When OpenAI was founded in 2015, it had 11 founding directors. Out in 2015, of these 11, OpenAI retains just two directors now, owing to an exodus after Sam Altman’s short-lived quit.

In 2024, three founders have departed, including John Schulman, who joined rival Anthropic. Greg Brockman too intends to take long leave from the company.

It is not unusual for a startup the churn of manpower. However, the exodus of senior management can lead to a leadership crisis.

OpenAI operates in a competitive field with strong rivals such as Google and Anthropic. Elon Musk, the earliest founder is a critic of OpenAI.

OpenAI had recruited top researchers as its founders when it was founded in 2015.

Let us see where these 11 founders are right now.

Gerg Brockman is on leave of absence since August 2024. John Schulman has joined Anthropic in August 2024. Ilaya Sutskever left to found Superintelligence in May 2024. Andrej Karpathy founded Eureka Labs in February 2024. Durk Kingma left for Google Brain in June 2018. Elon Musk resigned from the Board in 2018. Pamela Vagata joined Stripe in 2016. Vicki Cheung joined Lyft in 2017. Trevor Blackwell left in 2017.

The founders who remain with OpenAI are Sam Altman and Wojciech Zaremba Zarema, a Polish computer scientist and researcher.

Generative AI has created a buzz for AI-powered tools that can create content such as text, images and computer code. Since the launch of ChatGPT in November 2022, generative AI occupies the center stage.

This narrative has taken a dent on account of two issues — the realization that the technology is overhyped and absurdly expensive.

We know that chatbots still struggle to answer fundamental questions and hallucinate with flawed information. In addition, the models are hungry for humongous data and compute power. To remain afloat, the companies in this space must have massive funding. Many business enterprises still have to put the technology to use. Initial expectations reached sky high. There is trough of disillusionment now.

However, soon there will be realization that generative AI is not the entire spectrum of AI. There are components of AI called ML and predictive AI. In fact, these have preceded the arrival of generative AI. The whole AI is a broad toolkit. It is an issue of the use of right technology for an appropriate case.

Generative AI is not going anywhere. It has already become a part of our lives. It can give us productivity as well as efficiency. As we have become used to Google searches, so we will also get used to easy-to-read summaries of work meetings, composing of e-mails and office memos and creating images and presentations by uttering a few words.

Generative AI has attracted massive investment — say up to $1 trillion. It has to pay off. Its use must give us healthy bottom lines. Early adopters have passed on the technology to mainstream users, who find that their expectations are not being met. The process of resetting the expectations begins. There could be incremental benefits in applications. Industry has to work for monetization of AI technology. The sector as a whole has still to prove.

There is a history of AI Tecnologies being stimulating for other newer technologies, say computer vision has become a great contributor to multimodal generative AI. Similary generative AI can receive a push by technologies such as agentic AI — AI systems designed to act like autonomous agents to pursue complex goals and workflows. Such symbiotic relationship can help the technology to reach its full potential.

Please do not think that this is an AI winter. It is time to do the right tinkering of generative AI so as to get the work done.

In 2022, Noam Shazeer and Daniel De Freitas left their jobs at Google as Google was too slow and set up their own AI startup Character.AI which develops chatbots. In August 2024, Google struck a deal with them. They rejoined Google, together with 20 per cent manpower of Character.AI and provide Character. AI’s technology to Google. In fact, it is not a buy out of Character.AI, but involves licensing the technology, and recruiting the top employees. It is a swallow up of the startup, and its most precious assets — the manpower, without becoming the owner of the firm. The licensing fees agreed are $3 billion, Character.AI’s shareholders including Shazeer with a stake of 30 to 40 per cent stands to gain $750 million to $1 billion. The remains of Character.AI will continue as an entity without its founders and investors.

Such unusual deals are happening in Silicon Valley recently. Big Tech resorts to such complicated deals for acquiring startups. The idea is to obtain licensing technology and poach the top employees. It is a way to sidestep regulatory scrutiny, especially the FTC. It is non-traditional deal.

Microsoft started the trend by agreeing to pay the startup Inflection more than $650 million to license its technology and hire almost all its employees, including its founder Mustafa Suleyman. Suleyman now heads the consumer AI business of Microsoft.

Amazon similarly acquired Adept.

There is surrogate advertising — the advertising pertains to alcoholic drink Kingfisher, but what is advertised is Kingfisher mineral water, club glasses are advertised to promote Carlsberg, music CDs are advertised to promote Seagram’s Imperial Blue. ASCI guidelines distinguish between surrogate advertising (prohibited by law) and brand extension advertising (legally permitted). ASCI reports the cases of surrogate advertising to various authorities for appropriate action. There are loopholes here. If a brand extension product is available in at least 10 per cent of the stores as does the leading product in that category or if its sales reach Rs.5 crore annually or Rs.1 crore in the state it is sold, they are deemed to be in order. The well-funded liquor and tobacco lobbies take advantage of this provision.

The government intends to bring new regulation that will outlaw advertising for non-alcoholic items (mineral water, club glasses, soda, music CDs) if they display the same logo or branding as does the alcoholic brand. The violation of the rule would entail fines of up to Rs.50 lac, while the celebrity ambassadors promoting such brands could face an endorsement ban of up to three years. Thus, both the celebrity and the marketer will be held accountable.

Brands will have to take customized and targeted approach to reach the customers. Digital platforms will be crucial to enable campaigns to engage with targeted audience. There will be personalized ads. Thus, digital media will be the unintended beneficiary of all this. Companies can take social responsibility initiatives and community engagement to build brand loyalty.

Generative AI has a great market potential. It can account for 33 per cent of the global AI market by 2027. The total AI market is expected to reach $320-380 billion, growing at a compound growth rate (CAGR) of 25-35 per cent.

In Indian tech industry, women make up 36 per cent of workforce. There is a significant gender gap at executive level. Generative AI could transform the tech sector by reducing the disparity between men and women in the tech sector.

It is reported that 45 per cent women in the tech roles feel that generative AI can boost their perceived competence. One in five women use generative AI on a daily basis. Amongst the senior management women, the daily usage of generative AI tools is nearly 35 per cent.

Still, the usage is lower at the senior levels. This could be attributed to limited knowledge, lack of trust, restricted access to these tools and fear of competence scrutiny. The knowledge gap must be addressed to get greater adoption. The enabling environment should be created to make women comfortable while using generative AI.

Some measures to boost adoption among women are to provide clear career pathways, mentorship programmes, flexible work schedules, providing training, building a suitable work culture and fostering networking opportunities.

Many women have shown willingness to invest more time to achieve professional success in generative AI.

In order to leverage a huge opportunity of $320 billion market, India must have gender diversity.

John Schulman, one of the founders of OpenAI, quits to join rival Anthropic. Schulman had joined OpenAI in December 2015, just before his doctorate in electrical engineering and computer science at UC Berkeley. He announced his exit on Tuesday, August 6, 2024. He is making the move ‘to deepen his focus on AI realignment and to resume technical work, thus opening a new chapter of his career’.

Greg Brockman, OpenAI’s cofounder, has announced his sabbatical till the end of the year. He intends to relax and would like to work for achieving AGI goal.

Schulman at OpenAI has led the team of reinforcement learning that developed ChatGPT powered by GPT-3, a language model.

After the departure of Schulman, only three of OpenAI’s eleven original founders remain in the company — Sam Altman, Brockman and Zaremba.

Ilaya Sutskever, another founder, left the company in May 2024. Andrej Karpathy, another founder left in February 2024.

The most notable early exit was that of Elon Musk on the ground that OpenAI is deviating from its public good priority to commercial priorities.

Peter Deng from Product Management area who joined in 2023 also exited this year.

As we know, there is a demand in the market for anti-obesity drugs Wegovy (Navo Nordisk) and Zepbound (Eli Lilly). There are several other versions of such drugs in the market, not necessarily the generics of these two. There are promotional messages on a continuous basis for Wegovy (semiglutide) and Zepbound (tirazepatide). There are anti-diabetic counterparts of these — Ozempic and Mounjaro. There is wide-spread media coverage and outdoor advertising.

Several websites sell these drugs — some 1100 websites mentioned semaglutide. Many illegal online pharmacies sell these drugs. Here these could be obtained without a prescription. These could be counterfeit drugs — contaminated and with varying proportions of semiglutide. The big issue is the uncertainty of the quality of these drugs. Pharmacies can take advantage of a regulatory grey zone which allows them to sell alternative version of a branded drug during a drug shortage. There is doubtful sourcing of the active ingredient in the products of these pharmacies. And in case of infections, whether these pharmacies maintain the sterile manufacturing process.

Some offer fancy dosage forms such as gummies, lozenges and oral drops. The additives, they claim, purport to improve weight loss or minimize side effects. However, they could affect a drug’s efficacy.

Demand outstrips the supply. There may not be insurance coverage. That pushes the customers to buy the cheaper alternatives. There is confusion between genuine pharmacy products, compounded products and counterfeit stuff.

The WHO warned about vials of anti-obesity medicines that contain undeclared ingredients, including insulin. There are dosing errors on account of compounding of medicines. Many end up in hospitals. Novo Nordisk sells single use pens, whereas vials of semiglutide sold by some pharmacists are prone to overdosing on the part of patients. There is a rise in the cases of overdosing and poisoning.

There should be sufficient supply of these medications by approved pharmacies such as Novo Nordisk and Eli Lilly. That will abate the problem. The end of shortage will end the free-for-all compounding. Consumers should know that fake drugs are no bargain.

A US court in Washington ruled in August 2024 that Google illegally monopolised the search market. This gives the first win to the government in its anti-trust case against a tech giant in more than two decades.

The court referred to the $26 billion in payment to make its search engine the default option on the smartphones and web browsers. This move by Google effectively blocks any other competitor from succeeding in the market. Such monopolizing by Google has been able to raise consistently the prices of online advertising. The distribution agreements foreclose a substantial portion of the general search services market and impair the opportunities for the competitors. The default position has allowed Google to build up the most-used search engine in the world and Google could attract more than $300 billion in annual revenue largely generated by search ads.

The Justice Department will continue to enforce vigorously the anti-trust laws. Google plans to appeal the decision.

The Judge passed a harsh comment, “Google is a monopolist and it has acted as one to maintain its monopoly.”

Such a significant ruling was passed against Microsoft in the 1990s when the company bundled its browser as the default browser in Windows-based devices. It crowded out Netscape, the pioneer in browsing technology. The US authorities took notice, and a landmark decision against Microsoft was passed. Microsoft avoided the specter of breakup (since prior to this, AT&T and IBM were badly hit by breakup).

The US authorities could ask Google to share data. It can affect Google’s finances adversely. If competitors get access to similar data, advertisers will diversify ad spends across multiple platforms. It will result into a potential decline in Google’s advertising market share. Besides, there could be costs associated with sharing data. Even while sharing data, privacy cannot be compromised, and hence Google will have to work with stakeholders, regulators and privacy advocates.

There could be another remedy — divestiture or selling parts of the firm. It is a structural remedy. There could be a ban on the practice of paying billions to make Google a default search engine. A choice could be given to users. However, this is like shutting the stable after the horse has fled. Even the users can exercise choice in favour Google.

There are certain misgivings about the LLMs. Some feel they have been overhyped. Some consider them as an impediment in achieving AGI. In fact, LLM have limitations. There is lack of trust of the users. LLMs are not that accurate and reliable. The discussion is cliche, since the current LLMs do have reasoning and logical problems.

Despite the skepticism, big tech has not stopped building the best LLMs. Each company tries to demonstrate one-upmanship.

Yan LeCun believes LLMs will not lead to AGI. In fact, OpenAI has slowed the achievement of AGI by 5-10 years. It is necessary to try some novel approaches to AGI through abstraction and reasoning corpus (ARC). LeCun advises to attain animal-level intelligence first.

LLMs at times struggle with the apparently simple tasks. Youshua Bengio, one of the godfathers of AI, says in AI, some ingredients of human intelligence are missing.

LLMs fail to play common games such as tic-tac-toe. GPT- 4 struggled with Sudoku puzzles. DeepMind contends LLMs lack genuine understanding. Consequently, they cannot auto correct or adjust their responses. LLM-based chatbots are poor in math problems.

Still, the time is not ripe to write them off. Though at present, we have not reached human-level intelligence, it does not rule out the possibility of reaching it in future.

GPT-4 understands complex emotions. It beats human psychologists. GPT-5, according to Mira Murati, OpenAI CTO, will have Ph.D.-level intelligence.

According to Sutskever, text is the projection of the world. LLMs build cognitive architecture from scratch. They trace the evolution of learning and resonate with real-time learning.

There is research on LLMs too. In future, they can understand cause-and-effect relationships. LLMs can use neurosymbolic AI.

LLMs are worth another shot on the road to AGI.

AI can facilitate business processes, thereby cutting costs. The irony is that AI per se is very costly.

If AI is scaled up, the costs rise — on account of capital investments in data centers and acquisition of costly GPUs. these costs cannot be passed on to the clients. AI tools are not economical.

Even the cost of deployment of AI into a company’s systems costs a lot — ranging from $5 million to $20 million.

The silver lining is that AI costs are declining apparently, and the gap between investments and returns is narrowing.

The crux of the issue is the benefits AI can offer. Ther is continuing investment for the growth of AI. The companies prefer overinvestment, rather than err by underinvestment.

Over a period of time, the costs of training AI models have risen. These too are coming down. Gemini from Google is a powerful model but is available at a lesser cost. Similarly, OpenAI’s GPT-$o is faster, but is available at half the cost of the previous model GPT-4 Turbo.

The cost of accessing the models has dropped substantially — this is measured by processing of tokens.

Researchers focus on techniques such as ‘sparsity’ and ‘quantization’ to do the cost cutting.

Even enterprises now focus on choosing the right model for their needs, not necessarily the costliest model. They can opt for open-source models or smaller models.

Silicon Valley in past has accepted a margin hit in order to grow the market share. The aim is to be competitive, and skim the market later.

However, the issue is how the benefits of generative AI outweigh its costs for the businesses. Some analysts suggest a 30 per cent project drop by the end of 2025. If generative AI remains confined to summerization and deployment of chatbots, it may not be worth even the lower price tags. AI compnies will have to ponder over this issue.