contentSlug

type

ContentSlug_ContentQuery

slug

ContentPageContentQuery

content

trendingContent

query ContentSlug_ContentQuery(
  $contentSlug: String!
  $type: [ContentType!]
) {
  ...ContentPageContentQuery_13QmVO
}

fragment CompanyDetails_Content on Content {
  __isContent: __typename
  companies {
    name
    description
    pageUrl
    id
  }
}

fragment ContentBasicInfo on Content {
  __isContent: __typename
  id
  type
  title
  lang
  createdAt
  ... on Video {
    liveTime
  }
  ... on Webcast {
    liveTime
  }
  companies {
    viewerIsFollowing
    id
    name
    pageUrl
    forum {
      threadUrl
    }
    defaultInstrument {
      symbol
      id
    }
  }
  tags {
    viewerIsFollowing
    id
    name
    pageUrl
  }
  ...PublishingDetails
  ...Reactions
}

fragment ContentMetadata on Content {
  __isContent: __typename
  type
  title
  lang
  pageUrl
  alternatePageUrls: translations {
    __typename
    pageUrl
    lang
  }
  imgUrl
  description
  createdAt
  changedAt
  isPremium
  authors {
    __typename
    name
    pageUrl
    id
  }
  companies {
    name
    pageUrl
    id
  }
  tags {
    name
    id
  }
  ... on Video {
    liveTime
    videoUrl
    videoEmbedUrl(location: "inderes")
  }
  ... on Webcast {
    liveTime
    videoUrl
    videoEmbedUrl(location: "inderes")
  }
}

fragment ContentPageContentQuery_13QmVO on Query {
  content(slug: $contentSlug, type: $type) {
    __typename
    id
    type
    title
    isPremium
    lang
    htmlBody
    summary
    companies {
      forum {
        threadUrl
        latestPosts {
          ...ForumPosts
        }
      }
      name
      id
      viewerIsFollowing
      pageUrl
      defaultInstrument {
        isin
        id
        symbol
        marketOpen
        marketClose
        marketplace {
          region
        }
        returns {
          total
        }
      }
    }
    tags {
      viewerIsFollowing
      id
      name
      pageUrl
    }
    createdAt
    ... on Video {
      liveTime
    }
    ... on Webcast {
      liveTime
    }
    slug
    ... on CompanyReport {
      reportUrl
      researchContentUrl
      researchContent {
        title
        lang
        pdfUrl
        relatedContent {
          __typename
          ...PublishingDetails
          ...Reactions
          id
        }
        content {
          title
          lang
          htmlBody
          publishedAt
          createdAt
          changedAt
          isOutdated
          isPremium
        }
      }
    }
    ... on ThirdPartyCompanyReport {
      reportUrl
    }
    ... on StockExchangeRelease {
      reportUrl
    }
    ...ContentBasicInfo
    ...ContentMetadata
    ...PriceAndRecommendation_Content
    ...CompanyDetails_Content
    ...ReportLink_Content
    ...KeyFigures_Content
  }
  ...TrendingContent_1jQ2Cn
}

fragment ForumPosts on ForumPost {
  text
  likesCount
  url
  author
  postedAt
  authorImgUrl
}

fragment KeyFigures_Content on Content {
  __isContent: __typename
  companies {
    defaultInstrument {
      estimateData {
        modelType
        reportingCurrency
      }
      snapshot {
        transaction {
          date
          actualized {
            year
            quarter
            revenue
            revenueGrowth
            ebitAdjusted
            ebitReported
            ebitda
            enterpriseValue
            epsAdjusted
            dividend
            dividendYield
            sharePrice
            nav
            navPerShare
            opexOfNav
            quarterlySharePrice
          }
          estimates {
            year
            quarter
            revenue
            revenueGrowth
            ebitAdjusted
            ebitReported
            ebitda
            enterpriseValue
            epsAdjusted
            dividend
            dividendYield
            sharePrice
            nav
            navPerShare
            opexOfNav
            quarterlySharePrice
          }
        }
      }
      id
    }
    id
  }
}

fragment PriceAndRecommendation_Content on Content {
  __isContent: __typename
  createdAt
  companies {
    name
    pageUrl
    defaultInstrument {
      priceData {
        tradeCurrency
      }
      snapshot {
        priceData {
          date
          lastPrice
          tradeCurrency
        }
        transaction {
          date
          recommendation
          targetPrice
        }
        prevTransaction {
          recommendation
        }
      }
      id
    }
    id
  }
}

fragment PublishingDetails on Content {
  __isContent: __typename
  publishers {
    name
    imgUrl
  }
  authors {
    __typename
    id
    name
    slug
    imgUrl
    jobTitle
  }
}

fragment Reactions on Content {
  __isContent: __typename
  id
  reactionGroups {
    reactionType
    reactionsCount
    viewerReacted
  }
}

fragment ReportLink_Content on Content {
  __isContent: __typename
  type
  isPremium
  ... on CompanyReport {
    reportUrl
  }
  ... on ThirdPartyCompanyReport {
    reportUrl
  }
  ... on StockExchangeRelease {
    reportUrl
  }
}

fragment TrendingContent_1jQ2Cn on Query {
  trendingContent(type: ARTICLE) {
    __typename
    id
    title
    lang
    pageUrl
    createdAt
    type
    imgUrl
    companies {
      squareLogoUrl
      name
      id
    }
    authors {
      __typename
      imgUrl
      id
    }
    publishers {
      imgUrl
    }
  }
}


vaisala-earnings-and-multiples-should-gain-evli

__typename

title

isPremium

lang

htmlBody

summary

companies

createdAt

publishers

authors

reactionGroups

pageUrl

translations

imgUrl

description

changedAt

Vaisala is a technology company. The company is a supplier of measurement solutions in the field of meteorology. The company's specialist expertise is found in the development of sensors, radars, and associated system solutions. Customers are found in various industries, with the greatest concentration in the aviation and energy sectors. The company operates on a global level, primarily in Europe, North America and Asia.

From my perspective, a better alternative would have been for Vaisala to collaborate with an AI consultant to help integrate the desired AI into the system. Now, all that expertise sits with a couple of guys in California.

{“content”:“Sigh, what capital allocation. For a long time, I thought Vaisala was a conservative capital allocator, but when they invest 70 million into AI forecasting for the second time, it starts to make my head spin as a shareholder.\n\nThose AI-based weather forecasts have started appearing like mushrooms in the rain alongside the industry transformation. Forum readers can verify this for themselves by typing "AI weather forecast" into Google and counting how many startups pop up. Each one promises to be the industry’s best, most accurate, and fastest at producing forecasts.\n\nThe reason for the abundance of firms is, in my opinion, simple: making AI-based weather forecasts is relatively easy.\n\nHistory\n\nAs Vaisala also mentioned in their release, historically, weather forecasting has been handled by national meteorological institutes, international scientific institutes, or individual companies. This has been because producing forecasts required significant:\n\n1. Computational infrastructure\n2. A group of physicists specialized in atmospheric science\n\nOnly a few at the national level have been capable of such investments.\n\nThe forecasting load is due to the complexity of atmospheric physics. Complex physical processes related to cloud formation, the freezing of ice droplets, evaporation of road moisture, or the heating of apartment building roofs have only been calculable with supercomputers.\n\n\nSource: ECMWF\n\nA list of different variables provides further perspective.\n\nWhen an average person uses weather apps like Foreca, the Finnish Meteorological Institute, or Yr, they are exposed to a small list of important variables: temperature, wind, cloudiness, or UV index. An average consumer manages well with these in everyday life.\n\nHowever, others besides the average person on the street need weather forecasts. Partly for this reason, numerical weather forecasts predict close to 10,000 variables. No wonder it’s a heavy task.\n\nThe Basis of AI-Based Weather Forecasting\n\nIf you break down machine learning theory into "beer and sausage" (plain language), it is a learning process where a computer program learns to predict the next step based on training data.\n\nCancer screening is, in my opinion, a good example of this.\n\n\nImage: Wikipedia\n\nIn machine learning-based screening, a computer sees thousands of images of cell clusters. Some images contain cancer cells and some do not. Through the learning process, the computer is rewarded for finding cancer cells and potentially penalized for labeling healthy cells as cancer cells.\n\nThe result of the learning process is a machine learning model to which a doctor can provide a real-time image of a cell sample and ask the model: Are there cancer cells in this sample? Based on the training data, the model then makes its own assessment \n\nA similar analogy works for machine learning-based weather forecasting. These models are given decades of weather data, and based on that, they then predict the weather for the coming days. Although this process is also computationally heavy in practice using AI chips, it is not quite as heavy as traditional calculation. Thus, time and money can be saved if the quality is good.\n\nBased on scientific studies, these first-generation machine learning models have achieved quite good results \n\nSo what’s the problem here?\n\nIn practice, these models need both high-quality training data and high-quality, real-time input data for inference. The options for this data are essentially:\n\n* Atmospheric reanalysis\n* Weather observations via radar, satellites, ground sensors, etc. (Atmo mentions utilizing this, which is no surprise)\n\nA "positive" problem in atmospheric sciences is that no one really owns secret data. The World Meteorological Organization (WMO) has over the years successfully pushed for a global situation where observations and reanalyses are distributed quite openly to everyone.\n\nThis has ultimately led to the situation mentioned at the beginning, where competitors appear like mushrooms in the rain. A weather forecast is easy to make because the weather data for it is easy to gather (if you know what you’re doing). If you want to start a startup to forecast weather:\n\n1. Hire a meteorologist\n2. Hire a coder\n3. Set up a server\n4. Use open observations and reanalyses as the basis for your own model\n5. Hype that you are the fastest, most accurate, and best\n\nAtmo is based on this same recipe according to the open information on their website.\n\nToo good to be true?\n\nAs I already mentioned, AI-based models have, even according to scientific publications, achieved more accurate results than traditional forecasts in, for example, average temperature and pressure. These are the results being hyped on LinkedIn.\n\nHowever, if you look at the scientific literature, I believe the models still have the following shortcomings to prove:\n\n* Extremes\n\nFew customers are interested in normal weather. Everyone is interested in extreme weather phenomena. If you can’t handle the latter, your quality stamp suffers significantly.\n\n* How to predict weather phenomena for which there are few observations?\n\nThere are a massive amount of observations for temperature. There are fewer for precipitation. There are virtually no observations for icing risk at different flight altitudes for aircraft.\n\nSo far, according to studies, the quality of AI-based model forecasts has been proportional to the number of observations. It is easy for a machine learning model to "learn" to predict temperature because there are so many observations of it. So far, results for precipitation forecasting have been more lackluster.\n\n*"Despite these advancements, several challenges remain. Data availability and quality continue to be major obstacles, as gaps and inconsistencies of observational data can compromise the performance of the forecasting models."*\n\nI assume most B2B customers who buy weather forecasts are quality-critical. If the practical quality doesn’t live up to the hype due to the aforementioned issues, the provider might be replaced quickly as competition increases.\n\nMy Assessment\n\nIn practice, Vaisala has bought a company whose product technology is comparable to the Large Language Models (LLM) offered to consumers. My assessment is that competition will saturate, making differentiation very difficult. It is possible, but certainly not a given.\n\nIn addition to this, Vaisala already had WeatherDesk, so I believe the purchase speaks to challenges with their own technology. Why buy parallel operators? 70 million is quite a lot for just customer relationships.\n\nAt the same time, Vaisala has to compete against national players who have historically strong quality stamps for traditional weather forecasting, but who are also involved in producing AI-based weather forecasts. Examples include Europe, Japan, and the USA.\n\nVaisala bought a loss-making startup for 70 million euros in a saturating industry. I’d bet a beer that Vaisala won’t get a positive return on capital from this purchase \n\nFinal Word: The Industry Competition Dynamics are Changing\n\nHistorically, Vaisala has been satisfied in its niche as a device manufacturer. National operators (meteorological institutes, defense forces, etc.) have then bought their necessary equipment from Vaisala, but have also focused on their own niches within the field of meteorology.”,“target_locale”:“en”}Now that Vaisala has decided to push for vertical integration in forecasting, it will be interesting to watch the development of the industry dynamics from the sidelines. Vaisala certainly has the brand and the muscle to succeed in forecast production for certain customer segments, but it remains to be seen how defense forces and meteorological institutes will feel about purchasing equipment from a direct competitor in the future. After all, they can buy high-quality sensors from elsewhere as well

Joni has also had time to provide his comments on this recent acquisition by Vaisala.                    Inderes – 17 Jun 26              Vaisala ostaa kovaa kasvavan tekoälysääyhtiö Atmon - Inderes    Pidämme yrityskauppaa strategisesti erittäin loogisena askeleena, mikä vahvistaa merkittävästi Xweather-liiketoiminnan kilpailuetua sääennustamisessa.

XWeather was being discussed, so it will likely expand more towards governments and defense organizations if the service has previously operated more with corporations. Vaisala seems to be continuing its vertical integration, offering everything from sensors to the actual forecasts. In my opinion, this is quite good; aren’t they moving up the value chain?

In 2024, Vaisala acquired Maxar Intelligence’s WeatherDesk business. At the time, the acquisition was also justified as a way to increase AI-based weather forecasting capabilities. However, back then, the customers were described as entities focusing on “commodity and energy trading, as well as forecasting both energy demand and supply.” Now, the justification is: “The acquisition strengthens Vaisala’s position as a trusted partner for meteorological institutes, defense organizations, and weather-dependent industries.” So, a similar product for different customers? It sounds sensible, but what about the price? Do you consider it expensive, affordable, or somewhere in between? WeatherDesk cost 70 million, but its revenue in the year prior to the acquisition was 13 million. Now, at least the same price is being paid for a business that is, at best, about half of that. Of course, Atmo’s subscription-based revenue growth is threefold.

Tärkeimmät talousuutiset | Kauppalehti – 17 Jun 26              Sisäpiiritieto: Vaisala ostaa tekoälysääyhtiö Atmo, Inc:n johtaakseen...    Tärkeimmät ajankohtaiset talousuutiset, pörssikurssit ja kattavat sijoittajapalvelut löydät Kauppalehdestä.                             Vaisala has been out shopping for AI. Purchase price of 70 million plus a potential earn-out (lisäkauppahinta) of 60 million. Well, perhaps there are strong synergies to be found. The firm’s revenue is 2 million (already 6 million contract-based for 2026), making the purchase price 35x revenue or 12x this year’s contract-based revenue. We’ll see how much value-add Vaisala can create with this. They are likely looking to acquire experts, but it remains to be seen if they lined the boys’ pockets with enough money that they’re no longer interested in working for Vaisala-level salaries. Of course, the earn-out was partly tied to how well they can retain the talent.

Almanakka has also written about Vaisala after Q1.   Valuation: Last time I wrote: “Sigh, if only Vaisala were cheap for once.” Can’t do much else here than add another ‘i’ and say Siiigh. I even sold my small tracking position during the first quarter because I needed the money elsewhere. In my opinion, this is not at buying levels, even though it would be nice to own Industrial Measurements. This would be a hold; buys then below 15x.                    Sijoitustieto.fi              Vaisala Q1/26: Sääennusteet ovat pitäneet hyvin    Vaisalan Q1 tulos oli pääosin odotusteni mukainen: Teolliset mittaukset jatkoivat vahvaa kehitystä, kun taas Sää, energia ja ympäristö -segmentti (WEE) taapersi. Yhtiön liikevaihto jäi raportoidusti edellisvuoden tasolle (+1 % Q1 2025:een...

Vaisala

Vaisala reports Q1 results on Apr 24. In our view earnings growth rate has good chances of continuing at above 10%. 

Vaisala: Earnings and multiples should gain - Evli

Vaisala: Earnings and multiples should gain - Evli