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Tamara News  /  Blog  /  AI
14 Sep 2026
Apple Siri Google Gemini

Apple Just Answered the Foldable Rumors. The Bigger Change Was Inside Siri

Categories Tech

Apple Siri Google Gemini integration became official at Apple’s September 9 event. The company confirmed it is rebuilding Siri on Google’s Gemini models. It also unveiled its first foldable iPhone. Those were the two headline moves in what Apple called its biggest device wave in years. It was also the first keynote led by new device chief John Ternus.

What the Apple Siri Google Gemini Deal Actually Covers

Apple struck a deal with Google to license Gemini models. Those models now power upgraded Apple Intelligence features and a rebuilt Siri. Building an equivalent large language model entirely in-house, on Apple’s needed timeline, wasn’t realistic. Reporting on the partnership says the arrangement lets Apple ship more capable, conversational Siri features sooner than an internal model would have allowed. Google, in turn, gains a high-profile distribution deal across hundreds of millions of iPhones. Neither company has published the full financial terms of the agreement.

Our Preview Called the Foldable Rumor Right

Apple Siri Google Gemini

Tamara News previewed this event on September 8. At that point, the foldable iPhone was still an unconfirmed rumor riding on Ternus’s first keynote as device chief. At the event itself, Apple confirmed the device. It positioned the foldable as the centerpiece of a broader hardware refresh, not a niche add-on to the standard iPhone lineup. Foldable phones have belonged to Samsung and Huawei for years. Apple’s entry is likely to intensify competition on pricing and on software built specifically for folding displays.

Why Apple Chose Partnership Over Building Its Own Model

Apple’s AI efforts drew criticism over the past two years for lagging competitors on generative features, particularly conversational assistants. Partnering with Google for Gemini is a pragmatic call. Catching up on foundation-model quality alone would have taken longer than Apple’s product cycle allowed. The move also mirrors a broader 2026 pattern: large tech companies choosing high-profile AI partnerships and acquisitions, including Nvidia’s own multibillion-dollar deals, over purely organic development.

What This Means for the Wider AI Assistant Race

A Gemini-powered Siri puts Google’s models at the center of the world’s most widely used smartphone assistant. That distribution win could matter more than any single Google-branded product. For competitors, it raises a hard question. Can Amazon’s Alexa and Microsoft’s Copilot match a partnership of this scale, or will they need similar arrangements of their own? Apple, meanwhile, keeps its hardware and interface advantages while outsourcing the model layer underneath. Other device makers are likely to study that split closely.

What Reviewers Will Test First

Early hands-on coverage typically focuses on two things with a device like this: hinge durability and how visible the fold line is on screen. Expect reviewers to bend the new iPhone repeatedly, check for screen creasing under different lighting, and compare battery life against Apple’s standard, non-folding models. On the software side, testers will likely push Siri with multi-step requests to see how the Gemini-powered version handles follow-up questions compared with the old, more scripted Siri.

How This Fits Apple’s Broader AI Strategy

Apple spent the past two years insisting its AI approach would prioritize privacy and on-device processing over cloud-dependent models. The Gemini partnership complicates that message somewhat, since it relies on a third party’s cloud-based models for Siri’s most advanced features. Apple says the partnership follows its existing privacy commitments for handling user data. The company has not published full technical detail on how requests get routed, or what Google can see on its end.

How Competitors Are Likely to Respond

Samsung and Google’s own Pixel team already ship generative AI assistants built on in-house or closely integrated models, giving them a head start on tight software-hardware coordination that Apple’s outsourced approach doesn’t offer in the same way. Amazon, meanwhile, has invested heavily in a next-generation Alexa overhaul of its own. Analysts expect at least one competitor to announce a comparable large-model partnership within the next two quarters, simply to avoid ceding ground on assistant quality.

What to Watch as the Rollout Continues

Apple has not detailed a full rollout timeline for every Gemini-powered feature. Foldable iPhone reviews and durability testing will shape early public reaction once the device reaches reviewers. This week’s Apple earnings call, part of a broader stretch of major tech reporting, is the next concrete moment. Apple is expected to use that call to address how the launch affects sales in the current quarter.

Frequently Asked Questions

Is Siri now powered by Google Gemini?
Apple confirmed at its September 9 event that it is rebuilding Siri using Google’s Gemini models, under a licensing partnership between the two companies.

Did Apple actually release a foldable iPhone?
Yes. Apple confirmed its first foldable iPhone at the same event. That ended months of speculation built around new device chief John Ternus’s debut keynote.

Why didn’t Apple build its own AI model for Siri?
Apple’s in-house AI efforts had lagged competitors on conversational features. Partnering with Google let it ship more capable Siri features on a faster timeline.

What are the financial terms of the Apple-Google deal?
Neither company has published the full financial details of the Gemini licensing arrangement.

What happens next for Apple after this announcement?
Apple reports quarterly earnings this week. Analysts expect that to be the first chance for the company to address how the launch affects current-quarter sales.

Sources

  • CNBC — Apple picks Google’s Gemini to run AI-powered Siri. cnbc.com
  • Forbes — Apple Event Kicks Off 2026-2027 Tech Season. forbes.com

01 Sep 2026
AI chip interconnect bottleneck

The Real AI Chip Bottleneck Isn’t the Chip — It’s the Wires

Categories Tech

The AI chip interconnect bottleneck took center stage as SEMICON Taiwan 2026 opened in Taipei on 31 August. The event drew more than 100,000 semiconductor professionals from 65 countries to International Semiconductor Week. SEMI’s Terry Tsao delivered the conference’s central message, and it was blunt. Moving data between AI chips can now consume more energy than the computation itself. That shifts the industry’s hardest problem from individual chip performance to system-level architecture.

Why the AI chip interconnect bottleneck changes the conversation

For years, chipmakers competed primarily on transistor density and raw compute throughput. SEMICON Taiwan’s 2026 agenda reflects a shift toward a different metric entirely. The new focus is how efficiently data moves between thousands of chips working together inside a single AI training cluster. AI models have grown, and so has the number of chips that must communicate constantly during training. The wiring and interconnect standards linking them have not kept pace with compute gains. That gap creates exactly the kind of bottleneck Tsao described.

AI chip interconnect bottleneck

Who is racing to solve the wiring problem

Nvidia has staked much of its roadmap on faster interconnect standards to keep pace with the chips it sells. Its GPU Technology Conference earlier this year saw Jensen Huang announce roughly $1 trillion in expected orders for its Blackwell and Vera Rubin chip generations through 2027. Those orders depend partly on solving the same data-movement problem SEMICON Taiwan highlighted. Component suppliers focused on optical and copper interconnect technology have seen renewed investor interest as a result. Faster, more energy-efficient connections between chips could unlock compute gains that raw chip design alone cannot deliver.

The regulatory backdrop shaping who can compete

The interconnect race is unfolding alongside tightening US export controls on advanced AI chips and the semiconductors that power them. The Commerce Department has signaled further regulatory action on chips and AI is coming. The Department has separately signed letters of intent worth $874 million with seven companies to strengthen the domestic compute supply chain. Those two threads are tighter export rules abroad and new incentives at home. Together they are pushing US-based chip and interconnect makers to treat domestic manufacturing capacity as a competitive necessity, not a cost center.

Taiwan’s own chipmakers face a delicate balance in this environment. They supply both US and Chinese customers. Interconnect standards adopted at events like SEMICON Taiwan increasingly carry geopolitical weight alongside their technical merits. TSMC and other Taiwanese suppliers have avoided taking public positions on which standard should win out. They prefer to support multiple approaches until the market settles on a default, a stance that lets them keep selling into both American and Chinese supply chains without picking a side.

How this affects AI training timelines industry-wide

Data-center operators say interconnect limits already stretch some training runs longer than the chips themselves would require. A cluster with thousands of top-tier chips gains little if those chips spend significant time waiting on data transfers rather than computing. That waiting time translates directly into higher costs. Labs still pay for chip time regardless of utilization. Several cloud providers have begun advertising interconnect specifications alongside raw chip counts for the first time this year. Industry analysts say the shift reflects customer demand for clearer efficiency metrics. Buyers increasingly ask about bandwidth between chips before they ask about raw chip counts, according to several vendors present at the conference.

Smaller AI labs without the scale to negotiate custom interconnect solutions face a particular disadvantage. They typically rely on off-the-shelf networking gear that larger labs have already moved past. That gap can leave them paying similar chip costs for meaningfully worse effective performance. Several smaller labs have started pooling compute purchases specifically to negotiate better interconnect terms, mimicking a strategy large cloud providers pioneered years earlier. Industry groups say more such consortiums are likely to form if interconnect costs keep rising faster than raw chip prices.

What happens next

International Semiconductor Week runs through the rest of the week in Taipei. More technical sessions are expected to detail specific interconnect standards vying to become the industry default. Whichever approach gains traction will shape how quickly AI labs can actually deploy the chips they are paying for. That includes labs racing to fund new compute capacity, like DeepSeek.

Frequently Asked Questions

What is the AI chip interconnect bottleneck?

It refers to the energy and speed limits of moving data between AI chips working together in a cluster, which industry leaders now say can consume more energy than the chips’ own computation.

What is SEMICON Taiwan 2026?

It is International Semiconductor Week’s flagship event, held in Taipei starting 31 August 2026 and drawing more than 100,000 semiconductor professionals from 65 countries.

Why does this matter for Nvidia and its rivals?

Nvidia’s roadmap depends on faster interconnect standards to support roughly $1 trillion in expected orders for its Blackwell and Vera Rubin chips through 2027.

How do US export controls factor in?

Tightening restrictions on advanced AI chips are pushing chipmakers to treat domestic manufacturing and interconnect capacity as strategically necessary, alongside new federal incentives for the compute supply chain.

Related Coverage

Sources

01 Sep 2026
DeepSeek funding round

Inside the DeepSeek Funding Round That Could Precede a 2027 IPO

Categories Tech

A DeepSeek funding round worth roughly 50 billion yuan, about $7.4 billion, is nearing a close. Sources cited by China Money Network put the pre-money valuation near $74 billion. DeepSeek is the Chinese AI lab that rattled US markets in January 2026. Its model matched Western systems at a fraction of the training cost. DeepSeek is using this raise to fund about 1 gigawatt of new compute capacity, ahead of a planned initial public offering.

What the DeepSeek funding round is actually paying for

The bulk of the new capital is earmarked for compute infrastructure, not research headcount. That reflects how much of the competitive pressure in frontier AI now comes from access to chips and data-center capacity, rather than algorithmic breakthroughs alone. DeepSeek is racing rivals including Alibaba’s Qwen, Tencent’s Hy4, and Zhipu’s GLM for both compute and engineering talent. Sources familiar with the round say the financing is intended to close by the end of August. That would position DeepSeek to file for an IPO before the end of 2026, with a public debut targeted for 2027 on Shanghai’s Star Market.

DeepSeek funding round

Image credit: Feliciagrace-bytesrack / Wikimedia Commons (CC BY 4.0)

Why this funding round matters beyond DeepSeek

DeepSeek’s January 2026 model release briefly wiped more than $500 billion from AI-linked US equities. It demonstrated that competitive AI systems no longer required the enormous training budgets US labs had assumed were necessary. This new funding round suggests the opposite lesson is now taking hold inside DeepSeek itself. Staying competitive at the frontier still requires massive, sustained capital, even for a lab that built its reputation on efficiency. That shift has implications for every AI lab claiming a cost advantage. It implies efficiency gains buy time rather than permanently lower capital requirements.

The chip-supply backdrop shaping the round

DeepSeek’s fundraising lands against a backdrop of tightening US export controls on advanced AI chips. The Commerce Department has signaled further regulatory action on chips and AI is coming. Chinese AI labs have had to route around restrictions on the most advanced Nvidia and AMD chips. A war chest of this size gives DeepSeek more room to secure whatever compute capacity remains available to it. That capacity could come through domestic chipmakers or through channels that comply with existing export rules.

Analysts tracking China’s AI sector say the round also signals confidence from domestic investors that Beijing will keep backing frontier AI development despite the external pressure. That confidence matters as much as the capital itself, since it shapes whether other Chinese labs can raise on similar terms.

How investors are pricing DeepSeek against its global rivals

A $74 billion valuation puts DeepSeek well below OpenAI and Anthropic on paper. It still ranks among the most valuable AI labs outside the United States. Private investors backing the round are betting that DeepSeek can keep its cost advantage even as it spends more on infrastructure. That bet carries real risk. Training costs across the industry have climbed as models grow larger. DeepSeek’s efficiency edge could narrow if rivals adopt similar techniques.

Domestic Chinese funds make up a large share of the round, according to people familiar with the terms. That matters for a specific reason. It reduces DeepSeek’s exposure to foreign capital controls and export-linked investment restrictions. Those restrictions have complicated fundraising for other Chinese tech companies this year.

Some investors involved in the round declined to comment publicly, citing the sensitivity of ongoing negotiations. That reticence is typical for pre-IPO rounds of this size, where terms often shift until the final signing. Bankers advising on the deal have also stayed quiet about the exact investor list. That silence is standard practice ahead of a Shanghai listing, where regulators scrutinize pre-IPO ownership disclosures closely and expect confidentiality until filings become public.

What happens next

Investors will be watching whether the round closes at the reported terms by DeepSeek’s end-of-August target. They will also watch whether an IPO filing follows on the timeline sources have described. A successful Shanghai listing would give Chinese investors direct exposure to one of the country’s most closely watched AI companies. It would also test whether public markets value DeepSeek’s efficiency reputation as highly as private investors currently do.

Frequently Asked Questions

How much is DeepSeek raising in this funding round?

Reports put the round at roughly 50 billion yuan, about $7.4 billion, at a pre-money valuation near $74 billion.

What will DeepSeek use the money for?

The company plans to use most of the capital to build roughly 1 gigawatt of new compute capacity, according to sources familiar with the round.

Is DeepSeek planning an IPO?

Yes. Sources say DeepSeek could file for an IPO by the end of 2026, with a public debut targeted for 2027 on Shanghai’s Star Market.

How does this relate to DeepSeek's January 2026 model release?

That release showed DeepSeek could match Western AI systems at a fraction of the training cost, briefly erasing over $500 billion from AI-linked US stocks. This funding round suggests staying competitive still requires large, sustained capital investment.

Related Coverage

Sources

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