Palantir Technologies is turning heads in the defense world, achieving a market valuation nearly equal to the combined worth of the U.S.’s three largest defense contractors, despite projected 2025 sales of just $4.2 billion.
Retail investors poured $1.2 billion into the stock in July alone, drawn by 48% revenue growth and CEO Alex Karp’s push to expand commercial sales.
Palantir now supports nine U.S. Army programs, a $10 billion Army consolidation deal, and NATO operations.
While overseas growth lags due to data-sharing limits, investors are betting its AI-driven platforms will cement long-term dominance—even at extreme multiples.
Palantir Technologies is turning heads in the defense sector. With a valuation nearly equal to the combined worth of America’s three largest defense contractors, the AI-powered software company is rewriting the rules—despite projecting 2025 sales of just $4.2 billion.
Here’s how Palantir is defying norms and capturing investor attention.
Valuation That Stuns Palantir trades at nearly 90 times its projected 2025 sales, compared with just 2 times for giants like Lockheed and Northrop.
Retail Frenzy July saw retail investors pour $1.2 billion into the stock, signaling widespread excitement for Palantir’s growth potential.
Explosive Revenue Growth The company posted 48% year-on-year revenue growth last quarter, outpacing traditional defense contractors.
Commercial Ambitions CEO Alex Karp predicts 10% growth in non-government revenue by 2030, showing the company’s push into enterprise markets.
Major Army Partnerships Palantir now supports nine U.S. Army programs, secured a $10 billion decade-long consolidation deal, and expanded a $1.3 billion Maven Smart Systems contract.
NATO Integration Its software is now used by NATO for at least five years, proving its global defense relevance.
Tech That Powers Decisions Foundry and Gotham platforms integrate AI, data, and intelligence to provide real-time battlefield and enterprise insights.
Global Expansion Challenges Non-U.S. revenue dropped from 36% in Q2 2024 to 27% this year due to data-sharing concerns and U.S. policy restrictions.
Palantir’s meteoric rise shows how investors value AI-driven defense solutions, even at steep multiples.
While international expansion faces hurdles, its integration into critical defense and enterprise systems positions the company for long-term influence—if it can navigate geopolitical and data privacy risks.
Unicapital Securities, Inc. has launched its 2025 Mid-Year Briefing series, “Turning the Corner,” outlining how the Philippine economy is moving past early headwinds and toward a more stable second half of the year. With inflation cooling, markets strengthening, and trade dynamics shifting, the outlook suggests both optimism and caution.
Here are the seven most important takeaways:
1. Inflation hits its lowest first-half average in over a decade Inflation settled at just 1.8% in the first six months of 2025, far below expectations and the lowest first-half average in more than ten years. Lower rice and oil prices drove the decline, giving households relief from high costs and fueling consumer optimism.
2. Interest rates may see further cuts With inflation under control, the Bangko Sentral ng Pilipinas (BSP) has more room to adjust policy rates. After a 50-basis-point cut earlier this year, economists are eyeing another 25 to 50 basis points of easing. This would create a more supportive environment for both businesses seeking capital and consumers taking loans.
3. Philippine GDP growth remains regionally competitive While GDP growth expectations were revised to 5.5% for 2025, this figure is still ahead of Southeast Asia’s projected average of 4.2%. The economy continues to draw strength from household consumption, major infrastructure programs, recovering tourism, and stable remittance inflows from overseas Filipinos.
4. Stock market rebounds with stronger momentum The Philippine Stock Exchange Index (PSEi) bounced back to 6,350 in July after falling 3% earlier in the year. Unicapital forecasts the index could rise to 7,100 before year-end, boosted by steady corporate earnings and attractive share valuations.
5. Corporate earnings outlook signals opportunity Listed firms are expected to post an 8% earnings growth in 2025, with sectors like Consumer, REITs, and Utilities showing resilience. Unicapital emphasized that the PSEi rebound reflects more than a technical correction — it indicates underlying strength that investors can capitalize on.
6. Trade challenges emerge under new U.S. tariffs While semiconductors — the country’s largest export — were spared from tariff hikes, most other Philippine exports now face a 19% U.S. tax under President Donald Trump’s new policy. This rate is higher than those imposed on neighbors such as Vietnam and Thailand, potentially making Philippine goods less competitive in the U.S. market.
7. Export diversification offers a long-term path Despite tariff challenges, Unicapital sees opportunities for the Philippines to climb the global value chain by expanding higher-value exports. With semiconductors still in a strong position, the country has the potential to diversify and secure a more strategic role in international trade.
Unicapital’s 2025 mid-year outlook highlights a Philippines at a turning point: inflation is easing, monetary policy is supportive, and markets are regaining momentum. Yet, global trade headwinds remain a challenge. The key will be sustaining stability and translating it into long-term growth that benefits investors, businesses, and Filipino households alike.
Genesis AI has launched from stealth with a jaw-dropping $105 million seed round co-led by Eclipse Ventures and Khosla Ventures to create a foundational AI model for robots.
The startup was founded in December 2024 by Carnegie Mellon robotics Ph.D. Zhou Xian and ex-Mistral researcher Théophile Gervet.
Genesis aims to automate repetitive tasks such as lab work and housekeeping through a general-purpose robotics model.
Unlike large language models trained on text, robotics models need physical-world data—something Genesis hopes to shortcut using synthetic data.
Its proprietary physics engine, born out of an academic collaboration involving 18 universities, generates this synthetic data to simulate real-world interactions.
The company boasts a team of over 20 researchers with backgrounds in robotics, machine learning, and computer graphics.
Genesis claims its engine develops models faster than competitors relying on NVIDIA’s more common software.
Other startups in the race include Physical Intelligence, which snagged $400 million, and Skild AI, which hit a $4 billion valuation.
Genesis operates out of Silicon Valley and Paris, with plans to release its model to the robotics community by year-end.
Because what better way to train robots to mop floors than with imaginary messes in a virtual world?
In the race to appear cutting-edge, tech companies have overloaded their messaging with AI-related buzzwords. The irony? Many of the terms used to sound forward-thinking now come across as hollow, overhyped, or just plain meaningless. Yet despite the fatigue, the same companies—startups and global giants alike—keep using these terms to position themselves as leaders in artificial intelligence.
Here’s a breakdown of the most overused AI jargon terms today—and the companies still using them in their product launches, investor decks, and media campaigns.
1. “AI-Powered”
What began as a straightforward phrase to describe technology enhanced by machine learning has now become a default label for virtually every digital product. The term is slapped onto everything from CRM dashboards to toothbrushes.
Many of the products using this term rely on basic pattern recognition or rule-based automation, not actual intelligence. Still, companies across industries—from consumer tech to enterprise software—continue branding their offerings as “AI-powered,” even if the AI component is marginal or unnecessary.
2. “AI Agents”
“Agents” suggests independent software entities capable of reasoning, planning, and acting on behalf of users. While this concept holds real potential, it’s also been inflated beyond recognition.
Startups and tech giants now refer to basic chatbots, automated workflows, and scripted assistants as “AI agents.” Companies in customer service, HR, and developer tooling regularly use this term to imply autonomy where there is none.
3. “Generative AI”
This phrase originally referred to AI models capable of producing new content—text, images, code, or media. Today, the term is so broadly applied that it often fails to distinguish between truly novel systems and glorified autocomplete engines.
Even tools that simply remix templates or modify existing content are now sold as “generative AI.” From SaaS platforms to image editing tools, generative AI is used as a universal descriptor, regardless of how original the output really is.
4. “Explainable AI (XAI)”
This term refers to systems designed to offer transparency about how they arrive at decisions. It’s an important area, especially in regulated sectors, but it has lost clarity through overuse.
Companies now invoke “explainable AI” in compliance documents, pitch decks, and feature overviews—often with little evidence that the models they use are actually interpretable. Even black-box systems are being described as “explainable” with little more than a confidence score attached.
5. “Multimodal AI”
This term describes models that can process and integrate multiple data types—such as text, images, and audio—simultaneously. It became a buzzword after large tech firms began releasing flagship products supporting text-to-image and video generation.
Now, any application that handles two data types is loosely described as “multimodal.” Whether it’s justified or not, this label shows up frequently in product launches, even when true integration across modes is missing.
6. “Shadow AI”
Shadow AI is the AI version of shadow IT: unauthorized use of tools by employees outside approved channels. It describes real risk around governance, compliance, and data control—but it has also become a security team’s favorite buzzword.
Companies now include “Shadow AI” in risk presentations and cybersecurity audits, even if there’s little evidence that unmanaged AI use is actually causing harm. The term’s rapid rise reflects more of a branding trend than a new operational reality.
7. “AI Hallucination”
This term describes instances when AI models produce factually incorrect or completely fabricated content with high confidence. Initially coined in research contexts, it’s now part of everyday product lingo.
Firms offering content generation, legal drafting, and AI-assisted analytics often include warnings about hallucination. Some even use it as a feature differentiator—promising “less hallucination” than competitors. It has become more of a disclaimer than a true metric.
8. “AI-Washing”
AI-washing refers to the practice of exaggerating or fabricating AI capabilities for the sake of appearing more advanced. Ironically, the term itself is now used so often that it sometimes masks legitimate innovation.
Even companies with credible AI offerings use the threat of “AI-washing” as a tactic to differentiate themselves—by accusing others while glossing over their own inflated claims. It’s become a way to sound self-aware without actually changing the playbook.
Why the Jargon Persists
The repetitive use of AI jargon isn’t accidental. In a competitive funding and attention economy, these terms function as shorthand for innovation—even when they’re vague. They drive investor excitement, boost marketing traction, and signal technical credibility. Many organizations would rather risk overstatement than be seen as behind the curve.
In sectors like healthcare, enterprise software, and fintech, these terms show up in product demos, pitch meetings, and compliance audits. Teams are often rewarded for including them, regardless of the depth behind the claims.
What Happens Next
There’s growing pressure for companies to be more transparent and specific about the AI they use. As AI matures from hype to utility, vague jargon will become a liability. Users, regulators, and even investors are demanding substance over style.
In the near future, companies will need to explain—not just declare—how their models work, what data they rely on, and what outcomes they drive. Those that continue to hide behind buzzwords risk losing credibility, users, and market relevance.
