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CR CX Convos: Live from PegaWorld 2025 with Tara DeZao

CR CX Convos: Live from PegaWorld 2025 with Tara DeZao

AI isn't about replacing marketers - it's about empowering them. Constellation analyst Liz Miller sits down with Pegasystems product marketing whiz Tara DeZao to discuss marketing transformation through AI partnerships and ushering in the next wave of collaborative CustomerExperience.

Key takeaways:

📌 AI helps overcome the 'blank page' challenge
📌 Authenticity remains at the heart of great marketing
📌 Decisioning trumps data overwhelm
📌 Customer journeys need orchestration, not rigid paths

Watch the full conversation to learn more!

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Databricks launches Mosaic Agent Bricks, Lakeflow Designer, Lakehouse

Databricks launches Mosaic Agent Bricks, Lakeflow Designer, Lakehouse

Databricks launched Mosaic Agent Bricks, a workspace for creating AI agents that are production ready, accurate and cost efficient, Lakeflow Designer and Lakehouse, a transactional database.  

Agent Bricks advances the Databricks approach with Mosaic AI, which can build agent systems delivering domain-specific results, and aims to move AI agents into production. Agent Bricks will automatically generate domain-specific synthetic data and task-based benchmarks.

Databricks CEO Ali Ghodsi, said Agent Bricks is a "new way of building and deploying AI agents that can reason on your data." Databricks kicked off its Data + AI Summit in San Francisco.

Databricks said Agent Bricks will help build scalable AI agents that don't hallucinate, evaluate what's a good result and build a synthetic data set to mirror customer data. Agent Bricks are auto optimized.

With Agent Bricks, customers will be able to describe a high-level problem and Databricks will create LLM judges, generate synthetic data and auto optimize to create a grounding loop.

Databricks cited Astra Zeneca and Hawaiian Electric as early adopters of Agent Bricks that moved from point-of-concept to production in minutes from days.

By taking the guesswork out of creating production AI agents, Databricks is looking to scale agentic AI as well as drive consumption of its data platform.

Key points about Mosaic Agent Bricks:

  • Agent Bricks generates task-specific evaluations and LLM judges to assess quality.
  • Synthetic data is created that looks like customer data to supplement learning.
  • Agent Bricks uses multiple techniques to optimize agents.
  • Customers can balance quality and cost for agent results.
  • Use cases for Agent Bricks includes information extraction, knowledge supplementation, customer LLM agent and multi-agent supervision.

Databricks' announcements landed a week after Snowflake Summit 2025.

A common theme from both Databricks and Snowflake was that data platforms and AI are increasingly connected and that database technology was built for a different era. Both Databricks and Snowflake have doubled down on Postgres as a base for new AI applications. The general idea is to get data to AI applications in the lowest cost and efficient manner.

In addition, Databricks is looking to combine data and AI so enterprises can define objectives using natural language and then the platform handles the rest of the process--data prep and features; build models with fine tuning; deploy tools, retrieval models and agent sharing; evaluation (both automated and human; and governance and modeling. Databricks' big argument is that data intelligence needs to touch every application with analytics, AI and database.

Databricks also launched Mosaic AI support for serverless GPUs and MLflow 3.0, a platform for managing the AI lifecycle.

Lakeflow Designer

Separately, Databricks launched Lakeflow Designer, a no code to code first pipeline so builders have a common language.

Lakeflow Designer is backed by Lakeflow, which is now generally available and has no-code connectors that can create pipelines with a single line of SQL. Lakeflow Designer features no-code ETL with scale, access control and AI support.

Key items about Lakeflow Designer include:

  • Lakeflow Designer has a drag-and-drop UI so business analysts can build pipelines as easily as data engineers.
  • Lakeflow Designer is backed by Databricks Lakeflow, Unity Catalog and generative AI features.
  • Lakeflow Designer will be launched in private preview.

Constellation Research analyst Michael Ni said:

"This isn’t just about scale—it’s about unlocking the 90% of questions that never make it to engineering. From campaign lift tracking to territory planning, Lakeflow Designer lets business teams define and ship data products using no/low-code tools that don’t get thrown away. Lakeflow Designer is the Canva of ETL: instant, visual, AI-assisted—yet under the hood, it’s Spark SQL at machine scale. The business analyst designs, and the data engineers can review and tweak collaboratively with the analyst. The engine industrializes it with full transparency and trust."

In addition, Lakeflow and Lakeflow Designer will rhyme with Snowflake's OpenFlow. "Lakeflow and OpenFlow reflect two philosophies: Databricks integrates data engineering into a Spark-native, open orchestration fabric, while Snowflake’s OpenFlow offers declarative workflow control with deep Snowflake-native semantics. One favors flexibility and openness; the other favors consolidation and simplicity," said Ni. 

Databricks eyes transactional data with Lakebase

Databricks also announced Lakebase, a transactional database engine where data is stored in low-cost lakes for easy access to AI applications.

Lakebase is Databricks effort to address what databases need to do for AI applications. Databricks argued that Lakebase is designed for AI due to the following characteristics:

  • Lakebase has separate compute and storage, which creates very low latency, high queries per second and 99.999% uptime.
  • The Lakebase is built on open source Postgres that supports community extensions.
  • Lakebase is built for API since it launches in less than a second, gives customers the ability to pay for what they use and can manage changes well.
  • Lakebase runs on Postgres OLTP Engine and shares DNA with Neon and has fully managed pipelines for data sync.

Databricks also announced the following:

  • Lakebridge, a tooling set that's free and aimed at predictable migrations. Lakebridge features a warehouse profiler, code converter, data migration and validation with support for more than 20 legacy data warehouses.
  • Databricks Apps, which are governed data intelligence apps on Databricks, are generally available.
  • Unity Catalog Metrics, which defines metrics in one place and provides dashboards and notebooks across an enterprise. Unity Catalog Metrics also works with AI/BI Genie to promote novel questions in certified semantics. 
  • Databricks One, a version of Databricks designed for business teams that's in public preview. Databricks One has an intuitive customer experience, simple administration and Unity Catalog.
  • Community Edition: Databricks Community Edition was updated and includes most features. Developers can learn and experiment with data and AI use cases and Databricks is spending $100 million on programs for education.

Ni added:

"We’re entering a new era where data clouds and hyperscalers are racing to establish themselves as the dominant platform for AI-driven decision-making in their respective markets. The competition is no longer about warehouse performance—it’s about who owns the semantic layer, who governs the agent lifecycle, and who enables the next-gen data app ecosystem. With Lakebase, Agent Bricks, and Unity Catalog metrics, Databricks is asserting that ownership more broadly than ever before."

Data to Decisions databricks Big Data ML Machine Learning LLMs Agentic AI Generative AI AI Analytics Automation business Marketing SaaS PaaS IaaS Digital Transformation Disruptive Technology Enterprise IT Enterprise Acceleration Enterprise Software Next Gen Apps IoT Blockchain CRM ERP finance Healthcare Customer Service Content Management Collaboration Chief Information Officer Chief Technology Officer Chief Information Security Officer Chief Data Officer

Nvidia adds AWS, Microsoft Azure to DGX Cloud Lepton marketplace

Nvidia adds AWS, Microsoft Azure to DGX Cloud Lepton marketplace

Nvidia expands its DGX Cloud Lepton GPU marketplace with the addition of AWS and Microsoft Azure to its roster of providers.

DGX Cloud Lepton is a marketplace that unified Nvidia GPU resources across providers and regions. The marketplace is also integrated with Nvidia's AI stack for microservice containers, multiple large language models and management.

At Nvidia GTC Paris, the company said its global compute marketplace, launched at Computex, is adding a bevy of EU providers including Mistral AI, Nebius, Nscale, Firebird, Fluidstack, Hydra Host, Scaleway and Together AI. Nvidia CEO Jensen Huang said DGX Cloud Lepton is "connects developers to GPU compute powering a virtual global AI factory."

AWS and Microsoft Azure will be the first large-scale providers contributing Nvidia Blackwell and GPUs to DGX Cloud Lepton. CoreWeave, Crusoe, Firmus, Foxconn, GMI Cloud, Lambda and Yotta Data Services are already participating in DGX Cloud Lepton.

In addition, Nvidia said that Hugging Face will roll out Training Cluster as a Service integrating with DGX Cloud Lepton so researchers and developers can tap into GPU compute. Mirror Physics, Project Numina and the Telethon Institute of Genetics and Medicine will be among the first Hugging Face customers to access Training Cluster as a Service, which uses DGX Cloud Lepton for compute.

Nvidia also said it is working with European venture capitals firms Accel, Elaia, Partech and Sofinnova Partners to provide up to $100,000 in DGX Cloud Lepton credits to startups.

Enterprises with early access to DGX Cloud Lepton include Basecamp Research, EY, Outerbounds, Prime Mente and Reflection.

Also see:

Data to Decisions Tech Optimization nvidia Chief Information Officer

Nvidia outlines EU AI expansion, ecosystem, sovereign models

Nvidia outlines EU AI expansion, ecosystem, sovereign models

Nvidia outlined plans to scale AI factories and research hubs in Europe, expanded partnerships with Schneider Electric and Siemens and expanded model choices for sovereign AI and NIM Microservices.

Those high-level headlines from Nvidia GTC Paris were part of a broader stream of updates for the AI market in Europe, where Nvidia has more than 1.5 million developers. Nvidia also announced that European enterprises are adopting agentic AI including Novo Nordisk, Siemens, Shell, BT Group, SAP, Nestle, L'Oreal and BNP Paribas. The company also touted adoption of its Nvidia Drive autonomous vehicle platform at Volvo, Mercedes Benz and Jaguar as well as quantum computing efforts in the region.

Nvidia Jensen Huang said during the GTC Paris keynote:

"Europe has now awakened to the importance of these AI factories, and the importance of the AI infrastructure.  I'm so delighted to see so much activity here. This is just the beginning."

Dion Harris, Senior Director of HPC and AI Factory Solutions at Nvidia, said:

"We're deeply integrated with upskilling and education, working with all of the top higher education and research institutions and the global systems integrators, Europe is poised to be a powerhouse in this new industrial revolution. The only thing is missing is infrastructure. Today, every nation needs to build AI infrastructure, and every company needs to build an AI factory."

Here's a breakdown of what Nvidia announced:

  • Schneider Electric and Nvidia expanded a partnership designed to accelerate the deployment of AI factories. The two companies will collaborate on reference designs, simulation, design and layout, infrastructure and architecture for AI factories. The two companies will also look to scale production of cooling systems in Europe and 800 volt direct current architectures.
  • A roster of European supercomputing centers and cloud service providers building Nvidia-based AI infrastructure.
  • European Nvidia AI Technology Centers in Finland, Sweden, Germany, UK, France, Italy and Spain. Nvidia is working with Italy to advance its sovereign AI efforts.
  • DGX Cloud Lepton integration with Hugging Face Training Cluster as a Service. Nvidia also said it is working with EMEA model builders and offering sovereign AI models via Perplexity Pro. According to Nvidia, each country in EMEA needs strong models that reflect each nation's unique language and culture and operates in region.
  • Nvidia is working with Mistral AI to build a cloud platform powered by 18,000 Grace Blackwell systems.
  • As part of that expanded model selection, Nvidia said NIM Microservices will have access to more than 100,000 open and custom models via Hugging Face public and private LLMs.
  • NeMo AI agent additions including AI Safety Blueprint, Data Flywheel and Agentic AI Toolkit. Nvidia added that NIM and NeMo will be integrated into SAP Business AI.
  • Siemens and Nvidia will expand their partnership to accelerate AI capabilities in manufacturing with a focus on product design and engineering, production optimization, operational planning, digital twins and industrial edge computing. Nvidia's various libraries for CUDA X, RTX and Omniverse will be integrated into Siemens product portfolio.

  • Nvidia also announced how its GB200 NVL72 system is powering quantum computing workloads and simulations with European enterprises and research hubs.

Also see:

Data to Decisions Future of Work Tech Optimization Innovation & Product-led Growth Next-Generation Customer Experience Digital Safety, Privacy & Cybersecurity nvidia ML Machine Learning LLMs Agentic AI Generative AI Robotics AI Analytics Automation Quantum Computing Cloud Digital Transformation Disruptive Technology Enterprise IT Enterprise Acceleration Enterprise Software Next Gen Apps IoT Blockchain Leadership VR Chief Information Officer Chief Executive Officer Chief Technology Officer Chief AI Officer Chief Data Officer Chief Analytics Officer Chief Information Security Officer Chief Product Officer

OpenAI: New models, and chasing Altman’s superintelligence dream

OpenAI: New models, and chasing Altman’s superintelligence dream

OpenAI released o3-pro for ChatGPT Pro and Team users in what it calls its most capable model yet as it cut the prices for o3 by 80%. The moves come as OpenAI CEO Sam Altman ponders 2030 where there limitations of energy will lead to AI superintelligence that's almost free.

That's a mouthful, but Altman and OpenAI are arguing that we're at an event horizon, an inflection point and probably a lot of revenue growth. For enterprises, the takeaway is that OpenAI expenses may be declining in exchange for volume. OpenAI's enterprise business surging, says Altman

Let's recap the headlines:

  • OpenAI dropped o3 pricing by 80%. For developers, o3 may not be the latest and greatest, but it'll be good enough for many use cases.
  • OpenAI is scaling as it breaks away from its Microsoft partnership. Reuters reported that OpenAI is going to use Google Cloud for compute. That move would give OpenAI a multi-cloud approach that should meet its needs better than an exclusive with Microsoft Azure.
  • OpenAI launched o3-pro. The company said: "In expert evaluations, reviewers consistently prefer o3-pro over o3 in every tested category and especially in key domains like science, education, programming, business, and writing help. Reviewers also rated o3-pro consistently higher for clarity, comprehensiveness, instruction-following, and accuracy."

That barrage of headlines, however, are overshadowed by Altman's blog, which laid out his latest thoughts on AI superintelligence, energy consumption and how much a ChatGPT consumes in resources per query today.

The post is worth a read. Here are a few takeaways.

Energy will be plentiful. Altman: "In the 2030s, intelligence and energy—ideas, and the ability to make ideas happen—are going to become wildly abundant. These two have been the fundamental limiters on human progress for a long time; with abundant intelligence and energy (and good governance), we can theoretically have anything else."

But that ChatGPT usage isn't killing the environment today. Altman: "As datacenter production gets automated, the cost of intelligence should eventually converge to near the cost of electricity. (People are often curious about how much energy a ChatGPT query uses; the average query uses about 0.34 watt-hours, about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes. It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.)"

The self-reinforcing loops have already started and what was novel months ago is now routine. Altman: The economic value creation has started a flywheel of compounding infrastructure buildout to run these increasingly-powerful AI systems. And robots that can build other robots (and in some sense, datacenters that can build other datacenters) aren’t that far off."

Humans will adapt: Altman: "The rate of technological progress will keep accelerating, and it will continue to be the case that people are capable of adapting to almost anything. There will be very hard parts like whole classes of jobs going away, but on the other hand the world will be getting so much richer so quickly that we’ll be able to seriously entertain new policy ideas we never could before. We probably won’t adopt a new social contract all at once, but when we look back in a few decades, the gradual changes will have amounted to something big."

Altman does note the challenges. He said society will have to solve "the alignment problem" where we can guarantee "AI systems learn and act towards what we collectively want." Altman said society will also have to make sure superintelligence is cheap and not concentrated with any one person. The world needs to start a conversation about what the boundaries are and get aligned.

My take

  1. Altman's take that cost and scale will be solved is believable, but we can debate the timeline for sure. Can energy grids be revamped in 5 years?
  2. The concept that society is going to have a reasonable discussion about superintelligence and get alignment on what we call collectively want from AI is naive if not batshit crazy. Governments on a global basis barely function now and there's a shortage of consensus.
  3. Societal impacts are glossed over throughout the post. Altman's take that humans will adapt may apply to a sliver of the population.
  4. This quote made me chuckle: "In the most important ways, the 2030s may not be wildly different. People will still love their families, express their creativity, play games, and swim in lakes."
  5. This quote struck me as blasé: "We will figure out new things to do and new things to want, and assimilate new tools quickly (job change after the industrial revolution is a good recent example). Expectations will go up, but capabilities will go up equally quickly, and we’ll all get better stuff. We will build ever-more-wonderful things for each other. People have a long-term important and curious advantage over AI: we are hard-wired to care about other people and what they think and do, and we don’t care very much about machines."
  6. Either way, Altman's right that potentially wonderful and wrenching change is coming. Both can be true.
Data to Decisions Future of Work Innovation & Product-led Growth Chief Information Officer

CR CX Convos: Live from PegaWorld 2025 with Matt Nolan

CR CX Convos: Live from PegaWorld 2025 with Matt Nolan

Don't miss the latest CR CX Convo covering the future of marketing decisioning...

Tuning in LIVE from #PegaWorld2025, Constellation analyst Liz Miller and Pegasystems' Matthew Nolan discuss the evolution of #marketing beyond traditional campaigns. 

Key takeaways:

📌 Marketing is shifting from sales-driven to customer-outcome focused
📌 AI and decisioning are transforming how brands engage customers
📌 The goal: Create relevant, personalized experiences that truly matter

Marketers aren't just sending campaigns anymore - they're becoming strategic growth architects who leverage data and AI to drive meaningful connections.

Watch the full conversation!

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Cisco tunes network portfolio, gear for AI agents, introduces AgenticOps

Cisco tunes network portfolio, gear for AI agents, introduces AgenticOps

Cisco outlined a series of AI infrastructure, security products and software designed to support AI agents, hyperscale data centers and enterprises for various workloads.

The upshot from Cisco Live in San Diego is that the networking giant is reordering its stack for AI workloads.

Cisco outlined AgenticOps. The company said AgenticOps is its AI-driven approach to running operations including telemetry, automaton and domain knowledge. Cisco AgenticOps is powered by Deep Network Model, a network-focused LLM, and Cisco AI Assistant, which identifies issues, root causes and automates workflows.

The company also launched AI Canvas, an interforce for customer dashboards enables collaboration between network, security and dev operations to collaborate and optimized.

Cisco delivers strong Q3 amid AI infrastructure, security traction

Jeetu Patel, President and Chief Product Officer, Cisco, said: "As billions of AI agents begin working on our behalf, the demand for high-bandwidth, low latency and power efficient networking for data centers will soar."

Here's a look at what Cisco announced at Cisco Live:

  • Unified management of Cisco platforms including ACI, NX-OS and other systems with dashboards, policies and controls. Cisco launched the Unified Nexus Dashboard, which consolidates services across all services.
  • Cisco Intelligent Packet Flow, which steers traffic using real-time telemetry and congestion data across AI networks. The service has visibility across networks, GPUs and distributed AI jobs.
  • Cisco and Nvidia are unifying architectures and outlined their first technical integration of Cisco G200-based switches and Nvidia NICs. The companies also demonstrated Nvidia Spectrum-X Ethernet networking based on Cisco Silicon One.
  • The company expanded AI PODs to support Nvidia's release cadence. Nvidia RTX Pro 6000 Blackwell Server Edition GPU is available to order with Cisco's UCS C845A M8 servers. Cisco and Nvidia will work together on validated systems for the Cisco Secure AI Factory with Nvidia.
  • Cisco AI Defense and Cisco Hypershield are now included in the Nvidia Enterprise AI Factory validated design.
  • Cisco AI Defense can secure AI agents with open models and optimized with Nvidia NIM and NeMo microservices.
  • The company has embedded its security offerings into its networking gear. Cisco has embedded zero trust and observability into the network, added a new generation of firewalls (6100 Series, 200 Series) and tightened integration with its Splunk unit.
  • Cisco is bringing together Meraki and Catalyst into one unified management platform for next-gen wireless, switching, routing and industrial networks across all platforms.
  • ThousandEyes and Splunk are now integrated for network to application visibility.
  • Cisco launched new Cisco C9350 and C9610 Smart Switches for campus networks and 8100, 8200, 8300, 8400 and 8500 Secure Routers, which has integration with Cisco's security portfolio.
  • The company launched Cisco Wireless 9179F Series Access Points for campus networks.
  • Cisco rolled out a series of rugged switches for industrial AI use cases.

 

Data to Decisions Digital Safety, Privacy & Cybersecurity Future of Work Tech Optimization cisco systems Chief Information Officer

IBM outlines quantum computing roadmap through 2029, fault-tolerant systems

IBM outlines quantum computing roadmap through 2029, fault-tolerant systems

IBM updated its quantum computing roadmap heading into IBM Quantum Starling, a large-scale fault-tolerant quantum system in 2029.

Big Blue said IBM Quantum Starling will be delivered by 2029 and installed at the IBM Quantum Data Center in Poughkeepsie, New York. That system is expected to perform 20,000 times ore operations than today's quantum computers.

For IBM, Quantum Starling will be the headliner of a fleet of quantum computing systems. IBM CEO Arvind Krishna said the company is leaning into its R&D to scale out quantum computing for multiple use cases including drug development, materials discovery, chemistry, and optimization. IBM also recently outlined flexible pricing models for quantum computing to expand usage and upgraded its Quantum Data Center to its latest Heron quantum processor.

The news lands as quantum computing players outline plans to scale organically or via acquisition. IonQ just announced its plans through 2030 and quantum computing vendors have been laying out plans throughout 2025.

IBM said Starling will be able to run 100 million quantum operations using 200 logical qubits. A logical qubit is a unit of an error-corrected quantum computer tasked with storing one qubit’s worth of quantum information. Quantum computers need to be error corrected to run large workloads without fault.

Starling will also be a foundation system for IBM Quantum Blue Jay, which will be able to run 1 billion quantum operations over 2,000 logical qubits.

To get to fault tolerant scale, IBM is building an architecture that is fault tolerant, able to prepare and measure logical qubits, apply universal instructions and decode measurements from logical qubits in real time. This architecture, which was outlined in two research papers, also has to be modular and energy efficient.

Here's how IBM is going to get to Starling and beyond:

  • 2025: IBM Quantum Loon will launch to test architecture components for quantum low-density parity check (qLDPC) codes, which reduce the number of physical qubits needed for error correction and cuts overhead by about 90%.
  • 2026: IBM Quantum Kookaburra will feature a modular processor to store and process encoded information and combine quantum memory and logic operations.
  • 2027: IBM Quantum Cockatoo, will feature two Kookaburra modules that will link quantum chips together like nodes in a larger system.

Holger Mueller, an analyst at Constellation Research, said:

"Sometime in the last 6 months quantum vendors realized that they will not be able to produce enough qubits for real world use cases and are focusing on error correction. What is unique with IBM is that it's modular approach has led to the realization that there are challenges to overcome when putting multiple quantum computers together, hence a roadmap change and a focus on qLDPC based couplers, with 'Loon', coming this year. Next year will then be the showcase and proof point that all of this works with IBM Quantum Kookaburra. Kudos go to IBM for laying out its roadmap further, all the way to its Starling system, allowing CxOs to align their quantum uptake plans."

More IBM Quantum:

 

Data to Decisions Tech Optimization Innovation & Product-led Growth IBM Quantum Computing Chief Information Officer

IonQ acquires Oxford Ionics for $1.07 billion, gets quantum-on-a-chip technology

IonQ acquires Oxford Ionics for $1.07 billion, gets quantum-on-a-chip technology

IonQ said it will acquire UK's Oxford Ionics in a deal valued at $1.075 billion in mostly stock and $10 million in cash. The deal is designed to accelerate IonQ's quantum computing roadmap and establish a global hub for research and development.

The purchase is IonQ's largest to date. Niccolo de Masi, IonQ CEO, said in a statement that the Oxford Ionics purchase will " set a new standard within quantum computing and deliver superior value for our customers through market-leading enterprise applications."

According to IonQ, the Oxford Ionics will bring complementary technology to the company. IonQ focuses on trapped ion systems and Oxford Ionics holds world records in fidelity, which measures the accuracy of quantum applications. The game plan for the combined company is to provide an integrated quantum computing stack that features IonQ quantum computing, applications and networking with Oxford Ionics ion-trap technology, which is manufactured on standard semiconductors.

Oxford Ionics ability to bring ion-trap-on-a-chip to IonQ and enable the combined company to "accelerate IonQ’s commercial quantum computer miniaturization and global delivery." Oxford Ionics founders, Dr. Chris Ballance and Dr. Tom Harty, are expected to remain with IonQ after the acquisition is completed.

In an SEC filing, IonQ said it will issue up to about 35 million new shares to pay for the deal. The company said:

"The number of shares of Common Stock to be issued will not be less than 21,143,538 or more than 35,241,561. The final number of shares of Common Stock to be issued as Transaction Consideration will be calculated using the volume-weighted average price for shares of Common Stock for the 20 trading days immediately preceding, but not including, the third business day prior to the date of the Closing, but will not be more than $50.37 per share or less than $30.22 per share."

Ballance said Oxford Ionics' quantum chip can be manufactured in standard semiconductor fabs. "We look forward to integrating this innovative technology to help accelerate IonQ’s quantum computing roadmap for customers in Europe and worldwide," said Ballance.

IonQ has been on an acquisition spree of late with an emphasis on quantum networking. The acquisition of Oxford Ionic is designed to bring scale to compute and use cases in materials science, drug discovery, logistics, financial modeling and defense.

Here's a look at IonQ's acquisitions.

IonQ has also been expanding its global footprint and Oxford Ionics will be a beachhead in the UK.

Oxford Ionics outlined its roadmap last month with a plan that features enterprise grade quantum computing by 2027. IonQ's roadmap features a similar timeline and will bring an established customer base and sales team to better commercialize Oxford Ionics.

In a statement, IonQ said the combined company plans to build systems with 256 physical qubits with 99.99% accuracy by 2026 and scale to 10,000 physical qubits with logical accuracy of 99.99999% by 2027. Ultimately, IonQ wants to hit 2 million physical qubits in quantum computing by 2030.

IonQ and Oxford Ionics held a technology overview call featuring Ballance and Dr. Mihir Bhaskar, CEO of Lightsynq, which was acquired. De Masi touted a recent use case with Astra Zenica, IonQ, Nvidia and AWS and added that Oxford Ionics will accelerate commercial usage.

"IonQ, Lightsynq and Oxford Ionics will create the winning quantum computer in each year and every era of quantum computing," said de Masi.

Dean Kassmann, SVP of engineering and technology at IonQ, said the company's latest acquisitions will "represent a significant acceleration of our planned development work to realize our vision to build the world's best quantum computers to solve the world's most impactful and complex problems."

Kassmann also outlined IonQ's roadmap with Oxford Ionics in the fold.

Ballance said Oxford Ionics approach to leveraging existing technologies to go along with quantum computing will be scalable. Ballance said:

"We have a clear path to apply this to systems with 10s of 1000s of qubits in a single chip, and we've been to be working on our 256 qubit quantum processor units. Our technology allows us to scale devices to millions of qubits by building bigger and bigger chips. But what's more, these chips can be networked by photonic interconnects to allow for distributed compute."

Constellation Research analyst Holger Mueller said:

"The road to commercial quantum uses cases goes this way: (a) more qubits, (b) better error correction or (c) a combo of both. For a decade the industry was squarely rooted in more qubits. More recently it's about error correction, which means that vendors think they have sufficient qubits. IonQ is the perfect example of saying it's a combo of both to enable more sophisticated quantum use cases."

Data to Decisions Tech Optimization Innovation & Product-led Growth Quantum Computing Chief Information Officer

Apple's WWDC 2025: Apple Intelligence leaves a void as execs go redesign happy

Apple's WWDC 2025: Apple Intelligence leaves a void as execs go redesign happy

Apple executives acknowledged at the company's Worldwide Developer Conference (WWDC) that Apple needs more time to make Apple Intelligence work well. In the meantime, Apple executives outlined Liquid Glass, a redesign that'll flow through Apple devices.

The company also announced new naming conventions for iOS, watchOS, tvOS, macOS, visionOS and iPadOS.

If anything, Apple's developer keynote highlighted how Apple Intelligence, outlined in 2024 with great fanfare, has fallen short of expectations. Craig Federighi, SVP of Software Engineering at Apple, noted how Apple Intelligence did ship features including email and notification summarization, notes, smart replies and ways to clean up video and photos.

Federighi said:

"We delivered this while taking an extraordinary step forward for privacy and AI with private cloud compute, which extends the privacy of your iPhone into the cloud so no one else can access your data, not even Apple. We also introduced enhancements that make Siri more natural and more helpful, and as we've shared, we're continuing our work to deliver the features that make Siri even more personal. This work needed more time to reach our high quality bar, and we look forward to sharing more about it in the coming year."

He said that Apple Intelligence will get more languages, but the approach is incremental for now. "We're making the generative models that power Apple intelligence more capable and more efficient, and we're continuing to tap into Apple intelligence in more places across our ecosystem. Throughout today's presentation, you'll see new Apple intelligence features that elevate your experiences across iPhone, Apple, watch Apple vision pro Mac and iPad. Plus, this year, we're doing something new, and we think it's going to be pretty big. We're opening up access for any app to tap directly into the on device," said Federighi.

As for LLMs, Apple Intelligence will have a new foundation model framework that gives developers direct access with privacy and offline access built in.

"We think this will ignite a whole new wave of intelligent experiences in the apps you use every day. For example, if you're getting ready for an exam, an app like Kahoot can create a personalized quiz from your notes to make studying more engaging," said Federighi. "And because it uses on device models this happens without cloud API costs. We couldn't be more excited about how developers can build on Apple intelligence to bring you new experiences that are smart, available when you're offline, and that protect your privacy."

With that Apple Intelligence tease--that may not be reality until 2026--Apple moved on to other key items including a redesign for iOS that already has Windows Vista trending due to the resemblance

A few thoughts:

  • Apple Intelligence appears to be banking on local LLMs but given the rate of innovation that could be a mistake. Developers will need to figure out how much they can differentiate with Apple’s on-device model.
  • Key details about the Apple Intelligence's approach for developers is lacking.
  • Apple is clearly betting on privacy as a pitch for AI, but it's unclear whether focusing on its edge devices will keep pace with OpenAI, Google, Anthropic and Microsoft to name a few.
  • Apple does appear to be integrating OpenAI's ChatGPT across its applications. For instance, Apple said ChatGPT image generation is now available in Image Playground.
  • It's quite possible that Apple will have to buy its way out of this AI pickle, but historically the company hasn't made big acquisitions. The valuations are stretched for foundational model players.
  • The risk here is that Apple devices are merely a vessel for other companies' AI no matter how pretty the operating systems become.

Other news items:

  • Apple's WWDC keynote was devoted to the redesign and the changes across devices looked strong. However, much of what Apple is proposing is in the latest Android today. See: Apple release on redesign.
  • Developers are getting new APIs for location, enhancements to notifications in Apple Watch, App Intents, and other goodies.
  • Visual Intelligence will pull up context across iPhone apps to give you more information, rating and other data.

 

Data to Decisions Future of Work Next-Generation Customer Experience apple Chief Information Officer