Artificial Intelligence: Here, There & Everywhere

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This story features FLIGHT CENTRE TRAVEL GROUP LIMITED, and other companies.
For more info SHARE ANALYSIS: FLT
The company is included in ASX200, ASX300 and ALL-ORDS

FNArena's latest AI deep dive reflects on the acceleration in spend, which companies are benefiting, and how AI is impacting productivity and sectors.

• The latest hyperscaler results confirmed AI-related momentum is gaining not fading
• Australia is (believe or not) at the top of AI productivity gains
• Drone technology receives an AI fillip 
• Logistics and transportation in the eye of the AI storm
• LLMs have usurped search engines for data manipulation
• Technology replacement hits some macro turbulence 

By Danielle Ecuyer

FNArena has dedicated a section to the modern day’s technological break-through: 

https://fnarena.com/index.php/tag/gen-ai/

“AI is rapidly reshaping industries globally, driving massive investment in infrastructure, accelerating productivity gains and disrupting business models, while creating both opportunities in sectors like retail, data centres and logistics, and emerging risks such as labour displacement, market competition, cybersecurity threats and unintended consequences like AI-driven manipulation.” 

[update provided by ChatGPT]

AI investment isn’t slowing, it’s accelerating 

While pockets of the Twitter-sphere (sorry, X) steadfastily argue over AI technology company valuations and potential dotcom-like bubbles in infrastructure builds, chip stocks and the like, artificial intelligence is undeniably making real progress, and it is being deployed.

Look no further than the latest quarterly updates from hyperscalers in the US, which prompted Morgan Stanley to highlight the upside potential to capex spending on AI, currently totalling US$2.8trn in the US.

As we discuss in this article, the impacts are accelerating both negative and positive impulses across sectors and businesses.

PayPal and Coinbase are the two latest US companies to announce AI-related job cuts. PayPal announced it is firing one in five workers to redirect funds to AI. Coinbase has made -700 people redundant.

Anthropic’s CEO Dario Amodei declared, “I don’t know what will happen to the group of today’s [software-as-a-service] incumbents as a group… I think individual companies, it’s very possible for them to lose market value, go bankrupt, completely.”

AI disruption is no longer a question of whether it impacts companies, it’s when.

Which makes the recent AI-related comments from corporate Australia at Macquarie’s annual conference comforting the C-suite locally is on the case.

Flight Centre ((FLT)) pointed to strategic initiatives in AI and customer loyalty initiatives, while WiseTech Global ((WTC)) explained AI agents are improving the software company’s return on invested capital, with the roll-out expanding to eleven from eight countries. Further agents are planned for product release in May/June.

From an infrastructure perspective, Ventia Services Group ((VNT)) highlighted at the conference it sees major opportunities in digital infrastructure, specifically data centres. The addressable market is anticipated to expand to around $5.9bn from $2.6bn over the next five years, a compound average growth rate of around 18% annually.

Reinforcing the tidal wave of spending coming to Australia, CDC, 49.7% owned by Infratil ((IFT)), onboarded the largest data centre contract in Australia’s history at 555MW (capex guidance at -$4bn) with a US hyperscaler for 10 years, with an option to extend.

NextDC ((NXT)) post recent fund raisings has planned capex of -$3bn in FY26 and -$5bn in FY27, and another circa -$3bn in FY28.

Even credit markets have been swept up in the AI phenomenon. As highlighted by Morgan Stanley, year-to-date data centre issuance has come in at US$21bn in 1H2026, or some 20% of total supply of debt financing.

In 2025, the sector saw US$438bn of gross issuance across leveraged finance. Three hyperscalers have issued more than US$80bn of investment grade (IG) unsecured debt year-to-date and over US$100bn across all currencies.

Morgan Stanley forecasts around US$400bn of AI/adjacent issuance in IG in 2026, of which some US$250bn–US$300bn will come from hyperscalers.

Alphabet started post May Day celebrations with a EUR9bn corporate debt issuance via tranches ranging from 4 years to 37 years.

“Morgan Stanley now estimates the Big 5 hyperscalers, Amazon, Alphabet, Meta, Microsoft and Oracle, will collectively spend roughly US$800 billion on capex in 2026 before climbing toward an eye-watering US$1.1 trillion in 2027.

“Andrew Sheets framed it perfectly. Next year’s spend is nearly double the 2025 levels and triple what was spent in 2024. That is not a normal investment cycle. That is the digital equivalent of rebuilding the interstate highway system while the economy is still driving on it.”

[Quote from Stephen Inness, SPI Asset Management.]

AI productivity impacts in the global workforce

As highlighted in previous AI sector updates at FNArena, the scale and enormity of the infrastructure build-out in financial terms often boggles the mind, which begs the question: are companies starting to see benefits of using AI in the workplace?

Taking a deeper dive into productivity and AI use, Morgan Stanley established a positive correlation between industry-level AI exposure and faster labour productivity growth.

The broker reportedly estimates industries with high AI exposure generated 1.7 percentage points of the 2.4 percentage points growth in output per employee in the four quarters through to the December 2025 quarter, against a 0.7 percentage point contribution in 2024.

Morgan Stanley’s AI adoption survey, which included companies in the US, UK, Germany, Japan and Australia, including an extensive cross-section of company size and annual revenue, had several findings.

• Across all countries, companies reported an average net loss of -4% of jobs over the last 12 months. Companies in the auto sector indicated the highest percentage of losses at -15%. Consumer companies retrained staff the most at 32%, while healthcare companies redeployed the most staff at 19%.
• Across all countries, more positions for inexperienced graduates or those with two to five years of experience were lost or not backfilled.
• Over the last year, net productivity increased on average by 11.5% as a result of using AI, with Australian companies experiencing the most gain at 14% and the least in Germany at 10%. The healthcare sector evidenced the highest net increase in productivity at 12% in the previous year, and real estate achieved a net increase in productivity of 11%.
• The highest productivity gains were in IT/software development and customer service/support.
• On average, companies had been implementing AI solutions for three years, with Australia at 3.4 years and Japanese companies at 3.2 years. Germany and the UK lagged. Larger companies adopted AI solutions sooner.
•  Regarding responsible use of AI, 95% of companies have a policy.

AI steps into the world of drone technology

Stepping into real-world examples, Barclays explores the impact of AI technology on the drone market, which is expected to reach US$250bn in value by 2035.

The war in the Middle East has again focused world attention on the growth in this emerging technology, with an estimated 2k recorded drone strikes in the first weeks of the war, as was already evidenced in the Russia/Ukraine war, where drone production has risen to nearly five million in 2025 from 800k units in 2023.

Between 2020 and 2025, the size of the global drone market has doubled to over US$40bn in 2025 from US$20bn in 2020, with drone patents up twelve times since 2012.

Notably, this technology is a game changer in global defence weaponry, with Barclays pointing to some one-way drones costing between US$20k to US$50k. AI is a key aspect of the technology, facilitating navigation, sensing, autonomy and decision-making — the key function that enables the drone technology.

Current data infers around 40%–50% of total drone-related revenues are generated from defence applications.

The broker divides the market into high-value specialised drones and low-cost scalable drones, with AI functionality expressed as being at the core of the drone technology.

While the cost of drone production is relatively low, Barclays emphasises the adjacent AI infrastructure to facilitate the drone technology is experiencing constraints across AI capex, energy and minerals.

Defence spending is becoming more intrinsically tied to AI infrastructure, electricity demand and investment in grids. All aspects of the AI infrastructure supply chain face potential challenges as defence spending rises and the application to this sector also lifts.

In 2024, global military expenditure came in at US$2.7trn. The United Nations estimates if current trends remain, global defence spending could rise to US$3.5trn by 2030 and to more than US$4.4trn by 2035.

Were defence spending to reach 5% of global GDP, the estimates lift to US$4.2trn by 2030 and US$6.6trn by 2035.

In 2025, Norway spent some US$723 more per person than the US, for the first time in NATO’s recorded history. The number of NATO allies achieving the 2% of GDP defence spending target reached 31, up from four in 2015.

The trickle-down impact on electricity, rare earths and data centre development becomes quickly apparent. As AI becomes more embedded in defence, so too do these adjacent sectors.

Defence currently represents around 16% of total demand for rare earth elements.

Drone defence applications are also seeping into civilian markets as costs decline and will equally experience a rise in demand from use in agriculture, with the ability to lower spraying costs by around -70% and spraying times by more than -90%, as well as reducing water usage by -90%.

Logistics and transportation front and centre also

Logistics and delivery markets are also expected to benefit from drone technology, notably in boosting last-mile delivery times to under 20 minutes.

Morgan Stanley cast an eye over the adoption of AI into the logistics and freight sectors and makes an interesting observation, highlighting efficiency gains in the logistics sector have historically been eroded by competition, including pricing pressures, defending market share and improving the service offering.

AI in the transportation sector is viewed as a “double-edged sword” as commoditisation of services can be driven by improving efficiency. For Morgan Stanley, this equates to a growing gap between the “leaders and laggards”.

Those companies that employ AI into data, networks and workflows have the capacity to differentiate themselves and improve metrics and customer outcomes, while other operators face more intense competition.

Freight forwarders and logistics brokers are viewed as being at the core of the “disintermediation debate”. The broker views companies with scale, value-added services, including proprietary data and customer relationships, as preferred. DSV is cited as an example.

Asset-heavy companies that own their own trucks, trailers, depots and infrastructure, rather than outsourcing or using contractors, have a competitive edge, with physical networks harder to replace.

AI has the capacity to improve efficiency rather than substitute the business model.

Equally, contract logistics and warehouses with long-term contracts and typically high switching costs offer a moat against AI-native start-ups.

In terms of freight software, a key question being asked by the market is whether AI and automation adoption in freight forwarding is best applied in-house or via a third-party SaaS operator, notably CargoWise from WiseTech Global ((WTC)).

Noting several large freight forwarders, including DSV, are erring towards an in-house build culture, the broker believes evidence is weighted towards the “best-in-class, deeply embedded operating platform” for most of the industry, which is WiseTech.

The stock is rated Overweight.

Nefarious use of AI in the insurance sector

Macquarie has shone a light on the not-so-positive outcomes of AI, in this instance on what they refer to as “AI recommendation poisoning”, which is hitting the insurance sector.

The heart of the matter resides with AI data being used to negatively impact on search engine outcomes and create SEO manipulation.

Historically, search engines were at the core of this trend, and as the broker highlights, it took years to limit data manipulation techniques.

Large language models have taken the baton from search, creating new problems.

AI tools such as Brandwatch and Sprout Social are analysing tremendous volumes of data via social media posts, comments, hashtags and the like.

To gauge changes in consumer sentiment, LLMs are used to analyse and establish shifts in trends, emerging themes and behavioural changes.

In turn, Microsoft has found some entities are embedding hidden instructions within “Summarise with AI” buttons, which can tamper with outputs from AI-dependent models.

These instructions attempt to manipulate the AI’s memory, prompting it to treat a company as a trusted source or prioritise it in responses, with the aim of biasing outputs toward its products or services.

Google DeepMind found corrupting as little as 0.01% of sample data can result in persistent behavioural changes in trained models.

Macquarie points to the rise in new claims which use LLMs to find structural problems in everyday insurance policy wordings and then lodge thousands of claims and complaints with the Australian Financial Complaints Authority.

Such is the volume, the agency is being stretched in terms of resources and managing rising claims for insurers and insurance brokers, representing almost 25% of general insurance disputes lodged with the authority in FY25.

Equally, insurers are taking further measures to insulate their exposure to emerging AI-driven cyber risks via policy wording and sub-limits.

Macquarie points to QBE Insurance ((QBE)) and Beazley limiting payouts for specific AI-related losses, referred to as “LLMjacking” events. Recovery is on average being capped at circa 10% of cyber policy limits.

Elsewhere, Morgan Stanley details how AI adoption can facilitate faster claims for hospitals. 

AI tailwinds come for technology facing retail stocks

Jarden questions whether the near-term impacts of the Middle East war on supply chains, higher inflation and interest rates on consumers can be offset by tailwinds for AI technology-facing retail stocks such as JB Hi-Fi ((JBH)) and Harvey Norman ((HVN)).

The broker points to technology demand strengthening, supported by a post-covid replacement cycle, rising prices and growing AI adoption, which together point to upside risk for IT and consumer electronics retailers.

Jarden sees three key drivers: an upcoming replacement peak in FY26–FY27 that could lift sales by 6%–9%, ongoing OEM-driven price inflation linked to higher component costs, and an emerging “AI halo” that is expanding device penetration and lifting average selling prices.

Memory cost inflation, particularly in DRAM, is driving material price increases across devices, though the impact on volumes remains uncertain given affordability pressures and potential shipment declines.

Retailer margins are expected to remain broadly stable, supported by OEM pricing power and promotional flexibility.

Over the medium term, AI is expected to materially expand the total addressable market, increase the number of connected devices per household and drive premiumisation across categories.

Overall, Jarden sees mid- to high single-digit revenue growth across the sector, with JB Hi-Fi and Harvey Norman best positioned, and potential upside to earnings forecasts if these tailwinds materialise.

Notwithstanding the upbeat medium term outlook, the quarterly update from JB HiFi’s management highlighted:

 “As we enter the important end of financial year trading period, in the technology categories we are seeing significant supplier component related cost increases and stock availability shortages, along with heightened competitive activity.”

Look no further than the current stock price melt up in memory chips stocks globally to isolate the supply shain challenges for technology goods.

Even positive macro trends can have negative unintended consequences.

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