Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle. #execution #gap #why #projects #dont

When Russian troops invaded Ukraine on 24 February 2022, Russia’s datacentre sector was one of the fastest-growing segments of the country’s IT industry, with annual growth rates in the region of 10-12%. However, with the conflict resulting in the imposition of Western sanctions against Russia and an outflow of US-based tech companies from the country, including Apple and Microsoft, optimism about the sector’s potential for further growth soon disappeared. In early March 2025, it was reported that Google had disconnected from traffic exchange points and datacentres in Russia, leading to concerns about how this could negatively affect the speed of access to some Google services for Russian users. Initially, there was hope that domestic technology and datacentre providers might be able to plug the gaps left by the exodus of the US tech giants, but it seems they could not keep up with the hosting demands of Russia’s increasingly digital economy. Oleg Kim, director of the hardware systems department at Russian IT company Axoft, says the departure of foreign cloud providers and equipment manufacturers has led to a serious shortage of compute capacity in Russia. This is because the situation resulted in a sharp, initial increase in demand for domestic datacentres, but Russian providers simply did not have time to expand their capacities on the required scale, continues Kim. According to the estimates of Key Point, one of Russia’s largest datacentre networks, meeting Russia’s demand for datacentres will require facilities with a total capacity of 30,000 racks to be built each year over the next five years. On top of this, it has also become more costly to build datacentres in Russia. Estimates suggest that prior to 2022, the cost of a datacentre rack totalled 100,000 rubles, but now exceeds 150,000 rubles. And analysts at Forbes Russia expect these figures will continue to grow, due to rising logistics costs and the impact the war is having on the availability of skilled labour in the construction sector. The impact of these challenges is being keenly felt by users, with several of the country’s large banks experiencing serious problems when finding suitable locations for their datacentres. Sberbank is among the firms affected, with its chairperson, German Gref, speaking out previously about how the bank is in need of a datacentre with at least 200MW of capacity, but would ideally need 300-400MW to address its compute requirements. Stanislav Bliznyuk, chairperson of T-Bank, says trying to build even two 50MW datacentres to meet its needs is proving problematic. “Finding locations where such capacity and adequate tariffs are available is a difficult task,” he said. about datacentre developments North Lincolnshire Council has received a planning permission application for another large-scale datacentre development, in support of its bid to become an AI Growth Zone A proposal to build one of the biggest datacentres in Europe has been submitted to Hertsmere Borough Council, and already has the support of the technology secretary and local councillors. The UK government has unveiled its 50-point AI action plan, which commits to building sovereign artificial intelligence capabilities and accelerating AI datacentre developments – but questions remain about the viability of the plans. Despite this, T-Bank is establishing its own network of data processing centres – the first of which should open in early 2027, he confirmed in November 2024. Kirill Solyev, head of the engineering infrastructure department of the Softline Group of Companies, who specialise in IT, says many large Russian companies are resorting to building their own datacentres – because compute capacity is in such short supply. The situation is, however, complicated by the lack of suitable locations for datacentres in the largest cities of Russia – Moscow and St Petersburg. “For example, to build a datacentre with a capacity of 60MW, finding a suitable site can take up to three years,” says Solyev. “In Moscow, according to preliminary estimates, there are about 50MW of free capacity left, which is equivalent to 2-4 large commercial datacentres. “The capacity deficit only in the southern part of the Moscow region is predicted at 564MW by 2030, and up to 3.15GW by 2042.” As a result, datacentre operators and investors are now looking for suitable locations outside of Moscow and St Petersburg, and seeking to co-locate new datacentres in close proximity to renewable energy sources. And this will be important as demand for datacentre capacity in Russia is expected to increase, as it is in most of the rest of the world, due to the growing use of artificial intelligencetools and services. The energy-intensive nature of AI workloads will put further pressure on operators that are already struggling to meet the compute capacity demands of their customers. Speaking at the recent Ural Forum on cyber security in finance, Alexander Kraynov, director of AI technology development at Yandex, says solving the energy consumption issue of AI datacentres will not be easy. “The world is running out of electricity, including for AI, while the same situation is observed in Russia,” he said. “In order to ensure a stable energy supply of a newly built large datacentre, we will need up to one year.” According to a recent report of the Russian Vedomosti business paper, as of April 2024, Russian datacentres have used about 2.6GW, which is equivalent to about 1% of the installed capacity of the Unified Energy System of Russia. Accommodating AI workloads will also mean operators will need to purchase additional equipment, including expensive accelerators based on graphic processing units and higher-performing data storage systems. The implementation of these plans and the viability of these purchases is likely to be seriously complicated by the current sanctions regime against Russia. That said, Russia’s prime minister, Mikhail Mishustin, claims this part of the datacentre supply equation is being partially solved by an uptick in the domestic production of datacentre kit. According to the Mishustin, more than half of the server equipment and industrial storage and information processing systems needed for datacentres are already being produced in Russia – and these figures will continue to grow. The government also plans to provide additional financial support to the industry, as – to date – building datacentres in Russia has been prevented by relatively long payback periods, of up to 10 years in some cases, of such projects. One of the possible support measures on offer could include the subsidisation of at least part of the interest rates on loans to datacentre developers and operators. At the same time, though, the government’s actions in other areas have made it harder for operators to build new facilities. For example, in March 2025, the Russian government significantly tightened the existing norms for the establishment of new datacentres in the form of new rules for the design of data processing centres, which came into force after the approval by the Russian Ministry of Construction. According to Nikita Tsaplin, CEO of Russian hosting provider RUVDS, the rules led to additional bureaucracy in the sector. And, according to his predictions, that situation can extend the construction cycle of a datacentre from around five years to seven years. The government’s intervention here was to prevent the installation of servers in residential areas, such as garages, but it looks set to complicate an already complex situation – prompting questions about whether Russia’s datacentre market will ever reach its full potential. #conflict #putting #russias #datacentre #market

Business leaders are weighing in on the Elon Musk and Donald Trump breakup. Kevin Dietsch/Getty Images 2025-06-06T05:49:58Z d Read in app This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now. Have an account? The friendship between Elon Musk and Donald Trump publicly unravelled on Thursday. It all started when Musk criticized Trump's "Big Beautiful Bill." Here's what business leaders like Mark Cuban and Bill Ackman have to say about the breakup. Amid a dramatic falling out between Donald Trump and his "first buddy," Elon Musk, some of the business world's most influential voices are weighing in.The relationship between the president and his once-close ally imploded on Thursday as they clashed publicly over Trump's "Big Beautiful Bill."Musk, who stepped down from his role at DOGE in May, took to X to criticize the bill, calling it the "Debt Slavery Bill" and the "Big Ugly Spending Bill."In response, Trump fired back at Musk during a White House event. He also defended the bill on Truth Social, while threatening to cancel Musk's government contracts.Musk saw his net worth fall by billion on Thursday, per the Bloomberg Billionaires Index. Tesla shares were also down by more than 14%.Here's what several business leaders have to say about the row.Mark Cuban Mark Cuban appeared to support Elon Musk's suggestion to start a new political party. Richard Rodriguez/Getty Images Amid his feud with Trump, Musk proposed creating a new political party for "the middle" in a poll on X.Mark Cuban appeared to endorse the idea, quoting Musk's post and replying with three check marks. The former "Shark Tank" star previously said he's "not a fan of either party," but would run as a Republican if he wanted to join politics.Bill Ackman Bill Ackman called on Musk and Trump to reconcile. Brian Snyder/Reuters Hedge fund billionaire Bill Ackman voiced his support for both Trump and Musk on X, calling on the two to put aside their differences and "make peace for the benefit of our country."Ackman, who had endorsed Trump for his 2024 presidential bid, wrote: "We are much stronger together than apart." "You're not wrong," Musk responded.Paul Graham Paul Graham also took to X to share his thoughts on the feud. Joe Corrigan/Getty Images for AOL Paul Graham, cofounder of the startup accelerator Y Combinator, also weighed in on the public feud between the president and the Tesla CEO. "A lot of people seem to be treating this as if it were just a beef. But the underlying allegation is a very serious one. If it's true, Trump is surely going to have to resign," he wrote in a post on X.Graham did not specify what allegation he was referring to.Hours before Graham made his post, Musk went on X and accused Trump of withholding information about Jeffrey Epstein."Time to drop the really big bomb: @realDonaldTrump is in the Epstein files. That is the real reason they have not been made public. Have a nice day, DJT!" Musk wrote on X.Graham told Musk in February that he should work with the government "carefully" because it's not "just a company."A representative for Graham did not immediately respond to a request for comment from Business Insider. #here039s #how #big #business #leaders

When people talk about UX, it’s usually about the things they can see and interact with, like wireframes and prototypes, smart interactions, and design tools like Figma, Miro, or Maze. Some of the outputs are even glamorized, like design systems, research reports, and pixel-perfect UI designs. But here’s the truth I’ve seen again and again in over two decades of working in UX: none of that moves the needle if there is no collaboration. Great UX doesn’t happen in isolation. It happens through conversations with engineers, product managers, customer-facing teams, and the customer support teams who manage support tickets. Amazing UX ideas come alive in messy Miro sessions, cross-functional workshops, and those online chatswhere people align, adapt, and co-create. Some of the most impactful moments in my career weren’t when I was “designing” in the traditional sense. They have been gaining incredible insights when discussing problems with teammates who have varied experiences, brainstorming, and coming up with ideas that I never could have come up with on my own. As I always say, ten minds in a room will come up with ten times as many ideas as one mind. Often, many ideas are the most useful outcome. There have been times when a team has helped to reframe a problem in a workshop, taken vague and conflicting feedback, and clarified a path forward, or I’ve sat with a sales rep and heard the same user complaint show up in multiple conversations. This is when design becomes a team sport, and when your ability to capture the outcomes multiplies the UX impact. Why This Article Matters Now The reason collaboration feels so urgent now is that the way we work since COVID has changed, according to a study published by the US Department of Labor. Teams are more cross-functional, often remote, and increasingly complex. Silos are easier to fall into, due to distance or lack of face-to-face contact, and yet alignment has never been more important. We can’t afford to see collaboration as a “nice to have” anymore. It’s a core skill, especially in UX, where our work touches so many parts of an organisation. Let’s break down what collaboration in UX really means, and why it deserves way more attention than it gets. What Is Collaboration In UX, Really? Let’s start by clearing up a misconception. Collaboration is not the same as cooperation. Cooperation: “You do your thing, I’ll do mine, and we’ll check in later.” Collaboration: “Let’s figure this out together and co-own the outcome.” Collaboration, as defined in the book Communication Concepts, published by Deakin University, involves working with others to produce outputs and/or achieve shared goals. The outcome of collaboration is typically a tangible product or a measurable achievement, such as solving a problem or making a decision. Here’s an example from a recent project: Recently, I worked on a fraud alert platform for a fintech business. It was a six-month project, and we had zero access to users, as the product had not yet hit the market. Also, the users were highly specialised in the B2B finance space and were difficult to find. Additionally, the team members I needed to collaborate with were based in Malaysia and Melbourne, while I am located in Sydney. Instead of treating that as a dead end, we turned inward: collaborating with subject matter experts, professional services consultants, compliance specialists, and customer support team members who had deep knowledge of fraud patterns and customer pain points. Through bi-weekly workshops using a Miro board, iterative feedback loops, and sketching sessions, we worked on design solution options. I even asked them to present their own design version as part of the process. After months of iterating on the fraud investigation platform through these collaboration sessions, I ended up with two different design frameworks for the investigator’s dashboard. Instead of just presenting the “best one” and hoping for buy-in, I ran a voting exercise with PMs, engineers, SMEs, and customer support. Everyone had a voice. The winning design was created and validated with the input of the team, resulting in an outcome that solved many problems for the end user and was owned by the entire team. That’s collaboration! It is definitely one of the most satisfying projects of my career. On the other hand, I recently caught up with an old colleague who now serves as a product owner. Her story was a cautionary tale: the design team had gone ahead with a major redesign of an app without looping her in until late in the game. Not surprisingly, the new design missed several key product constraints and business goals. It had to be scrapped and redone, with her now at the table. That experience reinforced what we all know deep down: your best work rarely happens in isolation. As illustrated in my experience, true collaboration can span many roles. It’s not just between designers and PMs. It can also include QA testers who identify real-world issues, content strategists who ensure our language is clear and inclusive, sales representatives who interact with customers on a daily basis, marketers who understand the brand’s voice, and, of course, customer support agents who are often the first to hear when something goes wrong. The best outcomes arrive when we’re open to different perspectives and inputs. Why Collaboration Is So Overlooked? If collaboration is so powerful, why don’t we talk about it more? In my experience, one reason is the myth of the “lone UX hero”. Many of us entered the field inspired by stories of design geniuses revolutionising products on their own. Our portfolios often reflect that as well. We showcase our solo work, our processes, and our wins. Job descriptions often reinforce the idea of the solo UX designer, listing tool proficiency and deliverables more than soft skills and team dynamics. And then there’s the team culture within many organisations of “just get the work done”, which often leads to fewer meetings and tighter deadlines. As a result, a sense of collaboration is inefficient and wasted. I have also experienced working with some designers where perfectionism and territoriality creep in — “This is my design” — which kills the open, communal spirit that collaboration needs. When Collaboration Is The User Research In an ideal world, we’d always have direct access to users. But let’s be real. Sometimes that just doesn’t happen. Whether it’s due to budget constraints, time limitations, or layers of bureaucracy, talking to end users isn’t always possible. That’s where collaboration with team members becomes even more crucial. The next best thing to talking to users? Talking to the people who talk to users. Sales teams, customer success reps, tech support, and field engineers. They’re all user researchers in disguise! On another B2C project, the end users were having trouble completing the key task. My role was to redesign the onboarding experience for an online identity capture tool for end users. I was unable to schedule interviews with end users due to budget and time constraints, so I turned to the sales and tech support teams. I conducted multiple mini-workshops to identify the most common onboarding issues they had heard directly from our customers. This led to a huge “aha” moment: most users dropped off before the document capture process. They may have been struggling with a lack of instruction, not knowing the required time, or not understanding the steps involved in completing the onboarding process. That insight reframed my approach, and we ultimately redesigned the flow to prioritize orientation and clear instructions before proceeding to the setup steps. Below is an example of one of the screen designs, including some of the instructions we added. This kind of collaboration is user research. It’s not a substitute for talking to users directly, but it’s a powerful proxy when you have limited options. But What About Using AI? Glad you asked! Even AI tools, which are increasingly being used for idea generation, pattern recognition, or rapid prototyping, don’t replace collaboration; they just change the shape of it. AI can help you explore design patterns, draft user flows, or generate multiple variations of a layout in seconds. It’s fantastic for getting past creative blocks or pressure-testing your assumptions. But let’s be clear: these tools are accelerators, not oracles. As an innovation and strategy consultant Nathan Waterhouse points out, AI can point you in a direction, but it can’t tell you which direction is the right one in your specific context. That still requires human judgment, empathy, and an understanding of the messy realities of users and business goals. You still need people, especially those closest to your users, to validate, challenge, and evolve any AI-generated idea. For instance, you might use ChatGPT to brainstorm onboarding flows for a SaaS tool, but if you’re not involving customer support reps who regularly hear “I didn’t know where to start” or “I couldn’t even log in,” you’re just working with assumptions. The same applies to engineers who know what is technically feasible or PMs who understand where the business is headed. AI can generate ideas, but only collaboration turns those ideas into something usable, valuable, and real. Think of it as a powerful ingredient, but not the whole recipe. How To Strengthen Your UX Collaboration Skills? If collaboration doesn’t come naturally or hasn’t been a focus, that’s okay. Like any skill, it can be practiced and improved. Here are a few ways to level up: Cultivate curiosity about your teammates.Ask engineers what keeps them up at night. Learn what metrics your PMs care about. Understand the types of tickets the support team handles most frequently. The more you care about their challenges, the more they'll care about yours. Get comfortable facilitating.You don’t need to be a certified Design Sprint master, but learning how to run a structured conversation, align stakeholders, or synthesize different points of view is hugely valuable. Even a simple “What’s working? What’s not?” retro can be an amazing starting point in identifying where you need to focus next. Share early, share often.Don’t wait until your designs are polished to get input. Messy sketches and rough prototypes invite collaboration. When others feel like they’ve helped shape the work, they’re more invested in its success. Practice active listening.When someone critiques your work, don’t immediately defend. Pause. Ask follow-up questions. Reframe the feedback. Collaboration isn’t about consensus; it’s about finding a shared direction that can honour multiple truths. Co-own the outcome.Let go of your ego. The best UX work isn’t “your” work. It’s the result of many voices, skill sets, and conversations converging toward a solution that helps users. It’s not “I”, it’s “we” that will solve this problem together. Conclusion: UX Is A Team Sport Great design doesn’t emerge from a vacuum. It comes from open dialogue, cross-functional understanding, and a shared commitment to solving real problems for real people. If there’s one thing I wish every early-career designer knew, it’s this: Collaboration is not a side skill. It’s the engine behind every meaningful design outcome. And for seasoned professionals, it’s the superpower that turns good teams into great ones. So next time you’re tempted to go heads-down and just “crank out a design,” pause to reflect. Ask who else should be in the room. And invite them in, not just to review your work, but to help create it. Because in the end, the best UX isn’t just what you make. It’s what you make together. Further Reading On SmashingMag “Presenting UX Research And Design To Stakeholders: The Power Of Persuasion,” Victor Yocco “Transforming The Relationship Between Designers And Developers,” Chris Day “Effective Communication For Everyday Meetings,” Andrii Zhdan “Preventing Bad UX Through Integrated Design Workflows,” Ceara Crawshaw #collaboration #most #underrated #skill #one

In a Bloomberg interview Wednesday night in downtown San Francisco, Alphabet CEO Sundar Pichai pushed back against concerns that AI could eventually make half the company’s 180,000-person workforce redundant. Instead, Pichai stressed the company’s commitment to growth through at least next year. “I expect we will grow from our current engineering phase even into next year, because it allows us to do more,” Pichai said, adding that AI is making engineers more productive by eliminating tedious tasks and enabling them to focus on more impactful work. Rather than replacing workers, he referred to AI as “an accelerator” that will drive new product development, thereby creating demand for more employees. Alphabet has staged numerous layoffs in recent years, though so far, cuts in 2025 appear to be more targeted than in previous years. It reportedly parted ways with less than 100 people in Google’s cloud division earlier this year and, more recently, hundreds more in its platforms and devices unit. In 2024 and 2023, the cuts were far more severe, with 12,000 people dropped from the company in 2023 and at least another 1,000 employees laid off last year. Looking forward, Pichai pointed to Alphabet’s expanding ventures like Waymo autonomous vehicles, quantum computing initiatives, and YouTube’s explosive growth as evidence of innovation opportunities that continually bubble up. He noted YouTube’s scale in India alone, with 100 million channels and 15,000 channels boasting over one million subscribers. At one point, Pichai said trying to think too far ahead is “pointless.” But he also acknowledged the legitimacy of fears about job displacement, saying when asked about Anthropic CEO Dario Amodei’s recent comments that AI could erode half of entry-level white collar jobs within five years, “I respect that . . .I think it’s important to voice those concerns and debate them.” As the interview wrapped up, Pichai was asked about the limits of AI, and whether it’s possible that the world might never achieve artificial general intelligence, meaning AI that’s as smart as humans at everything. He quickly paused before answering. “There’s a lot of forward progress ahead with the paths we are on, not only the set of ideas we are working on today,some of the newer ideas we are experimenting with,” he said. “I’m very optimistic on seeing a lot of progress. But you know,” he added, “you’ve always had these technology curves where you may hit a temporary plateau. So are we currently on an absolute path to AGI? I don’t think anyone can say for sure.” Techcrunch event now through June 4 for TechCrunch Sessions: AI on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW #alphabet #ceo #sundar #pichai #dismisses