Media is a business about dreams, and Larry Ellison’s son is dreaming big. This might explain why the case for Paramount Skydance to buy Warner Bros. Discovery is so incoherent.
Artificial Intelligence
Larry Ellison’s big dumb gift to his large adult son
In October, Warner Bros. put itself up for sale, leading to a number of bids. The two we are concerned with are a bid from Netflix and another from two nepo babies: David Ellison and Jared Kushner. David Ellison is the head of Paramount, but most famous for being Larry’s son. Jared Kushner is most famous for being Donald Trump’s son-in-law, though he also got his start in business by taking over his felon father’s firm when Charles was in prison; his firm is involved in the financing.
Netflix won the bidding. Warner Bros. made an agreement to sell most of its business — the studio part — to the streaming giant for $83 billion, including debt. (That figure is a bit more than five times Paramount’s market cap.) Warner Bros. felt that spinning itself into two companies, the Netflix acquisition and the cable networks, gave shareholders a value of $31 to $32 a share, rather than the $30 a share Paramount was offering, according to The Wall Street Journal.
If the elder Ellison is serious about this, I’d like some of what he’s smoking
Nonetheless, Paramount announced a hostile bid to take over Warner Bros. for $30 per share, which puts the total at $108.4 billion, including debt. Apparently, Paramount has been after Warner Bros. for the last two years, even before Ellison père et fils entered the picture. But now, it’s backstopped the deal with the Ellison family trust, which includes a war chest of about 1.16 billion Oracle shares. The current offer from Paramount is not the “best and final” — at least, according to The New York Times — so this dumb fight is likely to drag on for a while.
Let’s pause here and notice that what Paramount is doing doesn’t make any fucking sense. At least, not business sense. Maybe I can see a case for consolidation among the streamers, rolling HBO Max into Paramount Plus, and acquiring a larger library. But HBO Max isn’t that big, and its library isn’t exclusive. So why would Larry Ellison trade Oracle stock (with, at maximum, AI-rules-the-world multiples) for garbage media multiples? If the elder Ellison is serious about this, I’d like some of what he’s smoking.
Warner Bros., in fact, also appears concerned about how serious Ellison senior is. Part of the reason Warner Bros. went with the Netflix bid was that its board was “worried that Mr. [Larry] Ellison did not personally guarantee the bid under his name and is planning to contribute equity for the deal through a trust with holdings that could be modified at any time,” wrote The New York Times. Yeah, I’d worry about that shit too!
And there are signs Larry Ellison doesn’t see his son’s vision as a sure thing, since he’s not the sole bankroll involved. The deal relies on third parties’ money, which includes $24 billion from three Middle Eastern funds. That’s in addition, of course, to Kushner’s Affinity Partners and Apollo Global Management, both of which manage significant money from other Middle Eastern investors. Larry Ellison’s trust, which he can change at any time, is only a backstop on this financing.
Still, Larry Ellison has called the White House to whine about antitrust concerns in the Netflix-Warner Bros. deal, which is not nothing.
At this point, you may be wondering what David Ellison’s case is for combining Paramount and Warner Bros. Here’s what he has to say: “Unless you can build a tech product that is truly competitive with what’s coming out of Silicon Valley, you can’t compete,” David Ellison said, according to The Hollywood Reporter. “And that has been one of the big problems that’s been facing legacy media, is they don’t actually understand that skill set and how critical that is, and that it is actually a combination of great content working with tech product hand in hand, that is how you actually get this business growing and scaling again, and you need both.”
First, David Ellison wants the Netflix multiple on his company. Second, he doesn’t understand how anything works.
This is vacuous nonsense. In an open letter about his company Skydance’s acquisition of Paramount, David Ellison pitched a plan to combine Pluto TV with Paramount Plus — wowie zowie, combining streaming platforms! There’s also some manner of plan to use AI slop to “supercharge” the company’s intellectual property and “content.” (Hear that, directors? You don’t make TV shows or movies. You make slop for the slop machine.) And improving recommendations will probably mean attempting to use the TikTok algorithm, since Daddy is involved — at least, if the TikTok deal ever goes through. So that’s the Paramount pitch. It is also, most likely, the Warner Bros. pitch.
David Ellison’s plan for Paramount, insofar as it is one, reveals two things. First, David Ellison wants the Netflix multiple on his company. Second, he doesn’t understand how anything works.
If I wanted to turn Paramount into a tech company, I might try linking product placement in Paramount’s existing library to actual shopping, for a modest fee to the brand, and perhaps a cut of any sales made. Or figure out how to make streaming faster and more reliable, or come up with new methods of compression to make delivery less costly. Perhaps I’d move into streaming video games, unlocking a huge market for my platform — which for the record is exactly what Netflix is doing right now. To do all — or any! — of that, I’d probably make a big impressive tech hire.
Instead, Ellison’s flashy hire is fellow know-nothing Bari Weiss. Weiss is the head of CBS News who for some reason is appearing on television to record flop interviews instead of doing her actual job, which is running a news organization. Hey, did you hear that David Ellison has been promising the White House it’ll do to CNN what it did to CBS News? It’s very funny that he is offering to pay a premium to drive the TV business into the ground faster, don’t you think?
Without any actual ideas, no Netflix multiple is going to be forthcoming on the Paramount stock
I guess we could get caught up in the culture war argument: Both Ellisons simply buy into Trump’s desire to destroy the supposed left-wing bastion of TV news. (The revanchist right has been particularly infuriated by their inability to dominate cultural products as they dominate politics.) But as a business decision, it is so stupid. Broadcast TV has been declining in importance for some time now. As of October, streaming has taken over nearly half of monthly TV viewing. In the streaming wars, there are only really two important platforms: Netflix and YouTube. Those two streamers combined account for a bit over 20 percent of the entire TV audience; broadcast TV as a whole accounts for 23 percent. Paramount Plus and Pluto TV together account for about 2 percent of the market.
Netflix, as a streaming platform, is stealing viewers from cable and broadcast television. David Ellison’s big idea to compete is combining streaming platforms and making vague noises about AI — and, I guess, TV? Without any actual ideas, no Netflix multiple is going to be forthcoming on the Paramount stock.
Now let’s consider Netflix, which actually is a tech company. Making a direct comparison of the deals is slightly complicated by the fact that Paramount wants the whole of Warner Bros. and Netflix wants to spin off the declining cable holdings, which include CNN. This is probably smart! What Netflix gets in the deal is a huge movie library it doesn’t have to negotiate rights for — and which it might be able to generate royalties from, should it choose to continue licensing to rivals. Historically, though, Netflix doesn’t do that, which may mean it’ll give consumers a fairly powerful reason to switch streaming services.
By comparison, it’s just not clear what the Ellisons think they’re doing. Sure, combining with HBO Max and forming one streaming service might be helpful, but that takes Paramount Plus from 2 percent of the market to 3.5 percent of the market. Paramount could similarly make a bunch of Hollywood history exclusive to its streaming service, but so what? It’s already got movies and TV shows and the means to make more. Is David Ellison really just trying to buy his way to importance?
We’ve got a real-life Kendall Roy on our hands
One more thing about Netflix. It has two CEOs: Ted Sarandos, who is Mr. Hollywood, and Greg Peters, who explicitly handles tech. David Ellison has the experience and talents of neither. Before founding Skydance, Ellison personally paid for a third of the $60 million budget for the bomb Flyboys, which costarred Ellison and James Franco. The movie made $18 million. Next, he founded Skydance, and his dad put in $150 million of the initial $350 million the company raised to finance movies with Paramount. Skydance’s trajectory has been uneven, with hits such as True Grit and stinkers such as Terminator: Dark Fate. Ellison quit acting in 2010, when Taylor Lautner backed out of a movie Ellison had written after finding out that Ellison also planned to costar.
So David Ellison took his imagination elsewhere. It seems his new vision is to become a mogul on par with Sumner Redstone, Barry Diller, or Rupert Murdoch; we’ve got a real-life Kendall Roy on our hands. The question now is how committed Larry Ellison is to buying that dream for his son. Sure, he was involved in the early negotiations for Warner Bros. Yes, he phoned the White House to try to scupper the Netflix deal. But neither of those are nearly as serious as Oracle shares. Acquiring Warner Bros. doesn’t do what’s needed to pivot Paramount into a tech company, either. So, much like Warner Bros.’ board, I don’t believe that Larry Ellison is going to trade Oracle shares for a media company. It just doesn’t make sense.
Under what circumstances would Larry Ellison decide to do it anyway? There are two things I can think of. Both of them have to do with Oracle, which has pivoted toward an enormous AI bet, taking on a tremendous debt load in the process. The company is spending a frankly shocking amount to build out data centers, based on revenue it expects from OpenAI — which may not be able to meet its obligations.
We are in the era of gangster tech regulation. Buying up the parent company of CNN, which Trump famously loathes, and reworking it to flatter him could maybe get Larry Ellison the political capital he’d need to get bailed out if OpenAI fails on its obligations. It might also get him the first line on federal contracts. Who can say? For our TV-addled president, maybe owning a couple channels is the most effective form of lobbying there is. But if this is true, Paramount’s bid for Warner Bros. isn’t about making a media company into a tech company — it’s about figuring out another way to butter up an astonishingly corrupt administration. It’s also a pretty risky gamble that no one who opposes Trump will come into power anytime soon.
The second possibility is that Larry Ellison has already recognized AI isn’t going to live up to its hype. If that is the case, he might as well spend money while he has it. Maybe buying his son’s love is worth it. After all, Larry Ellison is already the most important shareholder of Paramount. Why not expand the playhouse a little?
Correction, December 16th: The Netflix bid is worth $31 to $32 per share, not $31 to $31.
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Artificial Intelligence
Ronnie Sheth, CEO, SENEN Group: Why now is the time for enterprise AI to ‘get practical'
Before you set sail on your AI journey, always check the state of your data – because if there is one thing likely to sink your ship, it is data quality.
Gartner estimates that poor data quality costs organisations an average of $12.9 million each year in wasted resources and lost opportunities. That’s the bad news. The good news is that organisations are increasingly understanding the importance of their data quality – and less likely to fall into this trap.
That’s the view of Ronnie Sheth, CEO of AI strategy, execution and governance firm SENEN Group. The company focuses on data and AI advisory, operationalisation and literacy, and Sheth notes she has been in the data and AI space ‘ever since [she] was a corporate baby’, so there is plenty of real-world experience behind the viewpoint. There is also plenty of success; Sheth notes that her company has a 99.99% client repeat rate.
“If I were to be very practical, the one thing I’ve noticed is companies jump into adopting AI before they’re ready,” says Sheth. Companies, she notes, will have an executive direction insisting they adopt AI, but without a blueprint or roadmap to accompany it. The result may be impressive user numbers, but with no measurable outcome to back anything up.
Even as recently as 2024, Sheth saw many organisations struggling because their data was ‘nowhere where it needed to be.’ “Not even close,” she adds. Now, the conversation has turned more practical and strategic. Companies are realising this, and coming to SENEN Group initially to get help with their data, rather than wanting to adopt AI immediately.
“When companies like that come to us, the first course of order is really fixing their data,” says Sheth. “The next course of order is getting to their AI model. They are building a strong foundation for any AI initiative that comes after that.
“Once they fix their data, they can build as many AI models as they want, and they can have as many AI solutions as they want, and they will get accurate outputs because now they have a strong foundation,” Sheth adds.
With breadth and depth in expertise, SENEN Group allows organisations to right their course. Sheth notes the example of one customer who came to them wanting a data governance initiative. Ultimately, it was the data strategy which was needed – the why and how, the outcomes of what they were trying to do with their data – before adding in governance and providing a roadmap for an operating model. “They’ve moved from raw data to descriptive analytics, moving into predictive analytics, and now we’re actually setting up an AI strategy for them,” says Sheth.
It is this attitude and requirement for practical initiatives which will be the cornerstone of Sheth’s discussion at AI & Big Data Expo Global in London this week. “Now would be the time to get practical with AI, especially enterprise AI adoption, and not think about ‘look, we’re going to innovate, we’re going to do pilots, we’re going to experiment,’” says Sheth. “Now is not the time to do that. Now is the time to get practical, to get AI to value. This is the year to do that in the enterprise.”
Watch the full video conversation with Ronnie Sheth below:
Greg Holmes, Field CTO for EMEA at Apptio, an IBM company, argues that successfully scaling intelligent automation requires financial rigour.
The “build it and they will come” model of technology adoption often leaves a hole in the budget when applied to automation. Executives frequently find that successful pilot programmes do not translate into sustainable enterprise-wide deployments because initial financial modelling ignored the realities of production scaling.
“When we integrate FinOps capabilities with automation, we’re looking at a change from being very reactive on cost management to being very proactive around value engineering,” says Holmes.
This shifts the assessment criteria for technical leaders. Rather than waiting “months or years to assess whether things are getting value,” engineering teams can track resource consumption – such as cost per transaction or API call – “straight from the beginning.”
The unit economics of scaling intelligent automation
Innovation projects face a high mortality rate. Holmes notes that around 80 percent of new innovation projects fail, often because financial opacity during the pilot phase masks future liabilities.
“If a pilot demonstrates that automating a process saves, say, 100 hours a month, leadership thinks that’s really successful,” says Holmes. “But what it fails to track is that the pilot sometimes is running on over-provisioned infrastructure, so it looks like it performs really well. But you wouldn’t over-provision to that degree during a real production rollout.”
Moving that workload to production changes the calculus. The requirements for compute, storage, and data transfer increase. “API calls can multiply, exceptions and edge cases appear at volume that might have been out of scope for the pilot phase, and then support overheads just grow as well,” he adds.
To prevent this, organisations must track the marginal cost at scale. This involves monitoring unit economics, such as the cost per customer served or cost per transaction. If the cost per customer increases as the customer base grows, the business model is flawed.
Conversely, effective scaling should see these unit costs decrease. Holmes cites a case study from Liberty Mutual where the insurer was able to find around $2.5 million of savings by bringing in consumption metrics and “not just looking at labour hours that they were saving.”
However, financial accountability cannot sit solely with the finance department. Holmes advocates for putting governance “back in the hands of the developers into their development tools and workloads.”
Integration with infrastructure-as-code tools like HashiCorp Terraform and GitHub allows organisations to enforce policies during deployment. Teams can spin up resources programmatically with immediate cost estimates.
“Rather than deploying things and then fixing them up, which gets into the whole whack-a-mole kind of problem,” Holmes explains, companies can verify they are “deploying the right things at the right time.”
When scaling intelligent automation, tension often simmers between the CFO, who focuses on return on investment, and the Head of Automation, who tracks operational metrics like hours saved.
“This translation challenge is precisely what TBM (Technology Business Management) and Apptio are designed to solve,” says Holmes. “It’s having a common language between technology and finance and with the business.”
The TBM taxonomy provides a standardised framework to reconcile these views. It maps technical resources (such as compute, storage, and labour) into IT towers and further up to business capabilities. This structure translates technical inputs into business outputs.
“I don’t necessarily know what goes into all the IT layers underneath it,” Holmes says, describing the business user’s perspective. “But because we’ve got this taxonomy, I can get a detailed bill that tells me about my service consumption and precisely which costs are driving it to be more expensive as I consume more.”
Addressing legacy debt and budgeting for the long-term
Organisations burdened by legacy ERP systems face a binary choice: automation as a patch, or as a bridge to modernisation. Holmes warns that if a company is “just trying to mask inefficient processes and not redesign them,” they are merely “building up more technical debt.”
A total cost of ownership (TCO) approach helps determine the correct strategy. The Commonwealth Bank of Australia utilised a TCO model across 2,000 different applications – of various maturity stages – to assess their full lifecycle costs. This analysis included hidden costs such as infrastructure, labour, and the engineering time required to keep automation running.
“Just because of something’s legacy doesn’t mean you have to retire it,” says Holmes. “Some of those legacy systems are worth maintaining just because the value is so good.”
In other cases, calculating the cost of the automation wrappers required to keep an old system functional reveals a different reality. “Sometimes when you add up the TCO approach, and you’re including all these automation layers around it, you suddenly realise, the real cost of keeping that old system alive is not just the old system, it’s those extra layers,” Holmes argues.
Avoiding sticker shock requires a budgeting strategy that balances variable costs with long-term commitments. While variable costs (OPEX) offer flexibility, they can fluctuate wildly based on demand and engineering efficiency.
Holmes advises that longer-term visibility enables better investment decisions. Committing to specific technologies or platforms over a multi-year horizon allows organisations to negotiate economies of scale and standardise architecture.
“Because you’ve made those longer term commitments and you’ve standardised on different platforms and things like that, it makes it easier to build the right thing out for the long term,” Holmes says.
Combining tight management of variable costs with strategic commitments supports enterprises in scaling intelligent automation without the volatility that often derails transformation.
IBM is a key sponsor of this year’s Intelligent Automation Conference Global in London on 4-5 February 2026. Greg Holmes and other experts will be sharing their insights during the event. Be sure to check out the day one panel session, Scaling Intelligent Automation Successfully: Frameworks, Risks, and Real-World Lessons, to hear more from Holmes and swing by IBM’s booth at stand #362.
See also: Klarna backs Google UCP to power AI agent payments
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events including the Cyber Security & Cloud Expo. Click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
FedEx is using AI to change how package tracking and returns work for large enterprise shippers. For companies moving high volumes of goods, tracking no longer ends when a package leaves the warehouse. Customers expect real-time updates, flexible delivery options, and returns that do not turn into support tickets or delays.
That pressure is pushing logistics firms to rethink how tracking and returns operate at scale, especially across complex supply chains.
This is where artificial intelligence is starting to move from pilot projects into daily operations.
FedEx plans to roll out AI-powered tracking and returns tools designed for enterprise shippers, according to a report by PYMNTS. The tools are aimed at automating routine customer service tasks, improving visibility into shipments, and reducing friction when packages need to be rerouted or sent back.
Rather than focusing on consumer-facing chatbots, the effort centres on operational workflows that sit behind the scenes. These are the systems enterprise customers rely on to manage exceptions, returns, and delivery changes without manual intervention.
How FedEx is applying AI to package tracking
Traditional tracking systems tell customers where a package is and when it might arrive. AI-powered tracking takes a step further by utilising historical delivery data, traffic patterns, weather conditions, and network constraints to flag potential delays before they happen.
According to the PYMNTS report, FedEx’s AI tools are designed to help enterprise shippers anticipate issues earlier in the delivery process. Instead of reacting to missed delivery windows, shippers may be able to reroute packages or notify customers ahead of time.
For businesses that ship thousands of parcels per day, that shift matters. Small improvements in prediction accuracy can reduce support calls, lower refund rates, and improve customer trust, particularly in retail, healthcare, and manufacturing supply chains.
This approach also reflects a broader trend in enterprise software, in which AI is being embedded into existing systems rather than introduced as standalone tools. The goal is not to replace logistics teams, but to minimise the number of manual decisions they need to make.
Returns as an operational problem, not a customer issue
Returns are one of the most expensive parts of logistics. For enterprise shippers, particularly those in e-commerce, returns affect warehouse capacity, inventory planning, and transportation costs.
According to PYMNTS, FedEx’s AI-enabled returns tools aim to automate parts of the returns process, including label generation, routing decisions, and status updates. Companies that use AI to determine the most efficient return path may be able to reduce delays and avoid returning things to the wrong facility.
This is less about convenience and more about operational discipline. Returns that sit idle or move through the wrong channel create cost and uncertainty across the supply chain. AI systems trained on past return patterns can help standardise decisions that were previously handled case by case.
For enterprise customers, this type of automation supports scale. As return volumes fluctuate, especially during peak seasons, systems that adjust automatically reduce the need for temporary staffing or manual overrides.
What FedEx’s AI tracking approach says about enterprise adoption
What stands out in FedEx’s approach is how narrowly focused the AI use case is. There are no broad claims about transformation or reinvention. The emphasis is on reducing friction in processes that already exist.
This mirrors how other large organisations are adopting AI internally. In a separate context, Microsoft described a similar pattern in its article. The company outlined how AI tools were rolled out gradually, with clear limits, governance rules, and feedback loops.
While Microsoft’s case focused on knowledge work and FedEx’s on logistics operations, the underlying lesson is the same. AI adoption tends to work best when applied to specific activities with measurable results rather than broad promises of efficiency.
For logistics firms, those advantages include fewer delivery exceptions, lower return handling costs, and better coordination between shipping partners and enterprise clients.
What this signals for enterprise customers
For end-user companies, FedEx’s move signals that logistics providers are investing in AI as a way to support more complex shipping demands. As supply chains become more distributed, visibility and predictability become harder to maintain without automation.
AI-driven tracking and returns could also change how businesses measure logistics performance. Companies may focus less on delivery speed and more on how quickly issues are recognised and resolved.
That shift could influence procurement decisions, contract structures, and service-level agreements. Enterprise customers may start asking not just where a shipment is, but how well a provider anticipates problems.
FedEx’s plans reflect a quieter phase of enterprise AI adoption. The focus is less on experimentation and more on integration. These systems are not designed to draw attention but to reduce noise in operations that customers only notice when something goes wrong.
(Photo by Liam Kevan)
See also: PepsiCo is using AI to rethink how factories are designed and updated
Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
AI News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
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