Incoming president Donald Trump has confirmed reports that Sriram Krishnan, until recently a general partner at Andreessen Horowitz (a16z), will serve as senior policy advisor for AI at the White House Office of Science and Technology Policy. Trump said in a statement that Krishnan will “help shape and coordinate AI policy across government, working with […]
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Two big defense tech players, Palantir and Anduril, are talking to tech companies including SpaceX, OpenAI, Saronic, and Scale AI about forming a consortium to bid on Pentagon contracts, according to a report in the Financial Times. The goal, the FT says, is to challenge the dominance of “prime” defense contractors like Lockheed Martin, Raytheon, […]
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OpenAI announced a new family of AI reasoning models on Friday, o3, which the startup claims to be more advanced than o1 or anything else it’s released. These improvements appear to have come from scaling test-time compute, something we wrote about last month, but OpenAI also says it used a new safety paradigm to train […]
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LLM models are already capable of diagnosing scientific outputs, but, until now, had “no physical agency to actually perform" experiments.
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People and companies are placing big bets on artificial intelligence. One of the safer ones is that demand for workers with AI skills will continue to grow.
Labor market watchers told Business Insider that in 2025, as in 2024, many employers will likely be eager to hire people with skills in AI — like machine-learning specialists who train models, one of this year's most-talked-about roles — but also in wider areas that touch the technology.
In the tech industry, which has experienced years of lackluster hiring following a pandemic-era surge, there are early signs of a rebound, Hannah Calhoon, VP of AI at Indeed, told BI.
If that continues, she said, hiring will likely include roles involving AI.
Another area of demand, Calhoon said, could come from employers that aren't tech firms yet that need people skilled in incorporating off-the-shelf AI tools into their businesses and datasets.
However, unlike the tech giants, these employers aren't likely to try to build their own AI platforms, she said. So, rather than trying to recruit data scientists and those machine-learning engineers, these companies might instead want workers who can help decide which AI instruments to use and how to incorporate them into their workflow.
"What they're going to be looking for is people who understand those systems and can help them implement those tools in their business," Calhoon said.
That's likely to translate to increased demand in 2025 for roles involving AI implementation and transformation — jobs like applications administrators or solutions architects, she said.
There are other signs that the demand for talent involving AI is picking up.
Last week, Salesforce CEO Marc Benioff said that the company is experiencing "a big hiring surge" and working to fill thousands of roles to help sell products, including those involving AI. Benioff said the company has 9,000 referrals for the 2,000 positions it's opened.
Masayoshi Son, the CEO of SoftBank, likewise recently talked up AI's potential. At an event with President-elect Donald Trump last week, Son said that the Japanese conglomerate would invest $100 billion into the US over the next four years and create at least 100,000 jobs in AI and related areas.
Already, other employers are looking to grow around AI. According to Indeed, job postings mentioning AI that saw the biggest growth in the first 11 months of 2024 were senior scientists, software engineering managers, research engineers, and researchers.
The market may be growing, though it can be hard for employers to hire in some AI-related areas. The talent firm Randstad reports that it's twice as difficult to find and hire senior-level workers skilled in AI and automation as it is for other senior-level jobs in different industries.
Vacancy rates for roles involving specialized AI skills, like developing natural-language processing models, are as high as 15%, Randstad found. That's about double the overall job vacancy rate in the US. Randstad's estimate on AI jobs is based on an assessment of some 10 million job postings and 136 million résumés in the third quarter of 2024.
According to Randstad, employers worldwide are having the hardest time finding workers skilled in natural language processing, predictive modeling, and "stakeholder communication." The firm notes that this is partly because such abilities are specialized yet also in demand across industries.
In the US, Randstad said, the vacancy rate for jobs that require skills like natural language processing stands at 14%.
Indeed recently reported that, as of September, the share of US job postings that mention generative AI or related terminology was up 3.5 times year over year.
Yet that doesn't mean that all employers are looking for GenAI whizzes. Indeed found that only 2% of employers globally included skills related to AI in their job descriptions. By comparison, more than 20% called for basic computer skills.
Nevertheless, Calhoon said, employers' demands for AI skills are only likely to grow.
"Maybe not next year, but three or four years from now, in many roles, there will be an expectation that people will have basic fluency in being able to use some of these platforms," she said.
That's likely in part because it's not only major employers that will expect workers to have AI skills.
Andy Schachtel, CEO of Sourcefit, an offshore staffing firm, told BI that businesses of all sizes are looking to AI to boost efficiency.
The US Chamber of Commerce found in a mid-2024 survey of 1,100 small businesses that four in 10 reported using generative AI, up from 23% in 2023. About three-quarters of small businesses surveyed said they plan to adopt emerging tech like AI.
That could add to the already surging demand for leaders who are experts in AI. According to a review of more than 35,000 public and private companies in the US by Altrata, a research firm focused on executive data, the number of people in the role of chief AI officer or its equivalent — a job many people may not have heard of until this year — was up 70% year-over-year through late October.
That demand is likely one reason that workers with AI skills or who possess capabilities working with AI tools are, on average, 34% more likely to change jobs, according to Randstad.
Nicole Kyle, who researches the future of work, told BI that even for parts of a business where AI might be expected to take on a good share of the workload — like call centers —its adoption would likely increase demand for other roles.
She said that in the case of call centers, for example, those added roles might include positions involving data governance and data cleaning, as well as customer experience. That's one reason Kyle, who's cofounder of CMP Research, said she remains optimistic about AI's impact on jobs.
"I do think net-net, it will create jobs the way other technological advancements have," Kyle said.
Aditya Challapally
This as-told-to essay is based on a conversation with Aditya Challapally, a 30-year-old Microsoft employee who teaches a course for Stanford Online about generative AI. This story has been edited for length and clarity.
I started working in AI about a decade ago. I started as a data science intern at Uber, then did AI consulting at McKinsey, and later joined Microsoft, where I now work on Copilot.
I started guest teaching at Stanford four years ago and recently co-created a course called Mastering Generative AI for Product Innovation, which launched on Stanford Online in August 2024. It's an online, self-paced course that runs throughout the year. All of the research comes from talking to 300-plus users and 50-plus executives.
A lot of the people who take the class are tech adjacent, such as customer support representatives for a technical product, or product managers for a software or hardware product. They'll often be working on somewhat of a technical product and the course helps them understand gen AI a little bit more.
We teach three modules in this course. The first module explains what Gen AI is and where the biggest opportunities are. In the second module, we talk about what great Gen AI products look like.
The third module talks about how great Gen AI products are built and what individuals can do to set themselves up to be more influential, relevant, and useful when building Gen AI products.
These are the two main pathways you can take to do so.
When I go out and talk to Fortune 500 leaders, they say that their most burning need is for professionals who bridge both worlds — those who understand the business requirements but also understand the technical requirements.
This doesn't necessarily mean that you have to learn how to code, but you at least need to have enough technical literacy that you can translate product visions into technical requirements.
The beginner version is just getting really good at prompt engineering. This sounds like it would be quite basic, but understanding the exact limitations of prompts and all of the different tools across text, audio, and image makes you already very valuable in a business setting because you can help generate ideas even before they get to the technical team.
At an intermediate stage you also should start to understand a little bit about how gen AI systems work in systems design, like how gen AI models can be called within your data boundary.
Companies have data boundaries for which they have an agreement with their customers that their data can't go beyond. So if you're a bank, you may have an agreement with your customers that only the bank will use their information. If you send that in some sort of chat to OpenAI, that would be breaking the company data boundary. So something as simple as knowing that is already really helpful.
In the advanced stage of this track, there are two options.
Some people who don't work in big companies go deeper into understanding coding a little more. People who work in Big Tech companies usually dive deeper into system architecture. So they'll understand things like data boundaries and data flow diagrams in a lot more detail.
The domain expertise track is where business people automatically lean toward and have an advantage. This is not necessarily knowing more about the industry, but knowing how gen AI can apply to the domain in more detail.
For example, in finance, you have to know things like what data you can use to train a specific model. You also have to know things like what types of privacy and security regulations you have to go through to get an app approved or release a gen AI-related app.
This skillset is so valuable that companies pay large amounts to consultants that have this specialized expertise. I know this guy who used to work as an operations manager at a bank and he figured out where gen AI was the most valuable. Now, companies will just call him to figure out where to launch their gen AI product.
The best thing I see people do is try to automate a lot of their lives with gen AI. They use ChatGPT or Claude for everything and that helps them understand the limitations of AI really well and how to prompt it.
When beginners start to use gen AI, they're not used to what I call the abundance of intelligence. They'll say "Can you give me a response to this text message?"
Experts who use gen AI a lot will say something like, "Can you give me 20 responses to this text message?" And then they'll go and use their taste to pick one.
Outside of work, I use it in many ways to think through a lot of plans. It's really helpful as a thought partner for me, even if for communication, for general planning, or for something even as banal as trip planning.
Instead of asking a friend for advice you should think about asking an LLM or a chatbot for advice. That's when you really start to understand how it's useful.
OpenAI’s efforts to develop its next major model, GPT-5, are running behind schedule, with results that don’t yet justify the enormous costs, according to a new report in The Wall Street Journal. This echoes an earlier report in The Information suggesting that OpenAI is looking to new strategies as GPT-5 might not represent as big […]
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Welcome back to Week in Review. This week, we’re looking at OpenAI’s last — and biggest — announcement from its “12 Days of OpenAI” event; Apple’s potential entrance into the foldable market; and why Databricks is choosing to wait to go public. Let’s get into it. P.S. We’re off for the holidays! Week in Review […]
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Elon Musk has had a year for the record books.
His businesses have taken off, with Tesla, SpaceX, xAI, and Neuralink all touching new valuation highs. Their success has boosted Musk's net worth to above $450 billion for the first time, putting him over $200 billion ahead of the world's second-richest person, Amazon's Jeff Bezos.
Musk has also become a power player in US politics after wielding his cash and clout to help win Donald Trump a second term in office. As one of the president-elect's closest advisors, he's now gearing up to overhaul the US government.
The situation seems worse at X, formerly Twitter, after Musk's $44 billion takeover and reshaping of the platform sparked an advertiser exodus.
Take a look at Musk's 2024 in charts (all data is accurate as of Friday, December 20):
Tesla shares have shot up as much as 85% this year, driving the electric vehicle maker's market value above $1.4 trillion for the first time. They've since retreated but continue to trade near record levels.
The automaker has benefited from market buzz around artificial intelligence — which it's harnessing to develop self-driving cars and humanoid robots — plus a robust US economy and the Federal Reserve cutting interest rates.
Investors are also betting that Musk's businesses will benefit from his close ties to Trump, which could translate into less stringent regulations, government subsidies, tariff exemptions, and more.
SpaceX's valuation nearly doubled from $180 billion at the end of last year to $350 billion this month, based on the price paid by the company and its backers for employee shares in its latest tender offer.
Musk's rocket, spacecraft, and satellite communications company made several technological breakthroughs this year. For example, it plucked the first-stage booster of its new Starship out of the air using a massive pair of mechanical "chopsticks" in October.
Musk's net worth slumped in the spring as Tesla stock tumbled, dropping below $170 billion at its nadir.
But it rebounded by over $300 billion to touch an unprecedented $486 billion on December 17, as Tesla hit fresh highs and SpaceX notched a $350 billion valuation.
Musk's artificial intelligence company, xAI, was only founded in July 2023.
Yet it notched a post-money valuation of $24 billion in May following its Series B funding round. That rose to $50 billion in November, reports say, meaning the maker of the Grok chatbot is worth roughly as much as Monster Beverage.
It remains tricky to gauge the health of X, the social media company formerly known as Twitter that Musk took private in 2022. One way is to use Fidelity's monthly estimates of the value of its stake in the business.
The mutual fund giant's figures imply that X's valuation has crashed since Musk's purchase. The tech billionaire laid off a large part of the company's workforce and relaxed content moderation in support of greater free speech, triggering an advertiser exodus that hammered the company's revenues.
Regardless, Musk recently posted on X that the platform has roughly 1 billion active users, although around 40% of them only log on during important world events.
Musk was one of the biggest spenders in the US presidential election, deploying over $270 million to back Trump's race for president, run ads against Democrats, and promote conservative viewpoints.
His starring role in Trump's victory and emergence as one of the president-elect's closest advisors and a co-chief of the new Department of Government Efficiency suggests that his investment in the election has paid off.
Neuralink, Musk's neurotechnology company, was valued at $8 billion this summer, up from about $2 billion three years earlier.
The developer of brain-computer interfaces wants to allow people with quadriplegia to control computers with their thoughts. Musk released footage this spring of the first patient to receive one of its brain implants.
zentilia/Getty Images; Jenny Chang-Rodriguez/BI
After three years of tense reductions, the skies are clearing over Silicon Valley, and startup investors seem broadly optimistic about a resurgence in tech dealmaking.
We asked venture capitalists at 35 firms like Andreessen Horowitz, Insight Partners, IVP, and Sapphire Ventures, to tell us what's hot and what's not in tech next year, how potential regulatory changes could rouse a sleepy exit market, and where artificial intelligence goes from here.
In 2025, venture capitalists expect a loosening of antitrust regulations under the new presidential administration. This could reignite acquisition activity by strategic buyers, which would allow funds to distribute proceeds from those deals to their own investors, or limited partners, and raise new funds to invest in the next generation of startups, said Brian Garrett, managing director at Crosscut Ventures.
In recent years, startups weren't the only ones facing a cash crunch. Established funds raised the lion's share of funding dollars, while many newish and boutique funds struggled to raise. A torrent of dealmaking, combined with Trump's return to the White House and an end to the political uncertainty, could mobilize investors in these funds who had been sitting on the sidelines to whip out their checkbooks, said Ivan Nikkhoo, a managing partner at Navigate Ventures.
"Uncertainty breeds defense, optimism breeds offense," said Matt Murphy, a partner at Menlo Ventures and early Anthropic investor. "We're going into a cycle where acquirers are feeling they need to play offense and startups feel like it's time to invest in leadership. And the IPO market is open for best-in-class assets."
From IPOs to robotaxis, these are the tech trends to watch in 2025, according to venture capitalists.
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Jai Das, president and partner at Sapphire Ventures: "A larger number of 'application layer' companies will have a breakout year with several crossing $100 million in revenues. I predict 50 companies will cross $50 million ARR while still growing 60%+, and at least 10 will hit $100 million ARR. A lot of these companies will be prosumer companies, but there will be several business application companies as well."
Ben Lerer, managing partner at Lerer Hippeau: "When you get the cost of compute going down as quickly as it has, and the number of options in terms of foundational models growing as it has, you end up with a really interesting time for the application layer to thrive. If you're a startup, you can go with the flavor of the month — not just a ChatGPT wrapper, or a Claude wrapper, or a Gemini wrapper, or you name it — but some combination of all of them to optimize functionality, results, and the cost of those results."
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Sofia Dolfe, partner at Index Ventures: "2025 is the year we will see the IPO market opening back up. There are already signs that this is on the horizon: we're seeing gradual recovery, rates have started to come down, and there are many later-stage companies with the financial profiles to go public."
Michael Yang, senior managing partner at Omers: "Two kinds of companies will go public as the IPO window opens back up next year. First, the truly great businesses that are really scaled and have forecastable growth and would've gone public earlier if the IPO market was more favorable, and second, companies that entered into structured financings with dirtier terms that need to go public for timing reasons."
Nima Wedlake, managing director at Thomvest Ventures: "The IPO market will remain closed for most tech companies, with a high bar for entry — $300 million-plus ARR, fast growth, and cash-flow breakeven or better."
Jason Armond/Los Angeles Times via Getty Images
Nihal Mehta, general partner at Eniac: "Guidance on what the regulations could be for crypto and AI would encourage founders to build productively within those areas."
Jai Das, president and partner at Sapphire Ventures: "The new administration is crypto-friendly, bringing with it an expected acceleration of crypto-based business models (especially those using stablecoins). I predict we'll have another crypto mania in 2025."
AP Photo/Felix Marquez
Wesley Chan, cofounder and managing partner at FPV Ventures: "In 2025, I predict a lot of contraction for VCs, except for top funds. We're still in a downturn. Some firms shut down, a lot of firms are not doing new deals, and you will see a lot of junior-mid level employees leave."
iStock, BI
Molly Alter, partner at Northzone: "The 'sexiest' deals will continue to raise at sky-high valuations, but for the rest of the pack, companies will need to show very specific metrics to command a strong valuation. There will be a great bifurcation into the 'haves' and the 'have-nots.'"
Don Butler, managing director at Thomvest Ventures: "Startup shutdowns will increase, particularly at the seed stage, as companies run out of cash. This will influence valuations, with investors likely focusing on startups that have shown resilience or achieved meaningful milestones."
Matt Murphy, partner at Menlo Ventures: "Valuations will rise as growth rates and market multiples recover, but many companies still might not grow back into their ZIRP valuations. People are over that and won't let it get in the way of pursuing opportunity. Valuations for GenAI companies will continue to be outliers based on any historical metrics."
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Brian Walsh, head of Wind Ventures: "2025 will be the year that we enter the age of 'robo taxis' with, first, Waymo now well along its adoption S-curve in San Francisco and expanding quickly, and, second, Tesla favorably positioned with quickly maturing best-in-class autonomy technology (no human in the loop) and an existing large fleet to scale it."
Kasper Sage, managing partner at BMW i Ventures: "Autonomous fleet deployments will gain traction in controlled, high-density environments such as for applications like campus environments and logistics for heavy industries."
David Zalubowski/ AP Images
Aaron Jacobson, partner at NEA: "With the change of administration, I expect the return of mega M&A deals. We are going to see a 'WhatsApp' like $20 billion-plus M&A outcome for a leading AI company."
Michael Yang, senior managing partner at Omers: "Big Tech will be back at the M&A table with a new administration and regulatory regime in place. They've been quieter in recent times but should be chomping at the bit to capitalize on what is still a buyer's market."
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Sasha McKenzie and Van Jones, both deal leads at Wellington Access Ventures at Wellington Management: "The concept of letter rounds in VC is becoming more amorphous. We're seeing $30 million and $100 million seed rounds, raising questions about what seed even means anymore. The model is shifting towards evaluating how quickly founders can run and how disciplined they are with results, rather than hitting historically stated milestones (e.g., $1 million in revenue to raise a Series A). There will be more nuance in how VCs evaluate progress, focusing more on the operator and their ability to balance vision with execution, based on the capital they have."
iStock; Rebecca Zisser/BI
Aaron Jacobson, partner at NEA: "Chatbots are overhyped. Agents are under-hyped. Enterprises will move beyond the low-hanging fruit of 'GPT-wrappers' to deploy digital workers that can reason and take action to make a real business impact."
Praveen Akkiraju, managing director at Insight Partners: "If 2024 was the year of LLMs, we believe 2025 will be the year of agentic AI — where highly capable state-of-the-art reasoning LLMs are combined with orchestration frameworks like memory, tool calling, and user-in-the-loop processes to build AI agents that can address progressively complex business workflows."
Seema Amble, partner at Andreessen Horowitz: "In the short term, human workers will be the reviewer in the loop; in the future, as trust is established over time, I expect many data-derived actions will shift toward being entirely a set of narrowly defined task-driven agents."
S. Somasegar, managing director at Madrona: "The world where we each have a digital assistant that works with a collection of AI agents is probably five to ten years out. But having AI agents that can do specific tasks really, really well is happening sooner and I think we will see a ton of progress on this in 2025."
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Ravi Viswanathan, founder and managing partner at NewView Capital: "The venture secondaries market will continue to be an important source of liquidity — a trend we think is here to stay due to structural dynamics of the venture asset class."
Simon Wu, partner at Cathay Innovation: "The size of tender offers has grown from millions to billions as the desire to own top-performing names by mutual funds and VCs increases, thus allowing some of the best names to stay private longer. Tenders are likely to get bigger to a selective group of companies in tandem with a more active IPO market next year."
Paige Taylor White/Toronto Star via Getty Images
Molly Alter, partner at Northzone: "Vertical SaaS will become more highly valued than ever, due to the increasing difficulty of differentiating a product in horizontal categories."
Cathy Gao, partner at Sapphire Ventures: "Vertical software will evolve rapidly as AI moves to the agentic phase, enabling end-to-end automation of complex, industry-specific workflows that were once beyond the reach of software. By pairing deep domain expertise with intelligent automation, vertical AI will unlock new use cases, deliver outsized ROI, and become table stakes for staying competitive."
blackred/Getty, PM Images/Getty, Tyler Le/BI
Alexa von Tobel, managing partner at Inspired Capital: "Given the new political climate, we, of course, expect to see less regulation across the board. I think we'll see acceleration in a few core categories, including fintech."
Marlon Nichols, managing partner at MaC Venture Capital: "Fintech is an area I'm excited to invest in, particularly fintech startups leveraging AI to create transformative personal finance tools."
Sydney Thomas, general partner at Symphonic Capital: "We are watching the regulatory environment towards fintech ease which has enabled massive speculation on what asset class will win. … This also means, many startups will be required to regulate themselves, which isn't always an easy thing to do."
Tesla
Claire Yun, investor at Piva Capital: "Generative AI will continue to accelerate and supercharge robotics; simultaneously, we will see a choke point in human labor as an aging domestic workforce and protectionist policies create a sharp supply and demand imbalance. The result will be a colorful Cambrian explosion of robots as they step in to fill this gap."
Bob Ma, partner at Wind Ventures: "Urban areas will have fleets of robots on sidewalks, while drones will manage suburban and rural deliveries. Enhanced speed, cost-efficiency, and sustainability will redefine retail and e-commerce, with regulations supporting wider adoption and innovation."
Yuri Lee, partner at IVP: "As AI advances enable robots to move from structured, repetitive tasks to more complex and dynamic real-world applications, we'll see rapid progress in robotic perception, manipulation, and decision-making capabilities."
Microsoft
Tasneem Dohadwala, partner at Excelestar Ventures: "Small language domain-specific models are starting to show more value. Instead of using vast swaths of the internet to train large models, these smaller models can be trained on specific datasets, such as medical journals, newspapers, or email collections. As a result, they are highly tailored and more accurate in reflecting a user's particular constraints and voice.
Michael Yang, senior managing partner at Omers: "If 2024 was the year of the LLMs, 2025 will be the year of small language models (SLMs) and proprietary data sets spawning the next generation of enterprise SaaS applications. Companies have realized that data in their midst can be harnessed in new and better ways than the 'structured workflow apps' of old and by leveraging targeted SLMs, they can do work differently, more efficiently."
Patrick T. Fallon/AFP via Getty Images
Brad Bernstein, managing partner at FTB Capital: "Despite the IPO market showing better performance in Q3'24 with proceeds already surpassing 2023 totals, structural issues like regulatory burdens and governance challenges still pose obstacles for small and mid-cap companies. Private equity markets are stepping in to fill the gap, with growth equity deals comprising a larger share of activity and providing opportunities for startups in high-growth sectors like insurtech and healthcare tech."
Jai Das, president and partner at Sapphire Partners: "With the new administration, I predict we will see an uptick in exits, and much more tech M&A activity. We'll also see PE firms buying up a lot of companies once boards and management teams realize these businesses won't be able to grow at 30% at scale and ultimately, IPO."
Michael M. Santiago/Getty, Nordin Catic/Getty, Tyler Le/BI
Aaron Jacobson, partner at NEA: "Open-source foundation models will close the gap with the leading proprietary models. On top of this we will see a significant shift away from pre-training models from scratch to fine tuning OSS models and distilling them to smaller models for faster performance."
Mo Jomaa, partner at CapitalG: "I predict that in 2025 we will continue to see open source technologies consume the infrastructure layer in software. We have seen this trend play out in several categories already, including data and analytics (which led to our investment in Databricks) and observability (which drove our investment in Grafana). Enterprises will continue to adopt open source because it helps them save money, avoid vendor lock-in, and shape the product roadmaps of the technologies that they procure."
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Andrew Schoen, partner at NEA: "We will see a surge of investment into technologies critical to restarting the US industrial base and enhancing national security. A record number of deals and dollars will go into AI, automation, cybersecurity, and frontier technology serving manufacturing, supply chain, and defense markets."
Jake Seid, general partner at Ballistic Ventures: "Over the next 18 months, we're going to see a lot more cybersecurity exits. While this may include an uptick in M&A activity, I expect we'll see cybersecurity companies go public in 2025 and in the first half of 2026 given how large the market for cyber products has become."
Tom Williams/CQ-Roll Call, Inc via Getty Images
Samir Kumar, general partner at Touring Capital: "We should expect a lot less regulatory headwinds in 2025 for AI given David Sacks will be the AI and crypto czar for the new administration. This is likely to even result in the repeal of President Biden's executive order on AI."
Francesco Ricciuti, associate at Runa Capital: "In the US, Trump is bringing prominent people from the startup and VC world in the government, and I wouldn't be surprised if the regulatory landscape will evolve towards entrepreneurship and technology."
Over the past 12 business days, OpenAI has announced a new product or demoed an AI feature every weekday, calling the PR event "12 days of OpenAI." We've covered some of the major announcements, but we thought a look at each announcement might be useful for people seeking a comprehensive look at each day's developments.
The timing and rapid pace of these announcements—particularly in light of Google's competing releases—illustrates the intensifying competition in AI development. What might normally have been spread across months was compressed into just 12 business days, giving users and developers a lot to process as they head into 2025.
Humorously, we asked ChatGPT what it thought about the whole series of announcements, and it was skeptical that the event even took place. "The rapid-fire announcements over 12 days seem plausible," wrote ChatGPT-4o, "But might strain credibility without a clearer explanation of how OpenAI managed such an intense release schedule, especially given the complexity of the features."
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On Friday, during Day 12 of its "12 days of OpenAI," OpenAI CEO Sam Altman announced its latest AI "reasoning" models, o3 and o3-mini, which build upon the o1 models launched earlier this year. The company is not releasing them yet but will make these models available for public safety testing and research access today.
The models use what OpenAI calls "private chain of thought," where the model pauses to examine its internal dialog and plan ahead before responding, which you might call "simulated reasoning" (SR)—a form of AI that goes beyond basic large language models (LLMs).
The company named the model family "o3" instead of "o2" to avoid potential trademark conflicts with British telecom provider O2, according to The Information. During Friday's livestream, Altman acknowledged his company's naming foibles, saying, "In the grand tradition of OpenAI being really, truly bad at names, it'll be called o3."
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Chip company Nvidia gets the green light from the European Union to complete its acquisition of Run:ai. The EU came to a unanimous decision today that Nvidia could go ahead with its acquisition of Israeli GPU orchestration platform Run:ai, according to reporting from Bloomberg. The European Commission determined that if the merger went through, other […]
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Google said Friday that the company is expanding Gemini’s latest in-depth research mode to 40 more languages. The company launched the in-depth research mode earlier this month, allowing Google One AI premium plan users to unlock an AI-powered research assistant of sorts. The in-depth function works in a multi-step method, from creating a research plan […]
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In the first half of 2024 alone, more than $35.5 billion was invested into AI startups globally.
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Alexis Ohanian predicted that in a future oversaturated with artificial intelligence, people will seek out more raw, emotive human experiences.
And in 10 years, he said, live theater will be more popular than ever.
The 41-year-old, who co-founded social media platform Reddit in 2005, told the "On Purpose with Jay Shetty" podcast this week that AI will soon have an undeniable impact on nearly every aspect of society, including the entertainment sector.
Ohanian, who also founded venture capital firm Seven Seven Six in 2020, said that the industry will see a big shift when AI makes on-screen entertainment better, faster, cheaper, and more dynamic — which he said is happening.
Every screen we look at will become so programmed to show us "what we want, when we want it, how we want it," he said, that "a part of our humanity will miss, you know, thousands of years ago when we were sitting around a campfire and that great storyteller was doing the voices and the impressions.'"
"That's ingrained in our species," he said.
And that kind of raw, in-person magic will feel novel, he suggested.
"I actually bet 10 years from now live theater will be more popular than ever," Ohanian said. "Because, again, we'll look at all these screens with all these AI-polished images, and we'll actually want to sit in a room with other humans to be captivated for a couple hours in a dark room to feel the goosebumps of seeing live human performances."
The same is true for sports, he told Shetty. "We need humans doing that. We need to feel their pain and their success and their triumphs," he said. "Those are the areas that get me most hopeful."
AI can't replace genuine human empathy, Ohanian suggested.
No matter what jobs robots take over from us in the future, fields of work where empathy is a core component of the job will have an advantage, he said. And that's why one of the most important, marketable skills he's teaching his kids is empathy, he said.
Apple
Apple is facing backlash over one of its newest generative AI features.
The feature, available in the latest iOS 18.2 update, summarizes groups of notifications from an app on a user's iPhone to give them a quick rundown of what they missed at a glance.
Users, however, have pointed out at least two instances of it providing inaccurate information when attempting to summarize notifications from news organizations.
In one case, the summary falsely claimed the BBC reported that Luigi Mangione, the suspect in the killing of UnitedHealthcare CEO Brian Thompson, had killed himself. Mangione is alive and was extradited to New York on Thursday.
In another instance, the feature wrongly summarized a New York Times article to say that Israeli Prime Minister Benjamin Netanyahu had been arrested. The NYT article actually reported that the International Criminal Court had issued a warrant for Netanyahu's arrest, not that he had been arrested.
The nonprofit Reporters Without Borders has called on Apple to remove the feature.
"AIs are probability machines, and facts can't be decided by a roll of the dice," said Vincent Berthier, the head of the group's technology and journalism desk, in a public statement this week. "The automated production of false information attributed to a media outlet is a blow to the outlet's credibility and a danger to the public's right to reliable information on current affairs."
Apple, the BBC, the NYT, and Reporters Without Borders did not immediately respond to requests for comment from Business Insider.
A spokesperson from the BBC previously said the organization has filed a complaint with Apple "to raise this concern and fix the problem."
"It is essential to us that our audiences can trust any information or journalism published in our name and that includes notifications," the spokesperson previously said.
OpenAI saved its biggest announcement for the last day of its 12-day “shipmas” event. On Friday, the company unveiled o3, the successor to the o1 “reasoning” model it released earlier in the year. o3 is a model family, to be more precise — as was the case with o1. There’s o3 and o3-mini, a smaller, […]
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In September, OpenAI unveiled a radically new type of AI model called o1. In a matter of months, rivals introduced similar offerings.
On Thursday, Google released Gemini 2.0 Flash Thinking, which uses reasoning techniques that look a lot like o1.
Even before that, in November, a Chinese company announced DeepSeek, an AI model that breaks challenging questions down into more manageable tasks like OpenAI's o1 does.
This is the latest example of a crowded AI frontier where pricey innovations are swiftly matched, making it harder to stand out.
"It's amazing how quickly AI model improvements get commoditized," Rahul Sonwalkar, CEO of the startup Julius AI, said. "Companies spend massive amounts building these new models, and within a few months they become a commodity."
The proliferation of multiple AI models with similar capabilities could make it difficult to justify charging high prices to use these tools. The price of accessing AI models has indeed plunged in the past year or so.
That, in turn, could raise questions about whether it's worth spending hundreds of millions of dollars, or even billions, to build the next top AI model.
When OpenAI previewed its o1 model in September, the product was hailed as a breakthrough. It uses a new approach called inference-time compute to answer more challenging questions.
It does this by slicing queries into more digestible tasks and turning each of these stages into a new prompt that the model tackles. Each step requires running a new request, which is known as the inference stage in AI.
This produces a chain of thought or chain of reasoning in which each part of the problem is answered, and the model doesn't move on to the next stage until it ultimately comes up with a full response.
The model can even backtrack and check its prior steps and correct errors, or try solutions and fail before trying something else. This is akin to how humans spend longer working through complex tasks.
In a mere two months, o1 had a rival. On November 20, a Chinese AI company released DeepSeek.
"They were probably the first ones to reproduce o1," said Charlie Snell, an AI researcher at UC Berkeley who coauthored a Google DeepMind paper this year on inference-time compute.
He's tried DeepSeek's AI model and says it performs well on complex math problems that must be solved by thinking for longer and in stages.
He noted that in DeepSeek's DeepThink mode, the model shows users every step of its thought process. With o1, these intermediate steps are hidden from users.
"I've asked people at OpenAI what they think of it," Snell told BI. "They say it looks like the same thing, but they don't how DeepSeek did this so fast."
OpenAI didn't respond to a request for comment. On Friday, the startup previewed an o1 successor, called o3. Francois Chollet, a respected AI expert, called the update a "significant breakthrough."
Andrej Karpathy, an OpenAI cofounder, praised Google's new "Thinking" model for the same reasoning feature.
"The prominent and pleasant surprise here is that unlike o1 the reasoning traces of the model are shown," he wrote on X. "As a user I personally really like this because the reasoning itself is interesting to see and read — the models actively think through different possibilities, ideas, debate themselves, etc., it's part of the value add."
Snell shared a multistep math problem with Business Insider, which we used to test DeepSeek for ourselves:
"Find a sequence of +, -, /, * which can be applied to the numbers 7, 3, 11, 5 to get to 24, where each of the given numbers is used exactly once."
BI put that prompt in DeepSeek's chat window on its website. The model responded initially by laying out the challenge ahead.
"Alright, so I've got this problem here: I need to use the numbers 7, 3, 11, and 5, and combine them with the operations of addition, subtraction, multiplication, and division, using each number exactly once, to get to 24," it replied. "At first glance, this seems a bit tricky, but I think with some systematic thinking, I can figure it out."
It then proceeded through multiple steps over roughly 16 pages of discussion that included mathematical calculations and equations. The model sometimes got it wrong, but it spotted this and didn't give up. Instead, it swiftly moved on to another possible solution.
"Almost got close there with 33 / 7 * 5 ≈ 23.57, but not quite 24. Maybe I need to try a different approach," it wrote at one point.
After a few minutes, it found the correct solution.
"You can see it try different ideas and backtrack," Snell said in an interview on Wednesday. He highlighted this part of DeepSeek's chain of thought as particularly noteworthy:
"This is getting really time-consuming. Maybe I need to consider a different strategy," the AI model wrote. "Instead of combining two numbers at a time, perhaps I should look for a way to group them differently or use operations in a nested manner."
Snell said other companies are likely working on AI models that use the same inference-time compute approach as OpenAI.
"DeepSeek does this already, so I assume others are working on this," he added on Wednesday.
The following day, Google released Gemini 2.0 Flash Thinking. Like DeepSeek, this new model shows users each step of its thought process while tackling problems.
Jeff Dean, a Google AI veteran, shared a demo on X that showed this new model solving a physics problem and explained its reasoning steps.
"This model is trained to use thoughts to strengthen its reasoning," Dean wrote. "We see promising results when we increase inference time computation!"
