coding tools and platforms such as IBM watsonx and GitHub Models, there is no doubt that developers are now better equipped to handle legacy code, streamline code maintenance, and enhance productivity.

The post How Cursor AI, GitHub Copilot, Devin, and Amazon Q Help Reduce Technical Debt appeared first on AIM.

Much like the AI industry, sceptics can say the gaming industry is also alive because of hyped products. And currently, the ‘hype’ is around Black Myth: Wukong. 

The game developed by Game Science, which is backed by the Chinese tech giant Tencent and Hero Interactive, is nothing short of a visual masterpiece, which is powered by NVIDIA GPUs. At the same time, its success story can be compared to OpenAI’s ChatGPT and also teaches a lot about how investments should work in the startup landscape.

To set the record, Black Myth: Wukong sold over 10 million copies in just three days, becoming the fastest selling game, beating Elden Right, Hogwarts Legacy, and even Red Dead Redemption 2. This is highly reminiscent of OpenAI acquiring 1 million users in just five days, compared to Instagram reaching 2.5 months to reach 1 million downloads.

But apart from the users, the game also tells us about what investing in AI startups means. 

The Word is ‘Patient Capital’

Black Myth: Wukong took the company five years to make. Game Science was established in 2014 in Shenzhen by seven former Tencent Games employees. Before shifting their focus to Black Myth: Wukong in 2018, the startup released mobile games and the shift to making the AAA game only happened because of the rise of Steam users in China. 

At that time, the company had 13 employees and in August 2020, when Game Science unveiled the first trailer for the game to attract talent, received over 10,000 resumes, including applications from AAA gaming companies and international candidates willing to apply for Chinese work visas. 

The development team eventually grew to 140 employees.

In March 2021, Tencent acquired a 5% minority stake in Game Science, emphasising that their role would be limited to technical support, without influencing the company’s operations or decisions. Though the financial backing was there, the company received several controversies around the game’s content and technical problems due to the shift from Unreal Engine 4 to Unreal Engine 5.

Before Tencent and Hero Interactive’s investment, Game Science’s financial performance was largely dependent on the success of their mobile games. While these games were commercially successful, the studio’s primary goal was to develop high-quality console games, which required significant financial backing. 

Similarly, AI Takes Time

This is what patient capital stands for. Believing in what the startup is building and giving them years to develop their products. What Game Science did right with their journey was developing smaller revenue generating games along the way to make up for the cost of building Wukong, which is something that AI startups should focus on. 

Or maybe they need investors like Microsoft and YC, who believe in OpenAI.

Arjun Rao, partner at Speciale Invest, also told AIM a similar strategy when investing in R&D startups. He said that it is essential to be patient when investing as these startups are still in the budding stage, and placing the bet on the right founders is paramount. “Founders do not need to worry about the current downturn and keep a long-term mindset,” said Rao.

AI startups, just like games, require extensive patient capital from investors as they require extended periods of R&D before their innovations can be brought to market. That is what happened with Microsoft backing OpenAI, eventually resulting in the release of Chat`GPT.

OpenAI started laying the foundation for ChatGPT in the beginning of 2020 when they released GPT-3. Though a great technological marvel, the company wasn’t generating profits, and still isn’t even after two years of ChatGPT’s release in 2022. 

India is expected to have around 100 AI unicorns in the next decade. This wouldn’t be possible if investors do not trust the founders and pour money into R&D. Prayank Swaroop, partner at Accel, said that VCs are increasingly expecting AI startups to demonstrate rapid revenue growth.

“Even to the pre-seed companies, we say, ‘Hey, you need to show whatever money you have before your next fundraiser. You need to start showing proof that customers are using you.’ Because so many other AI companies exist,” said Swaroop. 

This is what investing in AI startups should look like. The game that took more than five years to make couldn’t have been possible if the investors were just looking for immediate profitability and not betting on the founders. Maybe, Indian investors need to relook at their investment strategy.

The post What Black Myth: Wukong’s Success Tells us About Investing in AI Startups appeared first on AIM.

Claude + Cursor = AGI. This is basically what the conversation is around coding right now everywhere on X. The hype around Cursor AI was just not enough, and then Andrej Karpathy added that he is choosing to use it instead of GitHub Copilot from now on. Is the future of coding in natural language already here?

Karpathy is the one who said more than a year back that English is the hottest programming language. Now for Cursor, he says, the future is ‘tab tab tab’ and that “The tool is now a complex living thing.” His support for Cursor even made people question if he is working with, or supporting, the team at Anysphere, the creators of Cursor, in some way.

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Further in the thread, he added that with the capabilities of LLM shifting so rapidly, it is important for developers to continually adapt the current capabilities. This argument has resonated by many developers that it is increasingly becoming difficult to catch up with the new tools in coding.

Is it now time to bring back the old days of learning how to code? A user replied to Karpathy saying that they are very grateful that they had time to learn computer science prior to the advent of AI tools. “…if I was new to programming I would be too tempted to skip actual learning in favour of more LLM usage, resulting in many knowledge gaps,” the user added.

Karpathy agreed that it is a very valid concern and he feels that “it’s slightly too convenient to just have it do things and move on when it seems to work.” This has also led to the introduction of a few bugs when he is coding too fast and tapping through big chunks of code.

But, Cursor AI is Here to Stay

Overall, all of the coding assistant tools have given productivity gains for organisations. Before Cursor, developers in companies were relying on GitHub Copilot for coding faster, which was overall reported to have increased the productivity of the teams. But it definitely brings the question of how people would learn coding from scratch from now on.

It is almost as if Cursor is bringing a ChatGPT moment for coding. Earlier, Ricky Robinett, VP of developer relations at Cloudflare, posted a video of his eight-year-old daughter building a chatbot on the Cloudflare Developer Platform in just 45 minutes using Cursor AI, documenting the whole process, even the spelling mistakes while giving prompts! Even Jeff Dean, chief scientist at Google DeepMind was fascinated by it.

“It is the future,” said several developers who reposted the video. People who started using Cursor have started implementing their own tricks. The most common one currently is using Claude 3.5 Sonnet with Cursor AI, as it allows people to use other open source LLMs and architectures such as Replit, Tailwind, React, Vercel, Firebase and many more, as well in their workflows.

Developers have built complex projects using Cursor in just hours, without even writing a single line of code. Plus, using LLMs, the code generator could also explain the use cases and meaning of the code, which in the end assists in learning how to code as well. “If you’re a technical founder, Cursor + Claude 3.5 Sonnet is a legit team of senior software engineers,” said Sahil Lavingia, founder of Gumroad.

Wake Up Call for Others?

Francois Chollet, the deep learning guru and creator of Keras, said that he is interested in watching developers stream while programming using Cursor and Claude and another developer who is really good at coding, and compare how generative AI has worked for the former one. 

Chollet had earlier also said that it would be great if someone could fully automate software engineering as he could move on to working on higher things, which is what Cursor AI is slowly ushering in. 

Meanwhile, there is another tool in the market called Zed, which is released in partnership with Anthropic, the creator of Claude, which several developers claim is better than Cursor and VS Code as well. 

The same was the case with GitHub Copilot and even Cognition Labs’ Devin. Cursor’s capabilities should be a wake up call for Microsoft to make VS Code integration with GitHub Copilot a lot easier. Basically, Cursor is also a glorified VS Code extension. 

Devin, on the other hand, is still to be released, which might create a new era of programming as well. Probably, replacing Cursor AI, or an entire software engineering team. 

It is clear that most of the upcoming generation of developers would want their code to be generated completely by AI, which GitHub Copilot in many instances was failing to do. But the issues with generated buggy code still exist with Cursor, which might get fixed with future iterations. 

The post The Future of Coding is ‘Tab Tab Tab’ appeared first on AIM.

Historically, running AI/ML workloads on Kubernetes has been challenging due to the substantial CPU/GPU resources these workloads typically demand. 

However, things are now changing. The Cloud Native Computing Foundation (CNCF), a nonprofit organisation that promotes the development and adoption of Kubernetes, recently released a new update, Kubernetes 1.31 (Elli). 

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Elli introduces enhancements designed to improve resource management and efficiency, making it easier to handle the intensive requirements of AI and ML applications on Kubernetes.

Enterprises are increasingly turning to cloud native applications, especially Kubernetes, to manage their AI workload. According to a recent Pure Storage survey of companies with 500 employees and more, 54% said they were already running AI/ML workloads on Kubernetes.

Around 72% said they run databases on Kubernetes and 67% ran analytics. Interestingly, the numbers are expected to rise as more and more enterprises turn to Kubernetes. This is because the development of AI and ML models is inherently iterative and experimental. 

“Data scientists continually tweak and refine models based on the evolving training data and changing parameters. This frequent modification makes container environments particularly well-suited for handling the dynamic nature of these models,” Murli Thirumale, GM (cloud-native business unit), Portworx at Pure Storage, told AIM.

Kubernetes in the Generative AI Era

Kubernetes marked its 10th anniversary in June this year. What started with Google’s internal container management system Borg, has now become the industry standard for container orchestration, adopted by enterprises of all sizes. 

The containerised approach provides the flexibility and scalability needed to manage AI workloads.

“The concept behind a container is to encapsulate an application in its own isolated environment, allowing for rapid changes and ensuring consistent execution. As long as it operates within a Linux environment, the container guarantees that the application will run reliably,” Thirumale said.

(Source: The Voice of Kubernetes Expert Report 2024)

Another reason AI/ML models rely on containers and Kubernetes is the variability in data volume and user load. During training, there is often a large amount of data, while during inferencing, the data volume can be much smaller. 

“Kubernetes addresses these issues by offering elasticity, allowing it to dynamically adjust resources based on demand. This flexibility is inherent to Kubernetes, which manages a scalable and self-service infrastructure, making it well-suited for the fluctuating needs of AI and ML applications,” Thirumale said.

NVIDIA, which became the world’s most valuable company for a brief period, recently acquired Run.ai, a Kubernetes-based workload management and orchestration software provider. 

As NVIDIA’s AI deployments become more complex, with workloads distributed across cloud, edge, and on-premises data centres, effective management and orchestration get increasingly crucial. 

NVIDIA’s acquisition also signifies the growing use of Kubernetes, highlighting the need for robust orchestration tools to handle the complexities of distributed AI environments across various infrastructure setups.

Databases Can Run on Kubernetes

Meanwhile, databases are also poised to play an important role as enterprises look to scale AI. Industry experts AIM has spoken to have highlighted that databases will be central in building generative AI agents or other generative AI use cases.

As of now, only a handful of companies are training AI models. Most of the remaining enterprises in the world will be finetuning their own models and will look to scale with their AI solutions very soon. Hence, databases that can scale and provide real-time performance will play a crucial role.

“AI/ML heavily rely on databases, and currently, 54% of these systems are run on Kubernetes—a figure expected to grow. Most mission-critical applications involve data, such as CRM systems where data is read but not frequently changed, versus dynamic applications, like ATMs that require real-time data updates. 

“Since AI, ML, and analytics are data-intensive, Kubernetes is becoming increasingly integral in managing these applications effectively,” Thirumale said.

Replacement for VMware

Broadcom’s acquisition of VMware last year also impacted the growing usage of Kubernetes. The acquisition has left customers worried about the pricing and integration with Broadcom.

“It’s a bundle, so you’re forced to buy stuff you may not intend to,” Thirumale said. 

Referring to the survey again, he pointed out that as a result around 58% of organisations which participated in the survey plan to migrate some of their VM workloads to Kubernetes. And around 65% of them plan to migrate VM workloads within the next two years. 

Kubernetes Talent 

As enterprises adopt Kubernetes, the demand for engineers who excel in the technology is also going to increase, and this will be a big challenge for enterprises, according to Thirumale.

“Kubernetes is not something you are taught in your college. All the learning happens on the job,” he said. “The good news is senior IT managers view Kubernetes and platform engineering as a promotion. So let’s say you’re a VMware admin, storage admin, if you learn Kubernetes and containers, they view you as being a higher-grade person,” he said.

When asked if education institutions in India should start teaching students Kubernetes, he was not completely on board. He believes some basics can be taught as part of the curriculum but there are so many technologies in the world.

“Specialisation happens in the industry; basic grounding happens in the institutions. There are also specialised courses and certification programmes that one can learn beyond one’s college curriculum,” he concluded.

The post How Generative AI is Fueling Demand for Kubernetes appeared first on AIM.

Since traditional video shoots are expensive and time-consuming, many enterprises have been turning to generative AI to craft their promotional videos. However, even state-of-the-art models like Sora and Kling frequently produce inaccurate results and concerns regarding copyright infringement and overall quality still persist.

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Today, there are a handful of startups providing tools and solutions for enterprises to create AI videos that enterprises can use. Bengaluru-based AI startup Unscript is one among them.

The startup recently transformed a single photo into a full-fledged video, generating head and eye movements, facial expressions, voice modulations, and body language, achieving studio-quality results in under 2 minutes. This significantly reduces manual shooting efforts. 

Interestingly, the startup claims that their model has surpassed OpenAI’s Sora, Google Vlogger, Microsoft’s VASA-1, and Alibaba’s EMO, making it an ideal choice for brands, marketing agencies, and virtual influencers. 

Over 50 leading companies, including Ceat and Mahindra, are already leveraging its cost-effective and scalable video-production capabilities.

“We have built a ‘Canva for videos’, but in a version where you can get an end-to-end solution. Starting from shooting to the final video that you want to deliver to social media, everything can be automated here,” said co-founder Ritwika Chowdhury in an exclusive interview with AIM.

Unscript provides advanced video-generation solutions, including text-to-video, image-to-video, and the creation of virtual influencers as brand ambassadors for enterprises. 

The company is experiencing strong demand from sectors such as BFSI, pharma, and media & entertainment. 

Is Unscript Better Than Sora? 

The startup has built its own diffusion model for converting text to video. According to Chowdhury, their model follows diffusion principles but is distinct in its architecture. 

“It’s an encoder-decoder plus diffusion-based model that we have developed. A key aspect is that we have collected substantial data—about 1,000 hours—to train it,” said Chowdhury.

OpenAI’s Sora tends to generate highly creative videos that are not commonly seen in the real world, such as dolphins cycling in the ocean. Unscript’s model, however, is trained specifically to generate content based on humans.

“Sora’s videos tend to be very abstract. You might generate something like a dog playing with a ball, but we specialise in creating content featuring human beings.

“If you look at Sora, you’ll see that physical interactions with the world are not mapped properly. For example, a person walking might appear to be floating or not interacting realistically, as it is not specifically trained for human-like interactions.”

Talking about Unscript, she said, “Our video tool is perfect at not only generating lip sync, but generating it based on the individual. This is important because we are working with a lot of enterprise customers, like Ceat, Mahindra, Bajaj, Maxlife, Flowworks, and Healthifyme.”

For script generation, Unscript uses third-party LLM vendors like OpenAI. “For the LLM component, especially in documents and videos where end-to-end script generation is needed, we’ve trained and fine-tuned with 1 million ad copies,” said Chowdhury.

She also mentioned that they train their models using proprietary data that they have collected, as open-source datasets often do not cover all types of ethnicities. “We literally hired 30 people last year, and for six months, we focused solely on collecting data,” she said.

Meanwhile, Unscript also supports content generation in over 40 languages. The team has published more than 30 research papers and has researchers from Samsung Research, Microsoft, Intel, and various IITs. Moreover, the company is advised by a former employee of OpenAI.

Targeting Enterprises

Chowdhury also cautioned about certain issues with Luma and Sora. “You cannot maintain the brand image consistently in all the clips. When creating enterprise content, you need to have logos, colours, and other elements presented in a specific way. Luma and Sora are not built for enterprise videos,” she said.

Unscript provides its customers with unlimited video generation. “We only charge for the final videos that you use, not for the R&D you might need to do,” Chowdhury said.

Businesses use the platform’s services to create diverse content, from YouTube videos to marketing assets and customer communications. BFSI companies use Unscript to produce short explainer videos as a more engaging alternative to traditional policy documents.

Future Roadmap 

Chowdhury revealed that her journey with generative AI started in 2014 while she was at IIT Kharagpur. Founded in 2021 by Chowdhury and Apurv Jain, Unscript has since raised over $1.25 million. 

The company does not plan to raise funds in the near future. Chowdhury noted that they are continually experimenting with new products and are preparing to release a new feature, which will be announced soon.

While Unscript is operating in an interesting space, there are other companies excelling in that space as well. 

The startup’s competitors include major names like OpenAI’s Sora, Kling, Runway ML, and Luma AI’s Dream Machine. Locally, InVideo, Phenomenal AI and Personate AI are also notable rivals, with Personate AI having developed AI anchors Krish and Bhoomi for Doordarshan.

The post This Bengaluru Startup is Competing with OpenAI Sora Heads-on appeared first on AIM.

Are software engineering roles disappearing? Not exactly. The apparent decline in job postings might just be a shift in titles—think ‘Generative AI Leader’ instead of ‘Software Engineer’. And now, as if on cue, everyone has become a generative AI expert and leader. Not just Silicon Valley, it’s the same in Bengaluru too. 

Vaibhav Kumar, senior director of AI/ML at AdaSci, pointed out this phenomenon in a LinkedIn post. “In Bangalore these days, if you randomly toss a stone, odds are it will land on a Generative AI Leader—earlier, they used to be software engineers, but now they seem to be on the brink of extinction.”

It is true that there are a slew of new jobs that are being created because of generative AI such as AI entrepreneur, chief AI officer, AI ethicist, AI consultant, and at least 50 more. But it has also given rise to people who simply call themselves ‘generative AI leaders’.

Everyone’s an AI Engineer

Kumar’s point is that now everyone is calling themselves an expert in AI as the barrier to entry is significantly lower. But then not everyone can be a good GenAI leader. Vishnu Ramesh, the founder of Subtl.ai, said that the only way to find a good generative AI leader is to ask them how many generative AI projects they have driven and whether these projects actually benefited the organisation. 

“The number of chatbots built will soon overtake Bangalore traffic,” Shashank Hegde, data science and ML manager at HP, said in jest, implying that with every company experimenting with generative AI in use cases, most of them are coming up with chatbots on their systems, which, honestly, people are not very fond of. 

Ramesh and Hegde’s points found takers. More engineers in the discussion described how their team’s ‘generative AI leaders’ were not able to not perform basic tasks of machine learning, and were mostly experts on data science, not generative AI really. “A Rs499 course from Udemy or Coursera is delivering GenAI leaders very rapidly,” commented Chetan Badhe.

AIM had earlier reported that fancy courses from new organisations are creating jobs for the future, but also causing freshers to search for jobs that are not there in the market. “GenAI leaders don’t know what’s bidirectional in BERT,” added another user.

Meanwhile, a recent IBM study found out that around 49% of Indian CEOs surveyed said they were hiring for GenAI roles that didn’t exist last year. Also, 58% of Indian CEO respondents say they are pushing their organisation to adopt generative AI more quickly than some people are comfortable with. 

This makes it clear that generative AI is the leading focus for companies, although for some, it’s more about appearances than substance. 

Moreover, getting ‘generative AI’ on your profile can also boost your salary by up to 50%. AIM Research noted that the median salaries of generative AI developers and engineers ranged between INR 11.1 lakh and 12.5 lakh per annum.

It just makes sense to call yourself a generative AI leader if you are already working in the software engineering field. But getting upskilled in the field to be credible is also important. 

Bengaluru is All About AI

Just like everyone is “thrilled” on LinkedIn, everyone is doing something in AI in Bengaluru. Someone correctly pointed out: If LinkedIn was a city, it would definitely be Bengaluru. The city’s AI hub, HSR Layout, was recently looking for a chief everything officer, someone who can perform a plethora of tasks all alone. 

And it is indeed true that most of the software engineering is becoming all about generative AI because of the trend and hype. Earlier, Bangalore was filled with software engineers from IT industries and startups; now they have slowly turned to generative AI leaders. Some are even influencers on X or LinkedIn. 

At the same time, Bengaluru’s AI culture is also giving rise to 10x engineers, who are able to do the task of 10 people using generative AI. Some even argue that there is no need for a computer science degree anymore to get into AI. It is definitely time to rewrite your resume and say you are a ‘generative AI leader’.

The post Toss a Stone in Bangalore and It will Land on a Generative AI Leader appeared first on AIM.

Indian commerce minister Piyush Goyal recently criticised e-commerce platforms like Amazon for using predatory pricing strategies, warning that such practices could harm local businesses and the Indian economy. The minister’s statement plays in favour of Bhavish Aggarwal, who recently announced his grand plan to disrupt the Indian e-commerce ecosystem.

Aggarwal supported Goyal, saying, “At Ola, we’re building the future of commerce with ONDC, which will enable kiranas and small merchants to reach consumers through digital networks. ONDC is the future.”

This comes after he declared ONDC “the UPI moment for e-commerce” at the recent Ola Sankalp 2024 event.

At the same event, he also announced that the company would change its name from Ola Cabs to Ola Consumer, as it expands its services beyond cabs. “We are going to offer a much broader suite of consumer services. Many of you have already used some of these. As I said, our ambition is to truly make commerce accessible, affordable, and efficient,” said Aggarwal.

For the same, Ola Consumer is integrating with ONDC to reduce the cost of commerce and expand service categories. Currently, Ola users can order food and beverages via ONDC through the Ola app, with plans to expand to groceries and fashion items. 

The company has also integrated a food delivery plugin within its app, enabling users to access a variety of restaurants and food brands listed on ONDC. This feature is in the pilot phase, available to Ola employees and a select group of consumers.

Aggarwal also plans to leverage the power of ONDC to take on the likes of Zepto and Blinkit. The company plans to implement fully automated dark stores and fulfilment centres to revolutionise warehousing and improve the commerce supply chain.

In addition, Ola is focusing on sustainable logistics by electrifying its delivery operations, which is expected to reduce logistics costs by about 50% and create additional jobs.

Can AI Help ONDC Achieve Its UPI Moment?

To make it easier for customers to buy products and improve discoverability for sellers, Ola has also introduced an AI shopping co-pilot on ONDC. “Your shopping experience on digital platforms is not linear and static. Imagine if there were an AI co-pilot guiding you along the way, personalising things, talking to you, and understanding your needs in real-time,” said Aggarwal.

Several major companies have joined ONDC to expand their market reach. These include Hindustan Unilever, ITC, Nestlé, PepsiCo, Dabur India, Godrej Consumer Products, Marico, and Tata Chemicals. 

Interestingly, Ola is not the only company banking on AI to turn ONDC into India’s e-commerce giant. Likewise, other contributors are pushing the boundaries of AI on ONDC. Plotch.ai, a Google-backed startup is also currently working with ONDC to build AI infrastructure and simplify e-commerce for consumers. 

The company has developed an AI-powered conversational commerce app featuring multilingual, voice-enabled semantic search and robust image search capabilities.

“Multilingual voice-based conversational commerce is one piece of the AI that we’re building,” said Manoj Gupta, the founder of Plotch.ai, in an exclusive interview with AIM. “For instance, if you’re looking to buy a saree, jewellery, or a T-shirt and want to find the most affordable options, you can simply ask the AI, and it will sort them for you,” he explained.

Recently, another Indian startup Sarvam AI recently introduced voice-based AI agents.

The cost of these agents starts at just one rupee per minute. According to co-founder Vivek Raghavan, enterprises can integrate these agents into their workflow without much hassle.

“These are going to be voice-based, multilingual agents designed to solve specific business problems. They will be available in three channels – telephony, WhatsApp, or inside an app,” Raghavan told AIM in an interaction prior to the event.

These agents could be integrated into contact centres and used for various applications across multiple industries, including insurance, food and grocery delivery, e-commerce, ride-hailing services, and even banking and payment apps. Raghavan further told AIM that Sarvam AI is working closely with Beckn Protocol, the underlying layer behind ONDC.

ONDC Is a Baby, Let it Grow

“ONDC is a very young, small baby, so we should let it grow. And I’m pretty sure that by 2030, we will see 100 million to 200 million transactions happening a month,” said Pramod Varma, former chief architect of Aadhaar, in an exclusive interview with AIM, when asked why ONDC is not seeing UPI-like success.

“ONDC is much broader. On ONDC, you will see taxi bookings happening through Yatri, metro ticketing being integrated, and physical goods like grocery commerce being added. Food delivery is also starting to kick in. So, it’s what’s called multi-sectoral commerce. It does take a little more complexity to unravel,” explained Varma.

ONDC logged a 21% month-on-month growth in transactions to 12 million in July 2024, compared to 10 million a month ago. Nationwide, ONDC handles 60,000 food orders daily, capturing 3% of the total order volumes managed by Swiggy and Zomato across India.

ONDC is also set to integrate nearly all metro services into its network by next year, according to MD and CEO Thampy Koshy. Currently, India has a operational metro network spanning 905 kilometres across over 20 cities, with the Kochi and Chennai metros already partnering with ONDC to offer ticketing services through platforms like Namma Yatri, Rapido, and redBus. 

Koshy said, “We are talking to every metro to become ONDC participants. Some of the talks are in an advanced stage. By the end of the coming year, all metros are likely to be part of ONDC.”

The post ONDC’s ‘UPI Moment’ for E-Commerce Has Arrived  appeared first on AIM.

The AI market is currently crowded with various models, as major players such as OpenAI, Meta, and Google continuously refine their offerings. However, the question remains: which models will developers choose, and is it feasible for AI startups and big tech companies to offer these models for free?

“If you’re only selling models, for the next little while, it’s gonna be a really tricky game,” said Cohere founder Aidan Gomez in a recent interview. By selling models, he meant selling API access to those AI models. OpenAI, Anthropic, Google, and Cohere offer this service to developers, and they all face a similar problem.

“It’s gonna be like a zero margin business because there’s so much price dumping. People are giving away the model for free. It’ll still be a big business, it’ll still be a pretty high number because people need this tech — it’s growing very quickly — but the margins, at least now, are gonna be very tight,” he explained. 

Interestingly, OpenAI just made $510 million from API services while it made $1.9 billion from ChatGPT subscription models.

Gomez hinted that Cohere might offer more than just LLMs in the future. “I think the discourse in the market is probably right to point out that value is occurring both beneath, at the chip layer—because everyone is spending insane amounts of money on chips to build these models in the first place—and above, at the application layer.”

Recently, Adept was acqui-hired by Amazon, Inflection by Microsoft, and Character.ai by Google. “There will be a culling of the space, and it’s already happening. It’s dangerous to make yourself a subsidiary of your cloud provider. It’s not good business,”said Gomez.

Sully Omar, Co-founder and CEO of Cognosys, echoed similar sentiments and said, “It won’t be long until we see options like ‘login’ with OpenAI/Anthropic/Gemini. In the next 6-8 months, we’re likely to see products that use AI at a scale 100 times greater than today.” 

He added that from a business standpoint, it doesn’t make sense to upsell customers on AI fees. “I’d rather charge based on the value provided,” he said.

Omar noted that the current system, which relies on API keys, is cumbersome for most users. “90% of users don’t understand how they work. It’s much easier for users to sign into ChatGPT, pay for compute to OpenAI/Gemini, and then use my app or service at a lower price,” he explained. 

He also criticised the credits-based pricing model, suggesting that it is ineffective as it requires constantly managing margins on top of LLM fees.

The rise of LLMs has ignited another debate: will generative AI lead to more APIs or the end of APIs?

“The AI model market is mirroring the early days of cloud computing, where infrastructure (IaaS) was a low-margin game. As cloud providers realised, value creation shifted towards higher-margin services like SaaS and PaaS, layering specialised applications on top of core infrastructure,” said Pradeep Sanyal, AI and ML Leader at Capgemini. 

“AI startups must move beyond selling raw models to offering differentiated, application-focused solutions,” he explained. 

Google and OpenAI Compete for Developer Attention

OpenAI recently announced the launch of fine-tuning for GPT-4o, addressing a highly requested feature from developers. As part of the rollout, the company is offering 1 million training tokens per day for free to all organisations through September 23.

The cost for fine-tuning GPT-4o is set at $25 per million tokens. For inference, the charges are $3.75 per million input tokens and $15 per million output tokens. Additionally, GPT-4o mini fine-tuning is available to developers across all paid usage tiers. 

This development comes after Google recently reduced the input price by 78% to $0.075/1 million tokens and the output price by 71% to $0.3/1 million tokens for prompts under 128K tokens (cascading the reductions across the >128K tokens tier as well as caching) for Gemini 1.5 Flash. 

Moreover, Google is giving developers 1.5 billion tokens for free everyday in the Gemini API. The Gemini 1.5 Flash free tier includes 15 requests per minute (RPM), 1 million tokens per minute (TPM), and 1,500 requests per day (RPD). Users also benefit from free context caching, allowing up to 1 million tokens of storage per hour, as well as complimentary fine-tuning services.

Logan Kilpatrick, Lead at Google AI Studio, said that they are likely to offer free tokens for the next several months.

Meanwhile, OpenAI recently launched GPT-4o mini, priced at $0.15 per million input tokens and $0.60 per million output tokens. This model is significantly more affordable than previous frontier models and over 60% cheaper than GPT-3.5 Turbo. The GPT-4o mini retains many of GPT-4o’s capabilities, including vision support, making it suitable for a broad range of applications.

Additionally, OpenAI has reduced the price of GPT-4o. With the new GPT-4o-2024-08-06, developers can save 50% on input tokens ($2.50 per million) and 33% on output tokens ($10.00 per million) compared to the GPT-4o-2024-05-13 model.

​​https://x.com/ofermend/status/1822783034296512597

Meta’s Llama 3.1 is a Game Changer

According to Harneet Singh, founder of Rabbitt AI, Meta’s latest model, Llama 3.1 70B, is the most cost-effective option, priced at $0.89 per million tokens while offering capabilities similar to OpenAI’s GPT-4o. “This cost-benefit ratio makes it an attractive choice for budget-conscious enterprises,” he said.

The company used  Groq’s hosted APIs for Llama 3.1.

Llama 3.1  70B model has an input token price of $0.59 per million tokens and an output token price of $0.79 per million tokens, with a context window of 8,000 tokens on Groq.

In contrast, the 8B model features a more affordable pricing structure, with input tokens costing $0.05 per million and output tokens priced at $0.08 per million, also with a context window of 8,000 tokens.

In comparison, the inference of Llama 3.1 405B costs $3 per 1M tokens and $5.00 per million output tokens on Fireworks. 

“I don’t think it’s possible to make it cheaper without some loss in quality. GPT-4o mini offering a comparable quality costs $0.15 per 1M input tokens and $0.6 per 1M output tokens (and half this price when called in batches),”said Andriy Burkov, machine learning lead at TalentNeuron.

“The math here is broken. Either OpenAI managed to distill a ~500B parameter model into a ~15B parameter model without a quality loss or they use crazy dumping. Any ideas?,” he pondered.

Conclusion

While open-source models like Llama 3.1 70B offer remarkable cost efficiency, proprietary models such as GPT-4o deliver unparalleled quality and speed, albeit at a higher price point. 

GPT-4o provides the most comprehensive multimodal capabilities, supporting text, image, audio, and video inputs. It is suitable for applications requiring diverse input types and real-time processing.

Gemini 1.5 Flash integration with Google’s ecosystem can be a significant advantage for businesses already using Google’s services, offering seamless integration and additional functionalities.

The choice of model thus largely depends on the specific needs and budget constraints of the enterprise.

The post Selling AI Models Is Turning into a Zero-Margin Business appeared first on AIM.

A lot goes into making the videos we consume online—brainstorming ideas, writing scripts, editing, and recording voice-overs—and all of this consumes a substantial amount of the content creators’ time. 

This is where generative AI can step in to ease the burden. It can be a great tool for the creators to streamline these processes and reduce the time spent on routine tasks.

Imagine an AI tool that lets you accomplish everything at one place with just a few prompts. InVideo AI promises exactly that, which is why content creators around the globe are falling in love with the platform.

According to Sanket Shah, co-founder and CEO of InVideo, the platform gets nearly 3 million new users every month. 

“We don’t track the total number of people using our free product, but if we take 3 million on average, we could have around 35 million users on our platform in just one year. In terms of paid users, we have about 150,000 of them,” he said.

AIM met Shah in early August in the Bengaluru edition of AWS GenAI Loft, a collaborative gathering of developers, startups, and AI enthusiasts to learn, build, and connect.

Shah revealed that the startup has already secured over 50% of its $30 million revenue target for the year.

Avoiding the Uncanny Valley

Interestingly, InVideo has not developed a text-to-video model like OpenAI’s Sora or Kling. Instead, the startup has partnered with several media providers like Getty Images and Shutterstock and pays them a licence fee. 

One primary reason for this approach, according to Shah, is that InVideo wants to provide users with publishable videos. 

Currently, even though models like Sora produce high-quality videos, there is limited clarity about the datasets they are trained on, which complicates the publishability of these videos.

“Moreover, at InVideo, one of our core principles is to avoid anything that falls into the uncanny valley. As of last week, we felt that generative image and video technology still often produced results with issues like extra fingers or multiple eyes—things that are not acceptable for our purposes. 

“We focus on delivering high-quality, publishable videos, so we prioritise ensuring that our users receive content that meets professional standards,” Shah said. 

The platform leverages AI to understand the user’s intent, write script, handle voice-overs—practically cloning the user’s voice. 

It selects and integrates media, performs editing tasks, adds music, and ensures that all elements like transitions and zooms are correctly applied, effectively automating much of the post-production process.

However, the startup does not shy away from leveraging models like Sora. Once available, they could contemplate integrating Sora and Kling into their platform. However, they are not in the business of building models.

“We don’t want to enter into a race with the hyperscalers (model builders) to build the next-big model. Models are also perishable and we have seen that already,” he said.

The AI in InVideo 

While the startup is refraining from entering the territory of model builders like OpenAI, Anthropic, Microsoft, and Google, it is building models that suit its business model.

“We prefer to focus on developing models that are niche and highly specific to our needs. For instance, we are working on a lip-sync model tailored to our requirements,” Shah revealed.

The startup also plans a new AI-powered feature next month, which allows users to create an avatar or a digital clone of themselves. 

“Here’s how it works– you record a short video, speaking for 30 seconds to a minute, and the AI generates an avatar. Once you have your avatar, you can input a prompt or specify what you want it to say,” Shah revealed.

The platform does leverage LLMs from Anthropic, OpenAI and Google, but Shah refrained from revealing much about their use cases. “This is very proprietary and that is where most of the magic happens.”

InVideo also leverages Amazon Bedrock, which gives them access to some of the top LLMs through a single API. 

Moreover, the startup also leverages AWS’ multi-region fleet of Spot GPUs for video rendering and editing on open-source solutions, allowing them to run 90% of their workload on Spot instances, which enables close to 40% cost reduction.

Enabling Content Creators with AI

The startup started with a pre-AI product in 2017 and was initially focussed on enterprises. However, pivoting to AI and to a more B2C business model from B2B proved to be a game changer.

Today, it caters to YouTubers and established and new content creators creating content for Facebook, Instagram, and TikTok. 

“The platform is also leveraged by small businesses, for example, a lady selling horses in Texas, a partially deaf teacher in Palo Alto who is teaching a community college, a bunch of students and teachers, someone selling water bottles, and some restaurants,” Shah revealed. 

“About 5% of our customers are also filmmakers.”

When AIM asked Shah about some of the most fun and interesting videos he has seen users generate using the platform, he revealed that the brand manager of the legendary rock band Aerosmith used InVideo to generate content on how to deal with depression.

“One day, the brand manager received an email from a viewer who was contemplating suicide. After watching the video and following some advice, they decided against it. Stories like these are incredibly powerful,” Shah revealed.

The post Why are Content Creators Falling in Love with This AI Startup? appeared first on AIM.

Australian company Savage Interactive which developed Procreate, a digital painting and illustration app, recently announced that they will not be incorporating generative AI into their offerings.

“I prefer that our products speak for themselves. I really f***ing hate generative AI. I don’t like what’s happening in the industry, and I don’t like what it’s doing to artists. We’re not going to be introducing any generative AI into our products,” said CEO James Cuda.

Ironically, CUDA is also the name of one of NVIDIA’s most popular tools for parallel computing and GPU connectivity, and plays a crucial role in enabling generative AI today.

“Our products are always designed and developed with the idea that a human will be creating something. You know, we don’t exactly know where this story is going to go or how it ends, but we believe that we’re on the right path supporting human creativity,” he added. 

This statement is bold given that the industry is increasingly adopting generative AI. Adobe, Procreate’s main competitor, is actively integrating generative AI into its creative tools to boost creativity and productivity.

“Is this the beginning of the end for Procreate? Hating on generative AI and clearly admitting that they’re never going to integrate AI features oh boy. As he said, he doesn’t know how it will end, but one thing is sure: it won’t end well,” said AI expert Ashutosh Shrivastava on X. 

Many in the artist community have wholeheartedly supported Procreate’s stance on generative AI. However, not everyone is aligned. “People aren’t looking at this from a business perspective. Right now a decent subset of artists hate AI, so it makes sense to try and target that market if it’s large enough,” posted a user on Hacker News. 

“If artists suddenly started loving AI tomorrow, this pledge would be out the window. It’s just business and marketing – nothing more, nothing less,” he explained. 

This development has certainly pleased artists, but for how long? Generative AI is set to become a crucial tool for creating new art. For instance, today, a majority of social media is filled with AI-generated images, and recently, we’ve seen the impressive capabilities of Flux integrated into Grok 2, which can create remarkably realistic images.

Artists Should Embrace Generative AI

Artists should not feel disheartened about using generative AI. Art has always evolved with the emergence of new technologies. Just as digital art emerged in the early 2000s and made the lives of graphic designers easier, generative AI is set to do the same. 

It also allows non-artistic individuals to experiment with art and create something new. For instance, now even an amateur can create AI-generated videos without any prior knowledge of filmmaking.

“I love artists. I have friends and family making a living in the arts. I went to art school. The artists who aren’t using generative AI to accelerate their process are, IMO, going to go extinct. Especially collaborative art,” posted a user on X. 

“AI art is real art, and there’s no shying away from this statement,” declared a 19-year-old artist who faced criticism for selling AI-generated artwork on Church Street in Bengaluru. 

Speaking to AIM, Ashok Reddy, a graphic designer at GrowthSchool, said, “It wasn’t a task I completed in one day; it was a collection of efforts over many months.” He emphasised that his images were original, generated from scratch, and not copied from any other creator or existing works. 

In a different approach to the AI art scene, David Sandonato, an Italian digital artist, began selling Midjourney prompt catalogues on PromptBase, a marketplace for AI art prompts. Today, he is the top-ranked artist on the platform, offering a library of 4,000 to 5,000 prompts, with new uploads daily.

In a recent interview, Santonato said, “It began as a side hustle, but I’m convinced that this business has big space to grow when people will realise that today 50% of the images available in the top microstock agencies can be generated in full quality with a good prompt.” 

Recently, self-proclaimed career guru Priyank Ahuja shared an intriguing post on X, that read, “ChatGPT and Canva will help you earn an extra $15,000/month.” He followed it up with a series of video demonstrations on how to use the tools for simple tasks like designing T-shirts, creating creative Instagram ads, and making YouTube Shorts.

Another user on Reddit said that he haa seen AI artists make $1000 a month selling adult themed content. “AI art is a different animal, and making money with it is going to look different than the traditional art community,” he said. 

AI artists around the world are gaining a lot of prominence. Refik Anadol, a Turkish-American new media artist, has captivated audiences worldwide with his work at the intersection of art and artificial intelligence at NVIDIA GTC earlier this year. 

Moreover, oftentimes, when an artist posts something generated using AI, many dismiss it as not being real art and offer a great deal of unwarranted criticism.

“As for the pro-AI community, we don’t have to tolerate aggressive behaviours and continual hyper-protective mentalities; you do have the right to show your work freely and without hate. Yes, you should develop your visual style in your work, but you should also be free to express love and passion for people with whatever tools you want. That is true inclusivity for everyone to learn to do,” posted one pro-AI artist on Reddit.

He argued that individuals often self-punish instead of seeking tools to address their weaknesses, develop their foundational skills, and enhance their artistic abilities, which he feels is unnecessary.

In India, designers often pursue a BDes or BFA degree, which offers thorough training in Adobe Creative Suite. With the growing prominence of generative AI, it’s crucial for designers to also learn these new skills, as they can greatly boost productivity.

In-house graphic designers at AIM also feel that with generative AI features, it has become increasingly easy to autofill and regenerate images, tasks that previously took a considerable amount of time.

A major concern artists have with generative AI is that it relies on data from the internet without adequately crediting them. While this is a legitimate issue, simply avoiding generative AI is not the answer. Beethoven.ai, an Indian generative AI music company, is leading the way by paying royalties to the artists whose music is used to train its models.

Similarly, Adobe is reportedly compensating artists and photographers for providing images and videos to train its artificial intelligence models. According to the report, Adobe pays between ¢6 and ¢16 per photo and an average of $2.62 per minute of video.

Competitors Are Betting Big on Generative AI

Procreate competitors are actively integrating generative AI into their products and services. 

Adobe’s generative AI platform, Firefly, offers capabilities such as text-to-image, which allows users to generate images from text prompts, expanding creative possibilities in applications like Photoshop. It also includes generative fill, enabling users to seamlessly add or remove elements from images, and generative shape fill and remove, which provide options to fill vector outlines and eliminate unwanted elements from images.

Similarly, Canva is pretty much bullish on generative AI. The Australian design company recently acquired Leonardo, a startup renowned for its generative AI content and research. The company caters to diverse industries such as fashion, advertising, and architecture by developing AI models for image creation. Some people call it the biggest competitor to Midjourney.

Canva has also introduced Canva Magic Media, which allows users to create images and videos from text prompts. Currently, around 180 million users worldwide use Canva. On the other hand, Procreate has over 30 million users.

P.S. The banner for this article was not created using generative AI.

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Occasionally, the AI research field becomes heated, not due to new launches but because of debates about who is wrong or who is undermining each other’s research. This time, it was Yann LeCun pointing fingers on Gary Marcus, Grady Booch, and Michael J Black for killing Meta’s Galactica.

It all started with the launch of Sakana AI’s AI Scientist, which the company claims is the first AI system for automating scientific research paper and open-ended discovery. This seems very similar to Galactica, an LLM trained on scientific knowledge for the scientific field. However, it was taken down because researchers were concerned about its outputs, which could be unreliable and prone to hallucination.

“Will the AI negativists who killed Galactica by dousing it with vitriol kill this one too?” asked LeCun, further questioning Booch, Marcus, and Black, and others, “will rescind their prophecies of LLM-powered doom for the scientific community?”

Just weeks after Galactica, ChatGPT was released. “Contrary to their predictions, the scientific publication system was not hurt by the availability of LLMs, let alone destroyed. If anything, it was actually helpful,” said LeCun.

What are these ‘Prophecies of Doom’?

Black, who is the director of Max Planck Institute for Intelligent Systems, had raised questions about the reliability of Galactica and similar products during the launch. 

Back in November 2022, Black posted a thread of all the problems with Galactica and said, “I applaud the ambition of this project but caution everyone about the hype surrounding it. This is not a great accelerator for science or even a helpful tool for science writing. It is potentially distorting and dangerous for science.”

Black replied to LeCun saying that his criticism was genuine. “I’m sorry but the truth is that *YOU* killed it, not me,” said Black. He mentioned that LeCun promoted Galactica as, “Type a text and galactica.ai will generate a paper with relevant references, formulas, and everything,” which according to Black is a big claim to make.

Thus, when Black conducted the test, Galactica fell short of his expectations. “It made up realistic looking references. And, it did so in a way that sounded authentic,” he added, saying that Meta did the right thing by taking it down as it did not live up to the hype around it.

Marcus, a Professor of Psychology and Neural Science at NYU, is a popular critic of deep learning and AGI, also took to X to state that Galactica got his birthday, education as well as research interests wrong. Nearly 85% of the results presented by Galactica about Marcus were not true.

Similarly, Booch had said, “Galactica is little more than statistical nonsense at scale,” adding adjectives like amusing, dangerous, and unethical.

As for the ‘prophecies of doom’, Black had done a thorough analysis of the problem with LLMs then, and said there should be new ethics rules for scientific publication. He highlighted issues that there can be a flow of fake scientific papers, in an industry which completely relies on peer reviews and public trust. He said that he also uses LLMs for research and there should be further research on LLMs instead of slowing it down and building safeguards to defend the public trust in science.

LeCun doesn’t agree. He replied to Black saying that the negative reaction from a scientist led to shutting down of Galactica. “In simple terms, *YOU* killed it,” said LeCun, asking him if the Galactica demo would have made the world a better place.

So, Who Killed Galactica?

Black believes that Meta, as an organisation, should have been more careful when releasing Galactica, even as a demo, and LeCun should accept the scrutiny faced for promotion of the product. 

“You will likely say that this was just a “demo” and not a “product”. Do you think the public really differentiates these when it comes from Meta, has a slick website, and is heavily hyped?” asked Black. Which makes sense. Since Meta released it as a software and not a research publication, it puts Meta under the spotlight, specifically when the claims were not adding up. 

Though Black appreciates LeCun and Meta’s efforts towards science and open source AI, he said that Meta is not a university, thus receives a lot of attention from the world, not just academia, which makes it riskier.

Today, the weights of Galactica are still up for academic research, but the product is not available for direct use. “That was a good decision. It shifted the focus away from Galactica being a Meta “product” to it being a research prototype,” said Black. 

Maybe that is also why LeCun had invested in Perplexity AI, which often sells itself as a research focused search engine.

Probably, Black is right. With the advancements in LLMs and people getting accustomed to them in the real world, shutting down Galactica back then sounded like a decent step to take. He didn’t kill Galactica, his feedback was strong enough that Meta had to kill it.

However, now seems like a good time to finally bring back Galactica.

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Google just concluded its Made By Google event this year with a new update on Project Astra, which also gained the limelight at Google I/O 2024. 

The company shared its plans of building advanced seeing and talking responsive agents with Project Astra. 

At the Pixel 9 launch, Rick Osterloh, SVP of platforms and devices at Google, concluded that among the first places where Project Astra will come to life will be Gemini Live, a chat mode limited to Gemini Advanced.

With technology like Project Astra, it’s easy to envision a future where people could have an expert Al assistant by their side, through a phone or a pair of glasses. And some of these capabilities are coming to Google products, like the Gemini app and web experience, later this year.

Project Astra is in the early stages of testing, and there are no specific launch dates. However, a test version of the model is available. 

Competing With GPT- 4o 

With the launch of Gemini Live, an arms race has kicked off with OpenAI, as it challenges ChatGPT’s voice mode by offering multiple voice options and the ability to interrupt and change topics mid-conversation.

Meanwhile, OpenAI has released GPT-4o, a more efficient and powerful model than GPT-4 Turbo. It’s twice as capable, more affordable, and enables users to generate video, audio, and image content, transforming site interactions.

Google also claims an early access programme for its voice mode. CEO Sundar Pichai said that Google’s Project Astra will be available later this year for its Gemini users. Moreover, the demo also showed that Gemini will be available on both smart glasses and smartphones while OpenAI only teased their model working on smartphones.

Despite the event highlighting several achievements, in a now deleted video, former Google CEO Eric Schmidt dropped a bombshell, asserting that Google’s competitive edge dulled significantly when it prioritised employees working from home and leaving early over the relentless pursuit of victory. 

He suggested that the tech giant’s shift towards a more relaxed, remote-friendly culture inadvertently blunted its cutting edge. 

Schmidt implied that flexible hours and home offices led to complacency, allowing rivals to gain an edge in innovation and market dominance. This highlights the balance between employee satisfaction and maintaining competitiveness in the tech industry.

Project Astra

At the event, Google showcased a video demonstrating its camera’s ability to identify objects and people in a room, recognise codes, and determine cities based on a view. It even creatively named a duo of a dog and its toy.

The product was available for the hands-on experience at Google I/O 2024.

Previously, Google also announced new prototype assistants called Gems and personalised AI chatbots which users could create, that quickly provide information by watching videos, listening to speech, and organising it effectively.

It is also confirmed Astra would be available in its products like the Gemini app and website, but hasn’t yet confirmed its integration with their glasses.

Osterloh closed the presentation with a glimpse into the future of Gemini, tying it to Project Astra. “One of the first places you’ll see Project Astra come to life is right in Gemini Live,” he said.

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In a recent interview, NVIDIA CEO Jensen Huang resonated the same, saying: “OpenUSD is the HTML of virtual worlds. My expectation is every single design tool in the world will be able to connect to Open USD. And once you connect to that virtual world, you can collaborate with anybody, with any other tool anywhere.”

Universal scene description or USD was invented and open sourced by Pixar as a paradigm for interchange and assembly of 3D assets using a novel composition engine. It’s quickly been adopted into the production pipelines of many visual effects and animation studios.

And its rapid evolution and democratisation has accelerated spread into other industries outside of media and entertainment making it an HTML of metaverse. 

OpenUSD is Everywhere

To understand the importance of OpenUSD one needs to watch Finding Dory. The 2016 Pixar film about a blue tang fish with anterograde amnesia was the first to be built using USD — which, many say, is a foundational building block of the metaverse. 

Many compare its current iteration to HTML: Assets can be loaded and representation can be specified. Its next phase will be enhanced interactivity and portability — the CSS moment, so to speak. 

The general consensus – “Let’s get to the JavaScript of USD,” said Natalya Tatarchuk, distinguished technical fellow and chief architect for professional artistry and graphics innovation at Unity Gaming Services. 

However, strong application cases across sectors and remote workforces in the 3D world have been made possible by the integration of the framework with NVIDIA RTX and the recently unveiled Omniverse Cloud, which puts the entire platform to the cloud and makes it available across devices.

Designing, creating, and managing virtual worlds and digital twins, Omniverse links teams globally, making it an effective organisational collaboration tool. Engineers, designers, and producers of video games have embraced it. It is also used in robotics and manufacturing.

“USD is the closest thing to a universal 3D standard there is today,” said Guy Martin, director of open source and standards at NVIDIA. Martin wants USD in industries like manufacturing, construction, architecture and engineering.

To achieve that, last year Pixar, Adobe, Apple, Autodesk and NVIDIA launched the  Alliance for OpenUSD to build true open standards for USD in many areas, including core specification, materials, geometry and more. 

In January 2024, Siemens and Intel announced that they have joined the Alliance, and are partnering with NVIDIA to help create the next generation of persistent, intelligent, real-time AI-powered 3D universes.

The USD Spec is Already Big

One of the advantages of HTML when it first came out was its simplicity. Users couldn’t do too much, so it wasn’t hard to create something that looked similar to Netscape’s or Sun’s website. 

Similarly, Pixar has released a number of tools to help get beginners up to speed — USDview is a helpful way to inspect USD source to see how it does what it does

But just as HTML evolved from the limited static documents of HTML 1 to the dynamic applications of HTML 5, it is clear that USD will need to evolve to meet the needs of the metaverse. To accelerate this evolution, NVIDIA has already made a number of additions to the USD ecosystem.

USD now occupies the same space as HTML did in the early 1990s. The difficult task of assisting in closing the gaps and enhancing tools across multiple leaders has been taken on by NVIDIA. 

NVIDIA is also using open USD to georeference real world geometry using geospatial coordinates. This can be used for railroad design or city mapping, or even to georeference real world geometry using geospatial coordinates. NVIDIA is also working on building USD connections to IoT data streaming protocols.

Promising examples of what could be achievable with better digital twin interoperability are being provided by early adopters. Companies like Siemens, Amazon Robotics, and BMW Group are leveraging USD to bring their virtual factories to life, finding new levels of operational efficiency for large-scale workloads.

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