Qaasid News

Download Our App

Author: admin

  • Type 2 diabetes physically changes the human heart, study finds

    Type 2 diabetes physically changes the human heart, study finds

    Researchers at the University of Sydney have uncovered new evidence showing that type 2 diabetes directly changes the heart’s structure and how it produces energy. These findings help explain why people living with diabetes face a much higher…

    Continue Reading

  • 2025 Winners and losers: OnePlus

    2025 Winners and losers: OnePlus

    2025 has been a busy year for OnePlus, with close to a dozen different launches across the globe in various categories. However, it was also one of the more controversial years for the brand, and signaled a change in direction for the…

    Continue Reading

  • Men’s Basketball Win Streak Ends At UC San Diego

    Men’s Basketball Win Streak Ends At UC San Diego

    Continue Reading

  • A big data approach to artificial intelligence driven predictive modelling for optimizing material properties in additive manufacturing

  • Joshua, R. J. N. et al. Powder bed fusion 3D printing in precision manufacturing for biomedical applications. Compr. Rev. Materials. 17 (3), 769 (2024).

    Google Scholar 

  • Zhou, L. et al. Additive manufacturing: A comprehensive review. Sensors 24 (9), 2668 (2024).

    Google Scholar 

  • Onuike, B. & Bandyopadhyay, A. Additive manufacturing of inconel 718–Ti6Al4V bimetallic structures. Additive Manuf. 22, 844–851 (2018).

    Google Scholar 

  • Ji, S. et al. Microstructural evolution and high-temperature resistance of functionally graded material Ti-6Al-4V/Inconel 718 coated by directed energy deposition-laser. J. Alloys Compd. 848, 156255 (2020).

    Google Scholar 

  • Park, C. W., Hajra, R. N., Kim, S. H., Lee, S. H. & Kim, J. H. Optimizing multi-interlayered additive manufacturing for high-strength robust joints in inconel 718 and Ti–6Al–4V alloys. J. Mater. Res. Technol. 25, 855–872 (2023).

    Google Scholar 

  • Hashemi, S. M. et al. Computational modeling of process–structure–property–performance relationships in metal additive manufacturing: a review. Int. Mater. Rev. 67 (1), 1–46 (2022).

    Google Scholar 

  • Sharma, A. S., Yadav, S., Biswas, K. & Basu, B. High-entropy alloys and metallic nanocomposites: processing challenges, microstructure development and property enhancement. Mater. Sci. Engineering: R: Rep. 131, 1–42 (2018).

    Google Scholar 

  • Kouraytem, N., Li, X., Tan, W., Kappes, B. & Spear, A. D. Modeling process–structure–property relationships in metal additive manufacturing: a review on physics-driven versus data-driven approaches. J. Physics: Mater. 4 (3), 032002 (2021).

    Google Scholar 

  • Yigezu, B. S., Mahapatra, M. M. & Jha, P. K. Influence of reinforcement type on microstructure, hardness, and tensile properties of an aluminum alloy metal matrix composite. J. Minerals Mater. Charact. Eng. 1 (4), 124–130 (2013).

  • Shahzad, K., Mardare, A. I. & Hassel, A. W. Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advances. Sci. Technol. Adv. Materials: Methods. 4 (1), 2292486 (2024).

    Google Scholar 

  • Shen, S. C. et al. Computational design and manufacturing of sustainable materials through first principles and materiomics. Chem. Rev. 123 (5), 2242–2275 (2023).

    Google Scholar 

  • Herriott, C. & Spear, A. D. Predicting microstructure-dependent mechanical properties in additively manufactured metals with machine and deep-learning methods. Comput. Mater. Sci. 175, 109599 (2020).

    Google Scholar 

  • Lee, K. H., Lim, H. J. & Yun, G. J. A data-driven framework for designing microstructure of multifunctional composites with deep-learned diffusion-based generative models. Eng. Appl. Artif. Intell. 129, 107590 (2024).

    Google Scholar 

  • Guo, S. et al. Machine learning for metal additive manufacturing: towards a physics-informed data-driven paradigm. J. Manuf. Syst. 62, 145–163 (2022).

    Google Scholar 

  • Nasiri, S. & Khosravani, M. R. Machine learning in predicting mechanical behavior of additively manufactured parts. J. Mater. Res. Technol. 14, 1137–1153 (2021).

    Google Scholar 

  • Min, Y. I. et al. Machine learning for predicting fatigue properties of additively manufactured materials. Chin. J. Aeronaut. 37 (4), 1–22 (2024).

    Google Scholar 

  • Valizadeh, M. & Wolff, S. J. Convolutional neural network applications in additive manufacturing: A review. Adv. Industrial Manuf. Eng. 4, 100072 (2022).

    Google Scholar 

  • Battula, S., Alla, S. N., Ramana, E. V., Kumar, N. K. & Murthy, S. B. Uncertainty quantification for digital twins in smart manufacturing and robotics: A review. In Journal of Physics: Conference Series (Vol. 2837, No. 1, p. 012059). IOP Publishing. (2024), October.

  • Akbari, P., Zamani, M. & Mostafaei, A. Machine learning prediction of mechanical properties in metal additive manufacturing. Additive Manuf. 91, 104320 (2024).

    Google Scholar 

  • Pouchard, L., Reyes, K. G., Alexander, F. J. & Yoon, B. J. A rigorous uncertainty-aware quantification framework is essential for reproducible and replicable machine learning workflows. Digit. Discovery. 2 (5), 1251–1258 (2023).

    Google Scholar 

  • Sanyal, S. et al. Mt-cgcnn: integrating crystal graph convolutional neural network with multitask learning for material property prediction. arXiv Preprint arXiv:1811.05660. (2018).

  • Kong, S., Guevarra, D., Gomes, C. P. & Gregoire, J. M. Materials representation and transfer learning for multi-property prediction. Applied Phys. Reviews, 8(2). (2021).

  • Massa, D., Chmielna, I. N., Kaszuba, G., Papanikolaou, S. & Sankowski, P. Transfer Learning in Materials Informatics: structure-property relationships through minimal but highly informative multimodal input.

  • Xie, T. & Grossman, J. C. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties. Phys. Rev. Lett. 120 (14), 145301 (2018).

    Google Scholar 

  • Karamad, M. et al. Orbital graph convolutional neural network for material property prediction. Phys. Rev. Mater. 4 (9), 093801 (2020).

    Google Scholar 

  • Shin, S. J. et al. Material-Adaptive anomaly detection using Property-Concatenated transfer learning in wire Arc additive manufacturing. Int. J. Precis Eng. Manuf. 25, 383–408 (2024).

    Google Scholar 

  • Yang, K. et al. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59 (8), 3370–3388 (2019).

    Google Scholar 

  • Luo, Q., Shimanek, J. D., Simpson, T. W. & Beese, A. M. An image-based transfer learning approach for using in situ processing data to predict laser powder bed fusion additively manufactured Ti-6Al-4V mechanical properties: 3D printing and additive manufacturing. 12(1), 48–60 (2024).

  • Li, L. et al. Uncertainty quantification in multivariable regression for material property prediction with bayesian neural networks. Sci. Rep. 14 (1), 10543 (2024).

    Google Scholar 

  • Tang, W. et al. Modeling and prediction of fatigue properties of additively manufactured metals. Acta Mech. Solida Sin. 36, 181–213 (2023).

    Google Scholar 

  • Battula, S., Alla, S. N., Ramana, E. V., Kumar, N. K. & Murthy, S. B. Uncertainty quantification for digital twins in smart manufacturing and robotics: A review. Journal of Physics: Conference Series, 2837(1), 012059. (2024), October.

  • Toprak, C. B. & Dogruer, C. U. Optimal process parameter determination in selective laser melting via machine learning-guided sequential quadratic programing. Proc. Institution Mech. Eng. Part. C: J. Mech. Eng. Sci. 239 (3), 807–820 (2025).

    Google Scholar 

  • ASTM F2924-14. Standard Specification for Additive Manufacturing Titanium-6 Aluminium-4 Vanadium with Powder Bed Fusion (ASTM International, 2014).

  • ASTM B213-13. Standard Test Methods for Flow Rate of Metal Powders Using the Hall Flowmeter Funnel (ASTM International, 2013).

  • Dadbakhsh, S., Speirs, M., Kruth, J. P. & Van Humbeeck, J. Influence of SLM parameters on mechanical properties of austenitic stainless steel 316L. Rapid Prototyp. J. 22 (4), 654–664 (2016).

    Google Scholar 

  • ASTM E11-20. Standard Specification for Woven Wire Test Sieve Cloth and Test Sieves (ASTM International, 2020).

  • ASTM E8/E8M-21. Standard Test Methods for Tension Testing of Metallic Materials (ASTM International, 2021).

  • Vrancken, B., Thijs, L., Kruth, J. P. & Van Humbeeck, J. Heat treatment of Ti6Al4V produced by selective laser melting: microstructure and mechanical properties. J. Alloys Compd. 541, 177–185 (2012).

    Google Scholar 

  • ASTM E112-13. Standard Test Methods for Determining Average Grain Size (ASTM International, 2013).

  • Savitzky, A. & Golay, M. J. E. Smoothing and differentiation of data by simplified least squares procedures. Anal. Chem. 36 (8), 1627–1639 (1964).

    Google Scholar 

  • Li, R. et al. Application of unsupervised learning methods based on video data for real-time anomaly detection in wire Arc additive manufacturing. J. Manuf. Process. 143, 37–55 (2025).

    Google Scholar 

  • Zheng, Y. et al. A thermal history-based approach to predict mechanical properties of plasma Arc additively manufactured IN625 thin-wall. J. Manuf. Process. 140, 91–107 (2025).

    Google Scholar 

  • Zuo, X. et al. 3D printed All-Natural hydrogels: Flame-Retardant materials toward attaining green sustainability. Adv. Sci. 11 (3), 2306360 (2024).

    Google Scholar 

  • Wang, L. et al. Multi-unit global-local registration for 3D bent tube based on implicit structural feature compatibility. Adv. Eng. Inform. 65, 103120. https://doi.org/10.1016/j.aei.2025.103120 (2025).

    Google Scholar 

  • Yao, S., Chen, F., Wang, Y., Zhou, H. & Liu, K. Manufacturing defect-induced multiscale weakening mechanisms in carbon fiber reinforced polymers captured by 3D CT-based machine learning and high-fidelity modeling. Compos. Part A: Appl. Sci. Manufac. 197, 109052 (2025).

    Google Scholar 

  • Zhang, Z. et al. Robotic wire-based friction stir additive manufacturing. Additive Manuf. 88, 104261 (2024).

    Google Scholar 

  • Niu, S. et al. Breaking the Trade-Off between complexity and absorbing performance in metamaterials through intelligent design. Small 21 (24), 2502828 (2025).

    Google Scholar 

  • Ren, D. et al. Harmonizing physical and deep learning modeling: A computationally efficient and interpretable approach for property prediction. Scripta Mater. 255, 116350 (2025).

    Google Scholar 

  • Jain, A. et al. Commentary: the materials project: A materials genome approach to accelerating materials innovation. APL Mater. 1 (1), 011002. https://doi.org/10.1063/1.4812323 (2013).

    Google Scholar 

  • Saal, J. E., Kirklin, S., Aykol, M., Meredig, B. & Wolverton, C. Materials design and discovery with High-Throughput density functional theory: the open quantum materials database (OQMD). JOM 65 (11), 1501–1509 (2013).

    Google Scholar 

  • Shen, C. et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel. Acta Mater. 179, 201–214 (2019).

    Google Scholar 

  • Sun, X. et al. Wire-based friction stir additive manufacturing of AZ31B magnesium alloy: precipitate behavior and mechanical properties. J. Magnesium Alloys. https://doi.org/10.1016/j.jma.2025.04.025 (2025).

    Google Scholar 

  • Chen, S., Khan, S. B., Li, N. & Xiao, C. In-depth analysis of sintering, exposure time, and layer height (µm) in LRS 3D printed devices with DLP. J. Manuf. Process. 133, 576–591 (2025).

    Google Scholar 

  • Ding, C., Li, C., Xiong, Z. & Li, Z. Computer-based big data technology in additive manufacturing and product design in sustainable manufacturing. Int. J. Adv. Manuf. Technol. 135 (9), 4855–4863 (2024).

    Google Scholar 

  • Long, J. et al. Printing dense and low-resistance copper microstructures via highly directional laser-induced forward transfer. Additive Manuf. 103, 104755 (2025).

    Google Scholar 

  • Xu, K. et al. Data-Driven materials research and development for functional coatings. Adv. Sci. 11 (42), 2405262 (2024).

    Google Scholar 

  • Shi, S. et al. Significance of α-Al cellular matrix in tensile behavior and work-hardening of additive manufactured AlSi10Mg alloy. Virtual Phys. Prototyp., 20(1), e2449189. (2025).

  • Zheng, Y. et al. A thermal history-based approach to predict mechanical properties of plasma Arc additively manufactured IN625 thin-wall. J. Manuf. Process. 140, 91–107. https://doi.org/10.1016/j.jmapro.2025.02.043 (2025).

    Google Scholar 

  • Wang, Z. et al. Digital-twin-enabled online wrinkling monitoring of metal tube bending manufacturing: A multi-fidelity approach using forward-convolution-GAN. Appl. Soft Comput. 171, 112684. https://doi.org/10.1016/j.asoc.2024.112684 (2025).

    Google Scholar 

  • Sun, S. et al. Wire-based friction stir additive manufacturing enables enhanced interlayer bonding in aluminum-matrix composites. J. Manuf. Process. 153, 1–15. https://doi.org/10.1016/j.jmapro.2025.08.078 (2025).

    Google Scholar 

  • Luo, J., Cheng, Z., Yu, N., Tian, Y. & Meng, J. A flexible skin material with switchable wettability for trans-medium vehicles. Int. J. Smart Nano Mater. 16 (2), 419–442. https://doi.org/10.1080/19475411.2025.2504442 (2025).

    Google Scholar 

  • Sun, F. et al. A versatile microporous design toward toughened yet softened Self-Healing materials. Adv. Mater. 36 (50), 2410650. https://doi.org/10.1002/adma.202410650 (2024).

    Google Scholar 

  • Zhang, Z. et al. Robotic wire-based friction stir additive manufacturing. Additive Manuf. 88, 104261. https://doi.org/10.1016/j.addma.2024.104261 (2024).

    Google Scholar 

  • Yang, G. et al. Prediction of restrained stress for UHPC: considering relationship between long-term and in-situ creep. Constr. Build. Mater. 484, 141722. https://doi.org/10.1016/j.conbuildmat.2025.141722 (2025).

    Google Scholar 

  • Zhang, J. et al. Modeling sediment flux in river confluences: A comprehensive momentum-based study. Water Resour. Res. 61, e2024WR039154. https://doi.org/10.1029/2024WR039154 (2025).

    Google Scholar 

  • Ren, D. et al. Harmonizing physical and deep learning modeling: A computationally efficient and interpretable approach for property prediction. Scripta Mater. 255, 116350. https://doi.org/10.1016/j.scriptamat.2024.116350 (2025).

    Google Scholar 

  • Shen, C. et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel. Acta Mater. 179, 201–214. https://doi.org/10.1016/j.actamat.2019.08.033 (2019).

    Google Scholar 

  • Zhang, Z. et al. Optimization of low-power femtosecond laser Trepan drilling by machine learning and a high-throughput multi-objective genetic algorithm. Opt. Laser Technol. 148, 107688. https://doi.org/10.1016/j.optlastec.2021.107688 (2022).

    Google Scholar 

  • Wang, H., Li, Y., Men, T. & Li, L. Physically interpretable Wavelet-Guided networks with dynamic frequency decomposition for machine intelligence fault prediction. IEEE Trans. Syst. Man. Cybernetics: Syst. 54 (8), 4863–4875. https://doi.org/10.1109/TSMC.2024.3389068 (2024).

    Google Scholar 

  • Liu, H., Zhang, D. & Geng, D. Design of a self-excited vibration tool bar for cutting difficult-to-machine alloys. Int. J. Mech. Sci. 300, 110456. https://doi.org/10.1016/j.ijmecsci.2025.110456 (2025).

    Google Scholar 

  • Xu, H. et al. An innovative normal self-positioning method with gravity and friction compensation for wall-climbing drilling robot in aircraft assembly. Int. J. Adv. Manuf. Technol. 138 (7), 3687–3704. https://doi.org/10.1007/s00170-025-15677-7 (2025).

    Google Scholar 

  • Cao, Y. et al. Structural-Functional-Integrated Ultra-Wideband Microwave-Absorbing composites based on in Situ-Grown graphene Meta-nanointerface. Adv. Funct. Mater. 34 (52), 2411271. https://doi.org/10.1002/adfm.202411271 (2024).

    Google Scholar 

  • Peng, J. et al. Numerical simulation and process optimization of laser welding in 6056 aluminum alloy T-Joints. Crystals 15 (1), 35. https://doi.org/10.3390/cryst15010035 (2025).

    Google Scholar 

  • Cao, Y. et al. Multi-Functional Self-Sensing electronic gasket for structural health monitoring of transportation pipelines. Adv. Funct. Mater. 35 (20), 2412634. https://doi.org/10.1002/adfm.202412634 (2025).

    Google Scholar 

  • Shi, S. et al. Significance of α-Al cellular matrix in tensile behavior and work-hardening of additive-manufactured AlSi10Mg alloy. Virtual Phys. Prototyp. 20 (1), e2449189. https://doi.org/10.1080/17452759.2024.2449189 (2025).

    Google Scholar 

Continue Reading

  • Bangladesh to demand T20 World Cup matches be moved outside India – France 24

    1. Bangladesh to demand T20 World Cup matches be moved outside India  France 24
    2. Bangladesh look to move T20 World Cup matches from India amid Mustafizur row  ESPNcricinfo
    3. ‘Days of slavery are over’: Bangladesh to demand its T20 World Cup matches…

    Continue Reading

  • These 1980s Home Décor Ideas Are Suddenly Trending Again

    These 1980s Home Décor Ideas Are Suddenly Trending Again

    There is a particular pleasure in leafing through old magazine archives. You notice which interiors still feel timeless, which collectables did people value and what recipes appeared again and again.

    There are, of course, certain ideas best…

    Continue Reading

  • Trust in Me (The Python’s Song) — musicians have been hypnotised by the Disney ditty

    Trust in Me (The Python’s Song) — musicians have been hypnotised by the Disney ditty

    Short but memorable, “Trust in Me (The Python’s Song)” comes at a crucial moment of what is now known as “mild peril” in the animated Disney film The Jungle Book (1967), when Kaa, the comic python villain, attempts to hypnotise Mowgli…

    Continue Reading

  • the productivity hack for 2026

    the productivity hack for 2026

    Stay informed with free updates

    Time (to mangle the Rolling Stones lyric) is definitely not on my side. Like many Financial Times readers I’m sure, life for most of last year involved juggling a sheaf of to-do lists, with the clock as a permanent reminder that such a volume and variety of tasks would not and could not be completed.

    Procrastination is not one of my psychological quirks, so that’s not it. And I’ve become quite good at “eating the frog” — doing one of the most unpleasant things on the list first, to give yourself a boost from getting it over with. No, the problem is quite simple: there is too much to do. This may be particularly the case for those with both young and old people to look after, as well as work responsibilities.

    Luckily (if not happily), it seems that for many middle-aged women, large chunks of extra time open up in your diary around the same time as the tasks and responsibilities proliferate. But there is a catch — those hours are from about 3am to maybe five or six in the morning.

    Yes, that’s right. Insomnia — it’s my tried and tested productivity hack for 2026. We’ve all been bludgeoned by the competitiveness of “the 5am club”, the go-getters who boast of starting their day super early to steal a march on the losers who need eight hours’ sleep a night. Well, this year I’m already planning the 3am club — think less business elite, more frazzled sandwich generation.

    Here’s how it goes. Strange mid-life biological changes start to interrupt your sleep patterns, leading to some sort of internal alarm going off at approximately 3am, regular as clockwork. It’s not that you are sleeping badly (although a newfound sympathy for friends who have suffered with life-long insomnia is perhaps a moral gain from the experience). You are just plain awake. And judging by the number of times colleagues and friends have described the same phenomenon, joking that we could have sorted out our work questions in the small hours when we were both up, this is widespread.

    You then have a choice: either worry about how bad the next day will feel, thereby worsening your anxious state; or embrace the bonus of a couple of extra hours to get on top of things. Personally, I found, following advice from a counsellor, that getting up to put on a load of laundry or mopping the floors has the benefit of a physical task that will eventually summon sleep again. Then I rationalise my to-do lists for the following days, and often get my physio exercises done. If I’m incurably alert, I might do some work, but using screens is not conducive to winding down again (though many are the columns I have written after midnight).

    You could also get creative — but be warned the output may not be a gift to the rest of humanity. One of the moments of 2025 was surely Reform UK’s sequin-clad Andrea Jenkyns, on stage at the party’s conference belting out lines from her own composition — a bombastic rock anthem entitled “Insomniac”. “I’m an insomniac!/ Staring at the ceiling, waiting for my thoughts to switch off.”

    Unforgettable, for sure, but not in a good way — and you have to assume she wrote it at night. So make sure you take a good hard look at your own masterpiece after you have managed to get some sleep.

    Reading, somehow, doesn’t work for me — the mind stays too active and I’m still awake as the rest of the household gets up. Last year I took up Japanese visible mending for our socks and sweaters, with mixed but useful (and therapeutic) results. The aim is to eventually fall back into an emergency top-up sleep before the day really has to start.

    Clearly, none of this is ideal. The mental and physical health effects of a lack of sleep can be severe. And on those nights when the internal alarm fails to go off, I slumber on blissfully and wake refreshed, much like my old (for which read young) self. But since at least for now it seems to be inevitable, better to accept and adjust. Chances are that this productivity hack will not solve the insomnia. But hey, we can beat those layabout 5am-ers at their own game and carry the day. And the night.

    miranda.green@ft.com

    Continue Reading

  • Twitter and Pinterest founders launch app as antidote to social media

    Twitter and Pinterest founders launch app as antidote to social media

    Two Silicon Valley veterans behind Twitter and Pinterest have launched a new app that is designed to be an antidote to the “terrible devastation” they say has been caused by social media.

    Biz Stone, a Twitter co-founder, and Evan Sharp, who co-founded online scrapbooking site Pinterest in 2010, have raised $29mn in funding for their new start-up West Co, according to a regulatory filing.

    West Co, which the pair founded in 2023, launched its first app, Tangle, in November. It is pitched as a “new kind of social network, designed for intentional living”.

    Tangle, which is at present accessible on an invite-only basis, suggests users share personal objectives or “intentions” with their friends, support each other’s goals and “reflect” on how they are achieved.

    “It is a tool for meaning that helps people plan with intention, capture the reality of their days, and see the deeper threads that shape their life,” the company said in a recent job advertisement.

    West Co, which is headquartered in San Francisco, said on its website that its mission is to “build tools to help people live life more on purpose”. Spark Capital, an early Twitter investor, led West Co’s seed financing round in 2024, according to another job ad.

    Stone said the current version of the app — which sends users notifications every morning asking “What’s your intention for today?” — was still an early test and could change before a full public launch.

    “It turns out that creating something to help people navigate their lifetime is difficult work,” he told the Financial Times, “but I think it’s worth it.”

    In a recent podcast interview, Sharp — who is West Co’s chief executive — described his “eight-year-long obsession” with “really trying to understand what we fundamentally disrupted with the phone and social media so that I could . . . help make that a little bit better”.

    “What could I build that might help address just some of the terrible devastation of the human mind and heart that we’ve wrought the last 15 years?” he said.

    Stone and Sharp are among several Silicon Valley executives grappling with the side effects of the products and services that they built, even as their companies’ success made them wealthy.

    Sir Jonathan Ive, the former Apple designer who helped birth the iPhone, has described his project to develop an AI-based consumer device with OpenAI as a response to the “unintended consequences” of the smartphone. Sharp spent two years working at LoveFrom, Ive’s design firm, before launching West Co.

    Tangle is Stone’s latest attempt to capitalise on his earlier success. He is also a co-founder of Medium, an online publishing platform, and Jelly, a question-and-answer app that was later acquired by Pinterest. He launched investment firm Future Positive in 2019 and at present serves on the board of Mastodon, another social networking group.

    After leaving Twitter in 2021, Stone clashed with Elon Musk after the Tesla and SpaceX chief acquired the company, which the billionaire has now renamed X. Musk is “not a serious person”, Stone said in a post in December 2022, describing the changes Musk made to the service as “heartbreaking”.

    Several of West Co’s founding team previously worked at Twitter and Pinterest. Another early employee, Reverend Sue Phillips, a former Unitarian Universalist church minister turned tech company adviser, now serves as the start-up’s “head of wise AI and ancient technologist”, according to her LinkedIn profile.

    Continue Reading

  • US to extend productivity lead on back of AI boom, say economists

    US to extend productivity lead on back of AI boom, say economists

    More than three-quarters of economists expect the US to maintain or widen its productivity lead over the rest of the world, because of artificial intelligence, deep capital markets and relatively low energy costs, a Financial Times survey has found.

    In the global poll, 31 per cent of 183 respondents thought the US would retain its advantage in productivity, while another 48 per cent expected the country to increase its dominance.  

    The economists were based in China, the Eurozone, the UK and the US.

    Productivity growth — which measures progress in converting inputs such as hours worked into goods and services — ultimately allows companies to increase wages and profits, improving living standards.

    US labour productivity rose 10 per cent between 2019 and 2024, thanks to rapid technological advances and the reallocation of workers during the Covid-19 pandemic. By contrast, it remained largely stagnant in the UK and Eurozone, according to OECD data.

    Jumana Saleheen, head of Vanguard’s investment strategy group in Europe, said US productivity was set to “pull away from other developed market economies” thanks to the country’s dynamic capital markets, flexible labour force and lead in emerging technologies.

    She added that Europe risked “falling further behind”, with research and development still heavily focused on traditional sectors such as automotive and pharmaceuticals.

    Saleheen also noted structural challenges for the EU, including fragmented infrastructure, more rigid labour markets and less supportive capital markets.

    The US economy is set for the strongest growth in the G7 this year, according to the OECD — buoyed by a tech-led investment boom and stock market gains that are bolstering wealth and spending among better-off households.

    The gains have helped counter some of the economic damage done by US President Donald Trump’s trade wars but have also raised fears of an unsustainable AI-related bubble.

    The FT survey, which was carried out in December, suggests economists do not expect the trends powering US outperformance to be reversed soon.

    AI and related digital technologies were the new productivity frontier, said Nina Skero, chief executive of the Centre for Economics and Business Research, and the US’s “position as a leader in investment and development of these technologies will extend the US’s productivity lead”.

    Some content could not load. Check your internet connection or browser settings.

    The trend is supported by a divergence in business investment. In the US, investment jumped 24 per cent in the second quarter of 2025 compared with the same period in 2019, before the pandemic. It contracted 7 per cent over that time in the Eurozone, according to Oxford Economics.

    Some economists surveyed by the FT warned that the surge in AI investment could reflect a “bubble” — a term mentioned 25 times in responses — and cautioned that a sharp correction might weigh on US output and productivity.

    A reversal in the stock market gains made by US tech could also have international repercussions via tighter financial conditions, softer external demand and rising risk aversion, some economists said.

    But the majority of respondents to the poll, which represented the UK and Eurozone more heavily than China and the US, still expected America to maintain its productivity edge globally. Overall, the poll surveyed 207 economists, although not all responded to every question.

    Some content could not load. Check your internet connection or browser settings.

    The US was starting from a “position of strength” in the productivity race, said Thomas Simons, chief US economist at Jefferies.

    Respondents also pointed to the US’s structurally lower and more predictable energy costs, flexible labour market and large domestic economy.

    The US benefits from “structurally lower and more predictable energy costs than Europe and many Asian economies, underpinned by an administration that treats energy policy as a driver of economic prosperity rather than a vehicle for moral posturing at the expense of growth and living standards,” said Martin Beck, chief economist at the consultancy WPI Strategy.

    Line chart of 2026 GDP growth forecast, by date of forecast showing Economists expect stronger 2026 growth in the US

    Europe is widely seen by economists as constrained by over-regulation, weaker investment, rigid labour markets and a business environment less favourable to cutting-edge technologies. The UK had the additional weight of the Brexit legacy to deal with, some economists contended.

    “While the US and others have made major strides in AI, the UK has spent much of the last decade chasing the Brexit tail, diverting attention and resources from innovation,” said Evarist Stoja, professor of finance at the University of Bristol Business School.

    Experts acknowledge that the US faces rising AI competition from Asia. “Other countries — particularly in Asia — will move to the frontier, meaning that the relative advantage of the US will erode somewhat but will not be eliminated,” said Jagjit S Chadha, professor of economics at the University of Cambridge. 

    China has the second-largest cumulative venture capital investment in AI since 2012 after the US and over three times more than the EU, according to the OECD.

    The US may be at the forefront of the AI wave, but “much of this may prove a misallocation of resources,” said David Owen, chief economist at Saltmarsh Economics. “Ultimately, much of the benefits will go to the users of the technology (elsewhere), not the early-stage innovators.”

    Many economists also highlighted risks from US trade protectionism, restrictive immigration policies, fiscal imbalances and political instability that could eventually undermine productivity growth. 

    US productivity gains from trade “have been traded away for chump change tariff revenues”, warned Robert Barbera, director of the Johns Hopkins University Center for Financial Economics.

    Jonathan Portes, professor of economics and public policy at King’s College in London, warned that a “toxic combination” of tariffs, an erosion of the quality of US government administration and anti-immigration policy would “over time do significant damage to the US economy”.

    Additional reporting from Olaf Storbeck, Claire Jones and Thomas Hale

    Video: The AI rollout is here – and it’s messy | FT Working It

    Continue Reading