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AI Agents Move from Boardroom Buzzword to Business Infrastructure

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AI Agents Move from Boardroom Buzzword to Business Infrastructure

The age of artificial intelligence agents has arrived. From the neon-lit corridors of a Las Vegas cloud conference to the factory floors of Osaka, two converging stories this week underscore that autonomous AI systems are transitioning from corporate pilot programs into the foundational layer of global enterprise.

Key takeaways

  • Google has officially ended the experimental phase of enterprise AI, consolidating its agent strategy under “Gemini Enterprise” and backing it with $175–185 billion in capital spending and new TPU 8t/8i chips purpose-built for agentic workloads.
  • Japan’s AI agent market is set to grow nearly tenfold from $250 million in 2024 to $2.43 billion by 2030, driven less by hype than by demographic necessity as the country confronts a shrinking workforce and an aging population.
  • Across both markets, agentic AI has crossed the threshold from pilot to deployment, with Forrester documenting 55–75% process cycle-time reductions and the global enterprise agent market projected to reach $142.35 billion by 2035.

The question that once dominated boardroom discussions, whether agentic AI was ready, has quietly been retired.

Google Draws a Line in the Sand

At Alphabet’s annual Google Cloud Next conference in Las Vegas, CEO Sundar Pichai and Google Cloud chief Thomas Kurian made no effort to temper expectations. Kurian set the tone during the opening keynote, declaring that the experimental phase is now behind the industry and that the real challenge is just beginning. The message was deliberate: for enterprise customers, the moment for cautious evaluation has passed.

Google signaled to investors that AI agents, human-like digital assistants capable of autonomous planning, decision-making, and action, are the linchpin of its strategy to monetize artificial intelligence, with large enterprise customers emerging as the industry’s most reliable revenue stream.

The financial commitment backing this shift is substantial. Pichai reaffirmed Alphabet’s capital spending plan of between $175 billion and $185 billion for the year, with just over half of the company’s investment in machine learning computing power dedicated to its cloud business.

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To consolidate its position, Google announced it was unifying a suite of AI products under the name “Gemini Enterprise,” most notably rebranding and expanding Vertex AI, a platform that allows cloud customers to select from a range of AI models for business applications. According to Kurian, the platform’s primary use case has already undergone a quiet but dramatic transformation, shifting away from traditional machine learning workflows toward a surge in users building custom AI agents.

The hardware strategy is equally ambitious. Google unveiled two new custom tensor processing units, the TPU 8t and TPU 8i, both designed end-to-end for what the company describes as the age of agents. The TPU 8i, tuned for inference workloads that power real-time agent responses, delivers 80 per cent better performance than the prior generation.

Google’s push is also a direct response to intensifying competition. While traditional cloud rivals such as Amazon and Microsoft remain ahead in overall market share, Google has grown its cloud market share to 14 per cent as of end-2025, buoyed by heavy investment in AI, data centres, custom chips, and networking infrastructure. Meanwhile, model providers including OpenAI and Anthropic have aggressively shifted resources toward enterprise customers, pushing into application-layer tools and agent-building platforms, forcing Google to differentiate not on model capability alone, but on end-to-end enterprise infrastructure.

Japan: Where Demographics Are Driving a $2.43 Billion Imperative

While Silicon Valley frames agentic AI as a strategic opportunity, Japan’s adoption is being shaped by something far more urgent: structural necessity.

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Japan’s AI agent market was valued at $250 million in 2024 and is projected to reach approximately $2.43 billion by 2030, growing at a compound annual growth rate of 46.3%, the highest among all enterprise software verticals in the country, and one of the fastest in the Asia-Pacific region.

The driver is not technological enthusiasm. It is demography. With more than 28 per cent of Japan’s population aged 65 or above and the working-age population shrinking for over two decades, the country faces a fundamental gap in manpower that immigration reform and productivity initiatives cannot adequately bridge. AI agents, autonomous software systems capable of continuous operation without human supervision, offer the most viable solution at scale.

Government policy has hardened this trajectory into law. Japan adopted the Act on the Promotion of Research and Development and Utilization of AI-related Technologies in May 2025, establishing an AI Strategic Office at the Cabinet Office under direct oversight of the Prime Minister and underpinning a ¥3 trillion public-private partnership for frontier AI infrastructure. The country’s broader Society 5.0 framework, a government blueprint for a human-centric, technology-integrated society, has positioned AI agents as instruments of national industrial policy, not just commercial innovation.

Sector-level adoption reflects Japan’s industrial heritage. Manufacturing and industrial automation represents the largest current segment, with AI agents deployed for predictive maintenance, quality control anomaly detection, and supply chain coordination, functions that align naturally with Japan’s precision-manufacturing culture. Healthcare is the fastest-growing vertical, driven by the mounting pressures of an aging population, with agents already deployed in hospital networks for patient triage, medication management, and clinical documentation.

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The competitive landscape is increasingly crowded. Domestic heavyweights including Fujitsu, NEC, Hitachi, and NTT Data have each launched enterprise AI agent platforms in the past year, while SoftBank, NEC, Sony, and Honda have partnered to develop a sovereign Japanese AI model with a capacity of one trillion parameters, a direct challenge to US and Chinese frontier AI dominance. Global players are moving in parallel: Microsoft pledged $2.9 billion in Japanese AI and cloud investment in April 2024, while NTT Corp. released its Smart AI Agent Ecosystem backed by $59 billion in five-year domestic commitments.

Convergence: A Global Infrastructure Shift

What emerges from these two narratives, Google’s enterprise pivot and Japan’s structural adoption, is a coherent picture of an industry crossing a threshold. The terminology has changed. Vendors no longer speak of AI “pilots” or “proofs of concept.” The word now is deployment.

Google’s Kurian told Reuters that the shift reflects models becoming significantly more sophisticated, with enterprises now building custom agents rather than merely experimenting with off-the-shelf tools. In Japan, research firm Forrester has found that enterprises deploying agentic AI workflows are achieving process cycle time reductions of between 55 and 75 per cent in relevant use cases, numbers that make the business case increasingly difficult to ignore.

The broader global enterprise AI agent market, valued at $6.65 billion in 2025, is forecast to reach $142.35 billion by 2035, with Japan’s trajectory outpacing even that remarkable global average. As Google bets its infrastructure roadmap on agents and Japan bets its economic future on them, the remaining question for enterprises worldwide is no longer whether to act, but at what speed, and with whose tools.

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A Career in Public Sector Leadership

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A Career in Public Sector Leadership

Monique Appeaning has spent more than two decades working inside government systems that most people never see. Her work has not been about headlines. It has been about structure, process, and results.

Based in Baton Rouge, Louisiana, Appeaning built her career step by step across both the executive and legislative branches. Along the way, she developed a reputation for discipline, clear thinking, and a steady approach to complex challenges.

“The best advice I’ve ever received is that applying logic to something illogical only breeds frustration,” Appeaning said. “That has stayed with me throughout my career.”

That mindset has shaped how she works. It also explains why her career has centered on accountability, budgeting, and system-level thinking.

Early Career in Government Operations and Budgeting

Appeaning’s career began in government operations, where she focused on budgeting and planning. Early roles required her to review agency budgets, analyze financial requests, and help prepare executive budget materials.

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These were technical roles, but they came with real impact. State budgets guide how resources are used across major systems. That includes infrastructure, public safety, and administrative functions.

She worked closely with agency leaders, program managers, and officials at multiple levels of government. The work required both detail and communication.

“Budgeting is what I’m most proud of as a skill,” she said. “It connects decisions to outcomes.”

Her early experience helped her understand how large systems operate from the inside. It also set the foundation for more senior roles.

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Growing Into Leadership Roles in State Government

As her career progressed, Appeaning moved into leadership positions focused on management and finance. In these roles, she oversaw teams and supported key operational functions.

Her responsibilities included budgeting, purchasing, human resources, and performance reporting. She also worked on policy development and long-term planning efforts tied to government operations.

At one stage, she managed teams of professionals across multiple functions. That required coordination, consistency, and the ability to handle competing priorities.

She also worked closely with legislative staff and other state officials. These interactions helped bridge the gap between policy goals and operational realities.

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“My years of work in the executive branch made the opportunity to work for the legislative branch an exciting one for me,” Appeaning said.

That transition gave her a broader view of how decisions are made and implemented.

Work in Legislative Analysis and Public Accountability

Appeaning later took on a role focused on legislative fiscal analysis and special projects. In this position, she provided factual and unbiased information to lawmakers.

Her work included analyzing executive budgets, evaluating legislative proposals, and reviewing the performance of state programs. She also prepared reports and presented findings to committees.

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These responsibilities required objectivity. They also required the ability to explain complex data in a clear way.

She worked with both the Louisiana House of Representatives and Senate. Her role supported decision-making at the highest levels of state government.

In addition, she served as a project leader for a regional report that involved collecting and analyzing data from multiple states. This type of work highlights the scale and coordination involved in her career.

“Clear information matters,” she said. “It helps people make better decisions.”

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What Sets Monique Appeaning Apart?

Appeaning’s career stands out because of its range and consistency. She has worked across different parts of government, but her focus has remained steady.

Her work has centered on:

  • Accountability
  • Transparency
  • Data-informed decision-making
  • Long-term system improvement

She is known for a structured and disciplined approach. She focuses on facts and avoids unnecessary complexity.

“What inspires me is knowing that my labor is not in vain,” she said.

That perspective reflects a long-term view. Many of the systems she has worked on take years to improve. Results are often gradual, not immediate.

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Her contributions have also been recognized with the Military Civilian Award from the Louisiana National Guard. This reflects her commitment to service and operational excellence.

A Career Built on Logic and Focus

One of the defining traits of Appeaning’s career is consistency. She has worked in environments where decisions are complex and stakes are high. Her approach has remained grounded in logic and clarity.

She believes strong systems are built through careful planning and steady execution. That belief has guided her work across roles and responsibilities.

There is also a personal philosophy behind her career. She once described growth as a process where there is “struggle in the caterpillar” before it “becomes a butterfly.”

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That idea reflects how she views progress. It takes time. It requires effort. And it often happens behind the scenes.

Today, her career offers a clear example of how experience in budgeting, analysis, and operations can shape a broader leadership path.

Her work may not always be visible to the public. But it plays a role in how systems function and how decisions are made.

And for Appeaning, that is where the value lies.

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Platforms, AI Orchestrators, and Workflow Design

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Most product teams adopt AI tools one by one — a code assistant here, a design generator there — and then wonder why delivery is still slow. The bottleneck was never individual tasks. It was always coordination.

Most product teams adopt AI tools one by one — a code assistant here, a design generator there — and then wonder why delivery is still slow. The bottleneck was never individual tasks. It was always coordination.

That’s what makes end-to-end product development with AI orchestration a different conversation: instead of asking “which AI tool should we add?”,  you start asking, “How do we make the whole system work?”

What is AI orchestration?

AI orchestration is a coordination and control layer for product delivery. When multiple AI models, tools, agents, and humans work on the same product, something has to define how work runs, in what order, with which inputs, and what to validate before progressing.

An AI orchestrator acts as the execution engine within this layer. It translates high-level intent into structured tasks, routes them to the appropriate execution layer, maintains shared context across steps, and triggers human intervention when decisions require judgment.

Isolated AI tools improve individual tasks. Orchestration improves the system. Without it, even strong tools produce fragmented outputs — slowing delivery through rework, misalignment, and unclear ownership at handoff points.

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Why end-to-end product development needs AI orchestration

The most common failure mode in AI-assisted product teams isn’t bad tooling. It’s disconnected tooling. Design, engineering, and QA each use AI independently, but integration points — where work moves between disciplines — remain manual and error-prone.

Agentic AI orchestration changes this by treating the entire product lifecycle as a single coordinated system. Work moves from validated spec to generated code to tested output to staged release, with the right humans reviewing at the right moments. The difference between AI assistance and AI-coordinated delivery is what actually ships.

How AI orchestration works across the product lifecycle

Discovery phase: We conduct research, validate assumptions, and define scope simultaneously rather than executing it step by step. This shortens analysis time while keeping depth and accuracy.

Product planning and prioritization: The system models different prioritization options, highlights dependencies, and surfaces risks early. Humans make final decisions based on complete context, not fragmented inputs.

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UX/UI design and prototyping: AI generates wireframes, applies design system rules, and flags accessibility issues. Designers focus on user flows and edge cases, while the system keeps everything aligned with the product spec.

Engineering and code generation: We don’t send AI code straight to production. The system runs automated tests and architecture checks before human review, reducing rework and keeping the codebase consistent.

QA, security, and compliance: We run tests automatically after every meaningful change. Compliance checks happen during development instead of at the end. Humans only review exceptions or unclear cases.

Release and post-launch iteration: We continuously collect production data, errors, and user behavior signals. The system feeds this back into development, so improvements happen as part of the workflow, not after release.

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Core components of an AI orchestration platform

First, it needs task routing, which decides what work goes to AI, what goes to humans, and under what conditions. Second, it needs shared context management, so information doesn’t get lost between steps. Third, it must connect to existing systems through API and tool integrations.

It also needs human checkpoints for decisions that require judgment, and full visibility (logs and tracking) so every action can be traced and reviewed. Finally, it needs failure handling, so one broken step doesn’t disrupt the whole process.

Teams like Goodface agency have operationalized this as a human-led AI-orchestrated framework — with senior experts owning architecture and decisions while AI handles execution — delivering 25–30% higher efficiency within the same time and budget.

AI orchestration vs related concepts

Vs workflow orchestration: Workflow orchestration handles deterministic sequences. AI orchestration introduces non-deterministic elements — language model outputs, agent decisions — where uncertainty is a first-class concern.

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vs AI agent: Agents execute. Orchestrators govern. An AI agent orchestration layer coordinates multiple agents, manages shared context, and enforces rules that individual agents don’t have visibility into.

Vs automation: Automation handles deterministic tasks. Orchestration handles workflows that involve judgment, generation, and variable outputs that require validation before they move forward.

Risks and limitations

Context loss between agents is the most common failure mode. Security exposure from misconfigured data access is the most serious. Tool sprawl, cost overruns from uncontrolled token usage, and accountability gaps when human ownership isn’t clearly defined round out the main risks. Over-automation without accountability is where orchestration projects most often break down in production.

KPIs for measuring AI orchestration

Track delivery cycle time, handoff reduction (manual coordination touchpoints eliminated), defect rates in automated validation versus staging, cost per completed workflow, and human review rate. A declining human review rate indicates the system is routing better; a rising one is an early warning sign worth investigating before it compounds.

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FAQ

What is orchestration in AI product development? A coordination system that determines how AI tools, agents, and humans work together across the product lifecycle — routing tasks, sharing context, enforcing quality gates, and managing handoffs from discovery through deployment.

What does an AI orchestrator do in an end-to-end workflow? It decomposes product intent into structured tasks, assigns each to the appropriate execution layer, exchanges context, monitors outputs, and triggers human review where automation isn’t sufficient.

When does a product team need an AI orchestration platform? When multiple AI tools don’t share context, when coordination creates more delay than execution, or when AI output quality is inconsistent across the pipeline.

Can AI orchestration support regulated product environments? Yes — when governance is built in explicitly. Audit trails, configurable human-in-the-loop checkpoints, and access controls can meet fintech and healthtech compliance requirements.

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How does AI orchestration improve delivery speed and quality? By running parallel workstreams, reducing rework at handoff points, and enforcing validation continuously rather than end-of-sprint.

What should companies look for in an AI orchestration platform? Human-in-the-loop configurability, deep observability, integration flexibility, and reliability under production load. Legibility — being able to understand what happened when something goes wrong — is a core requirement, not a nice-to-have.

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Sandisk Corporation (SNDK) Q3 2026 Earnings Call Transcript

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OneWater Marine Inc. (ONEW) Q1 2026 Earnings Call Transcript

Sandisk Corporation (SNDK) Q3 2026 Earnings Call April 30, 2026 4:30 PM EDT

Company Participants

Ivan Donaldson – Vice President of Investor Relations
David V. Goeckeler – Chairman & CEO
Luis Visoso – Executive VP & CFO

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Conference Call Participants

Mark Newman – Bernstein Institutional Services LLC, Research Division
Joseph Moore – Morgan Stanley, Research Division
Benjamin Reitzes – Melius Research LLC
Christopher Muse – Cantor Fitzgerald & Co., Research Division
James Schneider – Goldman Sachs Group, Inc., Research Division
Asiya Merchant – Citigroup Inc., Research Division
Vijay Rakesh – Mizuho Securities USA LLC, Research Division
Blayne Curtis – Jefferies LLC, Research Division
Victor Santiago – Evercore ISI Institutional Equities, Research Division
Ruplu Bhattacharya – BofA Securities, Research Division

Presentation

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Operator

Good afternoon, and welcome to Sandisk’s Third Quarter Fiscal Year 2026 Earnings Conference Call. [Operator Instructions] Please note this event is being recorded.

I would now like to turn the conference over to Ivan Donaldson, Vice President of Investor Relations. Please go ahead.

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Ivan Donaldson
Vice President of Investor Relations

Before we begin, please note that today’s discussion will contain forward-looking statements based on management’s current assumptions and expectations, which are subject to various risks and uncertainties. These forward-looking statements, including expectations for our technology and product portfolio, our business plans and performance, our capital allocation priorities, market trends and opportunities and our future financial results. We assume no obligation to update these statements. Please refer to our annual report on Form 10-K and our other filings with the SEC for more information on the risks and uncertainties that could cause actual results to differ materially from expectations.

We will also make references to non-GAAP financial measures today. Reconciliations between the non-GAAP and comparable GAAP financial measures are included in the written materials posted in the Investor Relations section of our website.

With that, I’ll turn the call over

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South Korean April exports rise 48.0% y/y as chip boom extends

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South Korean April exports rise 48.0% y/y as chip boom extends


South Korean April exports rise 48.0% y/y as chip boom extends

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Asara CEO appointed MD

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Asara CEO appointed MD

Asara Resources chief executive Matthew Sharples has been appointed managing director of the Subiaco-based junior.

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Trump signs executive order aiming to expand retirement account access

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Trump signs executive order aiming to expand retirement account access

President Donald Trump signed an executive order on Thursday to help expand access to retirement savings accounts for Americans who do not have employer-provided plans.

“Beginning at the start of next year, every American will be able to go to TrumpIra.gov and open a new low-cost IRA account,” Trump said in the Oval Office. “You’ll then be able to access the same type of retirement accounts that federal employees enjoy through the Thrift Savings Plans, which are incredible. As part of the Federal Savings Match program, low-income Americans will be eligible to receive up to $1,000 per year in matching funds deposited directly into their accounts.”

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“The great thing for millions of Americans who lack employer-sponsored plans, this will be really revolutionary because they’ll be covered,” Trump said. “Nobody thought that was possible. For example, if a 25-year-old who is eligible for a Savers Match program invest just $165 a month under the matching federal contributions, they will have an estimated $465,000 in their account by the time they’re 65 years old. In other words, they’ll be rich. And there’s something awfully nice about that.”

A White House official confirmed the planned order to FOX Business earlier Thursday. 

The Trump administration’s effort will work in conjunction with 2022 legislation that instructs the federal government to match retirement-plan contributions for people earning below $35,000 with as much as $1,000 beginning next year,” Semafor reported.

US RAKES IN $3B IN 90 DAYS FROM INTEL STOCK, TRUMP SAYS

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President Trump

President Donald Trump attends the White House Correspondents’ Association Dinner on April 25, 2026, in Washington, D.C. (Kevin Mazur/Getty Images for OP)

The president’s new order will instruct the Treasury Department to open a TrumpIRA.gov website by the time the matching opportunity takes effect in January, a White House official indicated. Workers will be able to utilize the site to filter private-sector retirement plans based on different factors so they can join one that would enable them to receive the match if qualified.

TREASURY FREEZES $344M IN CRYPTO AS ‘OPERATION ECONOMIC FURY’ PUSHES IRAN TO INDUSTRIAL BREAKING POINT

$100 bills

A picture taken on Dec. 7, 2021 in Istanbul shows U.S. currency. (Ozan Kose/AFP via Getty Images)

The department will screen the plans on the site, but will not team up with certain financial institutions like it did with Trump Accounts, the official noted, according to Semafor.

FEDERAL GOVERNMENT MADE $186B IN IMPROPER PAYMENTS LAST FISCAL YEAR

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President Trump

U.S. President Donald Trump waves to the media after walking off of Air Force One at Miami International Airport on April 11, 2026 in Miami, Fla.  (Tasos Katopodis/Getty Images / Getty Images)

CLICK HERE TO GET FOX BUSINESS ON THE GO

The order will instruct the Treasury Department to publicize the match and release information for those in the private sector who wish to give workers’ IRAs, the outlet reported, adding that the White House official compared the concept to the Dells’ pledge to seed Trump Accounts for kids.

Fox News’ Patrick Ward contributed to this report

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Lantern Pharma Inc. (LTRN) Discusses Live Demonstration of withZeta.ai AI Platform for Rare Cancer Drug Discovery Transcript

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OneWater Marine Inc. (ONEW) Q1 2026 Earnings Call Transcript

Craig Brelsford

Hi. This is Craig Brelsford with RedChip Companies. Thank you for joining us for what promises to be an exciting session with Lantern Pharma. Today’s session is centered around a live real-time demonstration of withZeta.ai, Lantern Pharma’s next-generation AI platform designed to transform how oncology drugs are discovered, particularly in rare cancers. Rather than just talking about the technology, you’ll see it in action, executing research workflows, synthesizing complex scientific data and generating insights in real-time. This is a rare opportunity to observe how AI is being applied at the front lines of drug development.

Lantern Pharma, which trades on the NASDAQ under the ticker LTRN, is positioning this platform not only as a scientific engine, but also as a scalable subscription-based business with meaningful commercial potential.

Joining us today is Panna Sharma, Chief Executive Officer, President and Director of Lantern Pharma, who will guide us through the demonstration and discuss the broader implications of this technology. We will begin with the presentation and demo momentarily followed by a Q&A session. [Operator Instructions]

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Before we begin, please allow me to read the safe harbor statement. This call may contain forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. All statements pertaining to future financial and/or operating results, along with other statements about the future expectations, beliefs, goals, plans or prospects expressed by management constitute forward-looking statements. Any statements that are not historical facts should also be considered forward-looking statements. And of course, forward-looking statements involve risks and uncertainties.

Panna, go right ahead.

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Perth home values lift, but show signs of slowing

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Perth home values lift, but show signs of slowing

Cotality’s latest home value index shows that Perth added more than $21,000 to its median home value in April.

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Start Hamstring Rehab Enters Key On-Court Phase as Lakers Push for Playoff Survival

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Luka Doncic

LOS ANGELES — Nearly four weeks after suffering a Grade 2 left hamstring strain, Los Angeles Lakers superstar Luka Doncic continues a cautious rehabilitation protocol focused on controlled on-court movement and progressive loading, with no firm timetable for return as the team battles through its first-round playoff series against the Houston Rockets.

Luka Doncic
Luka Doncic

The Slovenian guard, who injured his hamstring late in a blowout loss to the Oklahoma City Thunder on April 2, remains listed as out indefinitely. Coach JJ Redick confirmed this week that Doncic has advanced beyond standstill drills to light on-court activity, marking a significant step in his recovery from the partial muscle tear.

Medical experts describe a Grade 2 hamstring strain as involving noticeable fiber disruption without a complete rupture, typically requiring four to six weeks for full recovery. Doncic’s aggressive approach included traveling to Madrid shortly after the injury for specialized regenerative treatments, including platelet-rich plasma and stem cell injections under the care of physicians linked to his former club, Real Madrid.

Those interventions aimed to accelerate healing, potentially compressing the standard timeline. As of late April, sports medicine specialists like Dr. Jesse Morse estimated a possible return window of 10 to 14 days from the point when meaningful on-court movement begins, placing a potential comeback in mid-to-late May if progress holds.

Redick provided the most recent public update, noting Doncic “was able to move today a little bit on the court. Most of the stuff has been standstill. He’s progressing.” The coach stressed there is still no timeline, echoing reports from ESPN’s Shams Charania that the recovery path remains slow. Doncic has not yet progressed to one-on-one work or full-speed scrimmaging.

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The protocol follows a standard phased return-to-play model for hamstring injuries in elite athletes. Phase one emphasized protection and controlled mobility, including rest, compression, physical therapy and the European injections. Phase two has introduced light jogging, directional changes and basketball-specific movements at sub-maximal effort. Subsequent phases will incorporate sprinting, jumping, cutting and contact before clearance for game play.

Lakers medical staff monitor metrics such as muscle strength symmetry, flexibility and pain-free range of motion. Hamstring strains carry high re-injury risk — often 20-30% in professional sports — if athletes return prematurely, particularly in a high-usage player like Doncic who relies on explosive first steps and deceleration.

Doncic rejoined the team in Los Angeles ahead of the playoffs but has not traveled with the squad for road games against Houston. His presence on the bench has provided intangible leadership, yet the Lakers have leaned heavily on LeBron James, Austin Reaves (also recovering from an oblique strain) and supporting cast members.

Expectations point to Doncic missing the entire first-round series. Even if the Lakers advance, multiple reports indicate he is unlikely to be available for the start of the Western Conference semifinals, with a more realistic target around Games 3 or 4 of a potential second-round matchup. Six weeks from the April 2 injury date would land in mid-May.

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The injury occurred at a critical juncture. Doncic had been playing at an MVP-caliber level, powering the Lakers to a strong late-season surge. His absence has reshaped playoff dynamics in the Western Conference, forcing Los Angeles to adapt without its primary playmaker and leading scorer.

Team officials and medical experts weigh the long-term risks against short-term playoff ambitions. Rushing a return could jeopardize Doncic’s availability for future seasons or lead to chronic issues. Hamstring injuries have historically sidelined stars for extended periods, with re-aggravation often extending timelines significantly.

Doncic’s work ethic and competitive drive have been highlighted throughout the process. Sources describe him attacking rehab aggressively while remaining in good spirits. His agent, Bill Duffy, confirmed the European trip was a collaborative decision involving Lakers doctors and independent specialists.

Broader implications extend to the Lakers’ roster construction and future planning. The team traded for Doncic earlier in the season in a blockbuster move, banking on his superstar talent to elevate the franchise. His prolonged absence tests depth and underscores the fragility of championship contention when key pieces go down.

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Fans and analysts track every update closely. Social media buzzes with speculation about potential return dates, fueled by optimistic interpretations of practice footage and vague coaching comments. Yet insiders maintain patience is essential.

For a 27-year-old in his prime, full recovery remains highly probable with proper management. Modern sports medicine, including the biologics Doncic received, has improved outcomes for soft-tissue injuries. Strength and conditioning programs tailored to basketball movements will play a pivotal role in the final rehab stages.

As the Lakers navigate the playoffs without their All-NBA talent, focus shifts to collective resilience. James has shouldered a heavier load, while younger players step into expanded roles. A deep run without Doncic would be impressive; his eventual return could provide a massive boost for later rounds.

Looking ahead, the Lakers’ medical staff will continue daily assessments. Progression to non-contact 3-on-3 or 5-on-5 work will signal the next major milestone. Only after clearing sport-specific testing, including sprint times and change-of-direction drills, will Doncic receive medical clearance.

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The organization emphasizes a conservative approach. “We’re going to do what’s best for Luka long-term,” Redick has reiterated in various forms. That philosophy prioritizes sustainable health over rushed availability.

Doncic’s injury serves as a reminder of basketball’s physical demands. Even superstars face setbacks, and recovery protocols balance science, patience and individual response. For now, the focus remains on incremental gains — more fluid movement, greater confidence in the hamstring and steady buildup toward basketball activities.

Whether Doncic returns this postseason or begins the 2026-27 season fully healthy, his current protocol reflects best practices in elite athlete care. The basketball world watches, hoping the star’s dedication yields a strong comeback when the moment is right.

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Senseonics prices $80 million public offering at $5 per share

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